mappings iv classification: Topics by Science.gov

Sample records for mappings iv classification

Investigating the Capability of IRS-P6-LISS IV Satellite Image for Pistachio Forests Density Mapping (case Study: Northeast of Iran)

NASA Astrophysics Data System (ADS)

Hoseini, F.; Darvishsefat, A. A.; Zargham, N.

2012-07-01

In order to investigate the capability of satellite images for Pistachio forests density mapping, IRS-P6-LISS IV data were analyzed in an area of 500 ha in Iran. After geometric correction, suitable training areas were determined based on fieldwork. Suitable spectral transformations like NDVI, PVI and PCA were performed. A ground truth map included of 34 plots (each plot 1 ha) were prepared. Hard and soft supervised classifications were performed with 5 density classes (0-5%, 5-10%, 10-15%, 15-20% and > 20%). Because of low separability of classes, some classes were merged and classifications were repeated with 3 classes. Finally, the highest overall accuracy and kappa coefficient of 70% and 0.44, respectively, were obtained with three classes (0-5%, 5-20%, and > 20%) by fuzzy classifier. Considering the low kappa value obtained, it could be concluded that the result of the classification was not desirable. Therefore, this approach is not appropriate for operational mapping of these valuable Pistachio forests.
Design and update of a classification system: the UCSD map of science.

PubMed

Börner, Katy; Klavans, Richard; Patek, Michael; Zoss, Angela M; Biberstine, Joseph R; Light, Robert P; Larivière, Vincent; Boyack, Kevin W

2012-01-01

Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier's Scopus (about 15,000 source titles, 2001-2005) and Thomson Reuters' Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001-2004)-about 16,000 unique source titles. The updated map and classification adds six years (2005-2010) of WoS data and three years (2006-2008) from Scopus to the existing category structure-increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others.
Design and Update of a Classification System: The UCSD Map of Science

PubMed Central

Börner, Katy; Klavans, Richard; Patek, Michael; Zoss, Angela M.; Biberstine, Joseph R.; Light, Robert P.; Larivière, Vincent; Boyack, Kevin W.

2012-01-01

Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier’s Scopus (about 15,000 source titles, 2001–2005) and Thomson Reuters’ Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001–2004)–about 16,000 unique source titles. The updated map and classification adds six years (2005–2010) of WoS data and three years (2006–2008) from Scopus to the existing category structure–increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others. PMID:22808037
CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

PubMed

Kumar, Shiu; Mamun, Kabir; Sharma, Alok

2017-12-01

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems. Copyright © 2017 Elsevier Ltd. All rights reserved.
Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark.

PubMed

Bou Kheir, Rania; Greve, Mogens H; Bøcher, Peder K; Greve, Mette B; Larsen, René; McCloy, Keith

2010-05-01

Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N=54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N=14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N=39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas. Copyright 2010 Elsevier Ltd. All rights reserved.
Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.
[Addictive behaviours from DSM-IV to DSM-5].

PubMed

van den Brink, W

2014-01-01

The 5th edition of the DSM was published in May, 2013. The new edition incorporates important changes in the classification of addiction. To compare the classification of addictive behaviours presented in DSM-IV with the classification presented in DSM-5 and to comment on the changes introduced into the new version. First of all, the historical developments of the concept of addiction and the classification of addictive behaviours up to DSM-IV are summarised. Then the changes that have been incorporated into DSM-5 are described. The main changes are: (1) DSM-IV substance related disorders and DSM-IV pathological gambling have been combined into one new DSM-5 category, namely 'Substance Related and Addictive Disorders'; (2) DSM-IV abuse and dependence have been combined into one new DSM-5 diagnosis, namely 'Substance Use Disorder'; (2a) the DSM-IV abuse criterion 'recurrent substance-related legal problems' and the DSM-5 criterion 'craving' has been introduced; and (2b) the criteria for (partial) remission have been sharpened. DSM-5 is an improvement on DSM-IV, but for the diagnosis of a psychiatric disorder and the treatment of a psychiatric patient, classification needs to be complemented with staging and profiling.
A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

PubMed Central

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions. PMID:24717283
A practical and automated approach to large area forest disturbance mapping with remote sensing.

PubMed

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.
Mapping project on land use changes in the carboniferous region of Santa Catarina

NASA Technical Reports Server (NTRS)

Valeriano, D. D.; Pereira, M. D. B.

1983-01-01

The utilization of remote sensing data for monitoring land use changes by means of digital image analysis is described. The following data were utilized: LANDSAT data from September 4, 1975, April 24, 1978, and September 8, 1981; LANDSAT paper photography data; area IV color photographs; IBGE topography maps, and auxiliary data about the Brazilian state of Santa Catarina. Three kinds of analyses of digital images were carried out. The project identified and mapped major classes of land use areas including urban areas, coal deposits, agricultural areas, forests, lakes, and flood plains. Five areas directly affected by coal exploration southeast of Santa Catarina are identified and described. In addition, the classification system used for organizing data about land cover in a hierarchical arrangement is presented. The project made use of two remote sensing data sources: data of MSS spectral (Mulitspectral Scanner System)/LANDSAT on a scale of 1:100,000 with approximately 80 m resolution, and infrared color aerial photographs on a scale of 1:45,000 with approximately 5 m resolution. Therefore, the classification system included three levels, two selected to be compatible with aerial photography data and the third to conform to the resolution of MSS/LANDSAT.
[Formula: see text] graded discrete Lax pairs and Yang-Baxter maps.

PubMed

Fordy, Allan P; Xenitidis, Pavlos

2017-05-01

We recently introduced a class of [Formula: see text] graded discrete Lax pairs and studied the associated discrete integrable systems (lattice equations). In this paper, we introduce the corresponding Yang-Baxter maps. Many well-known examples belong to this scheme for N =2, so, for N ≥3, our systems may be regarded as generalizations of these. In particular, for each N we introduce a class of multi-component Yang-Baxter maps, which include H B III (of Papageorgiou et al. 2010 SIGMA 6, 003 (9 p). (doi:10.3842/SIGMA.2010.033)), when N =2, and that associated with the discrete modified Boussinesq equation, for N =3. For N ≥5 we introduce a new family of Yang-Baxter maps, which have no lower dimensional analogue. We also present new multi-component versions of the Yang-Baxter maps F IV and F V (given in the classification of Adler et al. 2004 Commun. Anal. Geom. 12, 967-1007. (doi:10.4310/CAG.2004.v12.n5.a1)).
Geological and Geographical Atlas of Colorado and portions of adjacent territory

USGS Publications Warehouse

Hayden, Ferdinand Vandeveer; Bien, Julius

1881-01-01

Sheets I-IV are triangulations, drainage, land classification, and geologic maps of Colorado west of longitude 102°, on the scale of 12 miles to the inch. Sheets V-XVI are topographic (contour) and geologic maps of Colorado and adjacent States, between meridians 104° 30' and 109° 30' and parallels 36° 45' and 40° 30', on the scale of 4 miles to the inch. Sheets XVII and XVIII contain three geologic sections across the State, west of the longitude 104° 30'. Sheets XIX and XX are panoramic views of the Pikes Peak group, Sawatch Range, central portion of West Elk Mountains, Twin Lakes, southwestern border of the Mesa Verde, San Juan Mountains, and La Plata Mountains.
Geological and Geographical Atlas of Colorado and portions of adjacent territory

USGS Publications Warehouse

Hayden, Ferdinand Vandeveer; Bien, Julius

1877-01-01

Sheets I-IV are triangulations, drainage, land classification, and geologic maps of Colorado west of longitude 102°, on the scale of 12 miles to the inch. Sheets V-XVI are topographic (contour) and geologic maps of Colorado and adjacent States, between meridians 104° 30' and 109° 30' and parallels 36° 45' and 40° 30', on the scale of 4 miles to the inch. Sheets XVII and XVIII contain three geologic sections across the State, west of the longitude 104° 30'. Sheets XIX and XX are panoramic views of the Pikes Peak group, Sawatch Range, central portion of West Elk Mountains, Twin Lakes, southwestern border of the Mesa Verde, San Juan Mountains, and La Plata Mountains.
Snohomish Estuary Wetlands Study Volume III. Classification and Mapping

DTIC Science & Technology

1978-07-01

Marine plant communities form the basis for some of the most complex i food webs known to man. Because of their complexity any destruction of these plant... NCV ) Ř fv;1 4 CV r% . coI * ".444 Ř m- 0mf n4 ~ ’ oC- . -4c C4 C CJL t o% P o I-""C4enc n S qw qt "* *n *nL P o% 0zwk oU a "C-4 2 C" Iv3gMNIV~ I.z -I
Mapping shallow waters habitats using OBIA by applying several approaches of depth invariant index in North Kepulauan Seribu

NASA Astrophysics Data System (ADS)

Siregar, V. P.; Agus, S. B.; Subarno, T.; Prabowo, N. W.

2018-05-01

The availability of satellite imagery with a variety of spatial resolution, both free access and commercial become as an option in utilizing the remote sensing technology. Variability of the water column is one of the factors affecting the interpretation results when mapping marine shallow waters. This study aimed to evaluate the influence of water column correction (depth-invariant index) on the accuracy of shallow water habitat classification results using OBIA. This study was conducted in North of Kepulauan Seribu, precisely in Harapan Island and its surrounding areas. Habitat class schemes were based on field observations, which were then used to build habitat classes on satellite imagery. The water column correction was applied to the three pairs of SPOT-7 multispectral bands, which were subsequently used in object-based classification. Satellite image classification was performed with four different approaches, namely (i) using DII transformed bands with single pair band input (B1B2), (ii) multi pairs bands (B1B2, B1B3, and B2B3), (iii) combination of multi pairs band and initial bands, and (iv) only using initial bands. The accuracy test results of the four inputs show the values of Overall Accuracy and Kappa Statistics, respectively 55.84 and 0.48; 68.53 and 0.64; 78.68 and 0.76; 77.66 and 0.74. It shows that the best results when using DII and initial band combination for shallow water benthic classification in this study site.
A cross-national examination of differences in classification of lifetime alcohol use disorder between DSM-IV and DSM-5: Findings from the World Mental Health Survey

PubMed Central

Slade, Tim; Chiu, Wai-Tat; Glantz, Meyer; Kessler, Ronald C.; Lago, Luise; Sampson, Nancy; Al-Hamzawi, Ali; Florescu, Silvia; Moskalewicz, Jacek; Murphy, Sam; Navarro-Mateu, Fernando; de Galvis, Yolanda Torres; Viana, Maria Carmen; Xavier, Miguel; Degenhardt, Louisa

2016-01-01

Aims To examine the diagnostic overlap in DSM-IV and DSM-5 alcohol use disorder (AUD) and determine the clinical correlates of changing diagnostic status across the two classification systems. Design DSM-IV and DSM-5 definitions of AUD were compared using cross-national community survey data. Setting Nine low-, middle- and high-income countries. Participants/Cases 31,367 respondents to surveys in the World Health Organization World Mental Health Survey Initiative. Measures Composite International Diagnostic Interview, version 3.0 was used to derive DSM-IV and DSM-5 lifetime diagnoses of AUD. Clinical characteristics, also assessed in the surveys, included lifetime DSM-IV anxiety, mood and drug use disorders, lifetime suicidal ideation, plan and attempt, general functional impairment and psychological distress. Findings Compared to DSM-IV AUD (12.3%, SE=0.3%), the DSM-5 definition yielded slightly lower prevalence estimates (10.8%, SE=0.2%). Almost one third (n=802) of all DSM-IV Abuse cases switched to sub-threshold according to DSM-5 and one quarter (n=467) of all DSM-IV diagnostic orphans switched to mild AUD according to DSM-5. New cases of DSM-5 AUD were largely similar to those who maintained their AUD across both classifications. Similarly, new DSM-5 non-cases were similar to those who were sub-threshold across both classifications. The exception to this was with regards to the prevalence of any lifetime drug use disorder. Conclusions In this large cross-national community sample, the prevalence of DSM-5 lifetime AUD was only slightly lower than the prevalence of DSM-IV lifetime AUD. Nonetheless there was considerable diagnostic switching, with a large number of people inconsistently identified across the two DSM classifications. PMID:27426631
Clinical classification of adult patients with chronic intestinal failure due to benign disease: An international multicenter cross-sectional survey.

PubMed

Pironi, Loris; Konrad, Denise; Brandt, Chrisoffer; Joly, Francisca; Wanten, Geert; Agostini, Federica; Chambrier, Cecile; Aimasso, Umberto; Zeraschi, Sarah; Kelly, Darlene; Szczepanek, Kinga; Jukes, Amelia; Di Caro, Simona; Theilla, Miriam; Kunecki, Marek; Daniels, Joanne; Serlie, Mireille; Poullenot, Florian; Wu, Jian; Cooper, Sheldon C; Rasmussen, Henrik H; Compher, Charlene; Seguy, David; Crivelli, Adriana; Pagano, Maria C; Hughes, Sarah-Jane; Guglielmi, Francesco W; Kozjek, Nada Rotovnik; Schneider, Stéphane M; Gillanders, Lyn; Ellegard, Lars; Thibault, Ronan; Matras, Przemysław; Zmarzly, Anna; Matysiak, Konrad; Van Gossum, Andrè; Forbes, Alastair; Wyer, Nicola; Taus, Marina; Virgili, Nuria M; O'Callaghan, Margie; Chapman, Brooke; Osland, Emma; Cuerda, Cristina; Sahin, Peter; Jones, Lynn; Lee, Andre D W; Bertasi, Valentino; Orlandoni, Paolo; Izbéki, Ferenc; Spaggiari, Corrado; Díez, Marta Bueno; Doitchinova-Simeonova, Maryana; Garde, Carmen; Serralde-Zúñiga, Aurora E; Olveira, Gabriel; Krznaric, Zeljko; Czako, Laszlo; Kekstas, Gintautas; Sanz-Paris, Alejandro; Jáuregui, Estrella Petrina; Murillo, Ana Zugasti; Schafer, Eszter; Arends, Jann; Suárez-Llanos, José P; Shaffer, Jon; Lal, Simon

2018-04-01

The aim of the study was to evaluate the applicability of the ESPEN 16-category clinical classification of chronic intestinal failure, based on patients' intravenous supplementation (IVS) requirements for energy and fluids, and to evaluate factors associated with those requirements. ESPEN members were invited to participate through ESPEN Council representatives. Participating centers enrolled adult patients requiring home parenteral nutrition for chronic intestinal failure on March 1st 2015. The following patient data were recorded though a structured database: sex, age, body weight and height, intestinal failure mechanism, underlying disease, IVS volume and energy need. Sixty-five centers from 22 countries enrolled 2919 patients with benign disease. One half of the patients were distributed in 3 categories of the ESPEN clinical classification. 9% of patients required only fluid and electrolyte supplementation. IVS requirement varied considerably according to the pathophysiological mechanism of intestinal failure. Notably, IVS volume requirement represented loss of intestinal function better than IVS energy requirement. A simplified 8 category classification of chronic intestinal failure was devised, based on two types of IVS (either fluid and electrolyte alone or parenteral nutrition admixture containing energy) and four categories of volume. Patients' IVS requirements varied widely, supporting the need for a tool to homogenize patient categorization. This study has devised a novel, simplified eight category IVS classification for chronic intestinal failure that will prove useful in both the clinical and research setting when applied together with the underlying pathophysiological mechanism of the patient's intestinal failure. Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
LMSD: LIPID MAPS structure database

PubMed Central

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Brown, Alex; Dennis, Edward A.; Glass, Christopher K.; Merrill, Alfred H.; Murphy, Robert C.; Raetz, Christian R. H.; Russell, David W.; Subramaniam, Shankar

2007-01-01

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available at PMID:17098933
Map Classification: A Comparison of Schemes with Special Reference to the Continent of Africa. Occasional Papers, Number 154.

ERIC Educational Resources Information Center

Merrett, Christopher E.

This guide to the theory and practice of map classification begins with a discussion of the filing of maps and the function of map classification based on area and theme as illustrated by four maps of Africa. The description of the various classification systems which follows is divided into book schemes with provision for maps (including Dewey…
Local soil classification and crop suitability: Implications for the historical land use and soil management in Monti di Trapani (Sicily)

NASA Astrophysics Data System (ADS)

Garcia-Vila, Margarita; Corselli, Rocco; Bonet, María Teresa; Lopapa, Giuseppe; Pillitteri, Valentina; Fereres, Elias

2017-04-01

In the past, the lack of technologies (e.g. synthetic fertilizers) to overcome biophysical limitations has played a central role in land use planning. Thus, landscape management and agronomic practices are reactions to local knowledge and perceptions on natural resources, particularly soil. In the framework of the European research project MEMOLA (FP7), the role of local farmers knowledge and perceptions on soil for the historical land use through the spatial distribution of crops and the various management practices have been assessed in three different areas of Monti di Trapani region (Sicily). The identification of the soil classification systems of farmers and the criteria on which it is based, linked to the evaluation of the farmers' ability to identify and map the different soil types, was a key step. Nevertheless, beyond the comparison of the ethnopedological classification approach versus standard soil classification systems, the study also aims at understanding local soil management and land use decisions. The applied methodology was based on an interdisciplinary approach, combining soil science methods and participatory appraisal tools, particularly: i) semi-structured interviews; ii) soil sampling and analysis; iii) discussion groups; and iv) a workshop with local edafologists and agronomists. A rich local glossary of terms associated with the soil conditions and an own soil classification system have been identified in the region. Also, a detailed soil map, including process of soil degradation and soil capability, has been generated. This traditional soil knowledge has conditioned the management and the spatial distribution of the crops, and therefore the configuration of the landscape, until the 1990s. Acknowledgements This work has been funded by the European Union project MEMOLA (Grant agreement no: 613265).

Lossless Compression of Classification-Map Data

NASA Technical Reports Server (NTRS)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.
Evaluation criteria for software classification inventories, accuracies, and maps

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1976-01-01

Statistical criteria are presented for modifying the contingency table used to evaluate tabular classification results obtained from remote sensing and ground truth maps. This classification technique contains information on the spatial complexity of the test site, on the relative location of classification errors, on agreement of the classification maps with ground truth maps, and reduces back to the original information normally found in a contingency table.
A Cross-National Examination of Differences in Classification of Lifetime Alcohol Use Disorder Between DSM-IV and DSM-5: Findings from the World Mental Health Survey.

PubMed

Slade, Tim; Chiu, Wai-Tat; Glantz, Meyer; Kessler, Ronald C; Lago, Luise; Sampson, Nancy; Al-Hamzawi, Ali; Florescu, Silvia; Moskalewicz, Jacek; Murphy, Sam; Navarro-Mateu, Fernando; Torres de Galvis, Yolanda; Viana, Maria Carmen; Xavier, Miguel; Degenhardt, Louisa

2016-08-01

The current study sought to examine the diagnostic overlap in DSM-IV and DSM-5 alcohol use disorder (AUD) and determine the clinical correlates of changing diagnostic status across the 2 classification systems. DSM-IV and DSM-5 definitions of AUD were compared using cross-national community survey data in 9 low-, middle-, and high-income countries. Participants were 31,367 respondents to surveys in the World Health Organization's World Mental Health Survey Initiative. The Composite International Diagnostic Interview, version 3.0, was used to derive DSM-IV and DSM-5 lifetime diagnoses of AUD. Clinical characteristics, also assessed in the surveys, included lifetime DSM-IV anxiety; mood and drug use disorders; lifetime suicidal ideation, plan, and attempt; general functional impairment; and psychological distress. Compared with DSM-IV AUD (12.3%, SE = 0.3%), the DSM-5 definition yielded slightly lower prevalence estimates (10.8%, SE = 0.2%). Almost one-third (n = 802) of all DSM-IV abuse cases switched to subthreshold according to DSM-5 and one-quarter (n = 467) of all DSM-IV diagnostic orphans switched to mild AUD according to DSM-5. New cases of DSM-5 AUD were largely similar to those who maintained their AUD across both classifications. Similarly, new DSM-5 noncases were similar to those who were subthreshold across both classifications. The exception to this was with regard to the prevalence of any lifetime drug use disorder. In this large cross-national community sample, the prevalence of DSM-5 lifetime AUD was only slightly lower than the prevalence of DSM-IV lifetime AUD. Nonetheless, there was considerable diagnostic switching, with a large number of people inconsistently identified across the 2 DSM classifications. Copyright © 2016 by the Research Society on Alcoholism.
Reliability, Validity, and Classification Accuracy of the DSM-5 Diagnostic Criteria for Gambling Disorder and Comparison to DSM-IV.

PubMed

Stinchfield, Randy; McCready, John; Turner, Nigel E; Jimenez-Murcia, Susana; Petry, Nancy M; Grant, Jon; Welte, John; Chapman, Heather; Winters, Ken C

2016-09-01

The DSM-5 was published in 2013 and it included two substantive revisions for gambling disorder (GD). These changes are the reduction in the threshold from five to four criteria and elimination of the illegal activities criterion. The purpose of this study was to twofold. First, to assess the reliability, validity and classification accuracy of the DSM-5 diagnostic criteria for GD. Second, to compare the DSM-5-DSM-IV on reliability, validity, and classification accuracy, including an examination of the effect of the elimination of the illegal acts criterion on diagnostic accuracy. To compare DSM-5 and DSM-IV, eight datasets from three different countries (Canada, USA, and Spain; total N = 3247) were used. All datasets were based on similar research methods. Participants were recruited from outpatient gambling treatment services to represent the group with a GD and from the community to represent the group without a GD. All participants were administered a standardized measure of diagnostic criteria. The DSM-5 yielded satisfactory reliability, validity and classification accuracy. In comparing the DSM-5 to the DSM-IV, most comparisons of reliability, validity and classification accuracy showed more similarities than differences. There was evidence of modest improvements in classification accuracy for DSM-5 over DSM-IV, particularly in reduction of false negative errors. This reduction in false negative errors was largely a function of lowering the cut score from five to four and this revision is an improvement over DSM-IV. From a statistical standpoint, eliminating the illegal acts criterion did not make a significant impact on diagnostic accuracy. From a clinical standpoint, illegal acts can still be addressed in the context of the DSM-5 criterion of lying to others.
A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping

PubMed Central

Sun, Min; Wong, David; Kronenfeld, Barry

2016-01-01

Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification. PMID:28286426
The 13th Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-IV Survey Mapping Nearby Galaxies at Apache Point Observatory

NASA Astrophysics Data System (ADS)

Albareti, Franco D.; Allende Prieto, Carlos; Almeida, Andres; Anders, Friedrich; Anderson, Scott; Andrews, Brett H.; Aragón-Salamanca, Alfonso; Argudo-Fernández, Maria; Armengaud, Eric; Aubourg, Eric; Avila-Reese, Vladimir; Badenes, Carles; Bailey, Stephen; Barbuy, Beatriz; Barger, Kat; Barrera-Ballesteros, Jorge; Bartosz, Curtis; Basu, Sarbani; Bates, Dominic; Battaglia, Giuseppina; Baumgarten, Falk; Baur, Julien; Bautista, Julian; Beers, Timothy C.; Belfiore, Francesco; Bershady, Matthew; Bertran de Lis, Sara; Bird, Jonathan C.; Bizyaev, Dmitry; Blanc, Guillermo A.; Blanton, Michael; Blomqvist, Michael; Bolton, Adam S.; Borissova, J.; Bovy, Jo; Nielsen Brandt, William; Brinkmann, Jonathan; Brownstein, Joel R.; Bundy, Kevin; Burtin, Etienne; Busca, Nicolás G.; Orlando Camacho Chavez, Hugo; Cano Díaz, M.; Cappellari, Michele; Carrera, Ricardo; Chen, Yanping; Cherinka, Brian; Cheung, Edmond; Chiappini, Cristina; Chojnowski, Drew; Chuang, Chia-Hsun; Chung, Haeun; Cirolini, Rafael Fernando; Clerc, Nicolas; Cohen, Roger E.; Comerford, Julia M.; Comparat, Johan; Correa do Nascimento, Janaina; Cousinou, Marie-Claude; Covey, Kevin; Crane, Jeffrey D.; Croft, Rupert; Cunha, Katia; Darling, Jeremy; Davidson, James W., Jr.; Dawson, Kyle; Da Costa, Luiz; Da Silva Ilha, Gabriele; Deconto Machado, Alice; Delubac, Timothée; De Lee, Nathan; De la Macorra, Axel; De la Torre, Sylvain; Diamond-Stanic, Aleksandar M.; Donor, John; Downes, Juan Jose; Drory, Niv; Du, Cheng; Du Mas des Bourboux, Hélion; Dwelly, Tom; Ebelke, Garrett; Eigenbrot, Arthur; Eisenstein, Daniel J.; Elsworth, Yvonne P.; Emsellem, Eric; Eracleous, Michael; Escoffier, Stephanie; Evans, Michael L.; Falcón-Barroso, Jesús; Fan, Xiaohui; Favole, Ginevra; Fernandez-Alvar, Emma; Fernandez-Trincado, J. G.; Feuillet, Diane; Fleming, Scott W.; Font-Ribera, Andreu; Freischlad, Gordon; Frinchaboy, Peter; Fu, Hai; Gao, Yang; Garcia, Rafael A.; Garcia-Dias, R.; Garcia-Hernández, D. A.; Garcia Pérez, Ana E.; Gaulme, Patrick; Ge, Junqiang; Geisler, Douglas; Gillespie, Bruce; Gil Marin, Hector; Girardi, Léo; Goddard, Daniel; Gomez Maqueo Chew, Yilen; Gonzalez-Perez, Violeta; Grabowski, Kathleen; Green, Paul; Grier, Catherine J.; Grier, Thomas; Guo, Hong; Guy, Julien; Hagen, Alex; Hall, Matt; Harding, Paul; Harley, R. E.; Hasselquist, Sten; Hawley, Suzanne; Hayes, Christian R.; Hearty, Fred; Hekker, Saskia; Hernandez Toledo, Hector; Ho, Shirley; Hogg, David W.; Holley-Bockelmann, Kelly; Holtzman, Jon A.; Holzer, Parker H.; Hu, Jian; Huber, Daniel; Hutchinson, Timothy Alan; Hwang, Ho Seong; Ibarra-Medel, Héctor J.; Ivans, Inese I.; Ivory, KeShawn; Jaehnig, Kurt; Jensen, Trey W.; Johnson, Jennifer A.; Jones, Amy; Jullo, Eric; Kallinger, T.; Kinemuchi, Karen; Kirkby, David; Klaene, Mark; Kneib, Jean-Paul; Kollmeier, Juna A.; Lacerna, Ivan; Lane, Richard R.; Lang, Dustin; Laurent, Pierre; Law, David R.; Leauthaud, Alexie; Le Goff, Jean-Marc; Li, Chen; Li, Cheng; Li, Niu; Li, Ran; Liang, Fu-Heng; Liang, Yu; Lima, Marcos; Lin, Lihwai; Lin, Lin; Lin, Yen-Ting; Liu, Chao; Long, Dan; Lucatello, Sara; MacDonald, Nicholas; MacLeod, Chelsea L.; Mackereth, J. Ted; Mahadevan, Suvrath; Geimba Maia, Marcio Antonio; Maiolino, Roberto; Majewski, Steven R.; Malanushenko, Olena; Malanushenko, Viktor; Dullius Mallmann, Nícolas; Manchado, Arturo; Maraston, Claudia; Marques-Chaves, Rui; Martinez Valpuesta, Inma; Masters, Karen L.; Mathur, Savita; McGreer, Ian D.; Merloni, Andrea; Merrifield, Michael R.; Meszáros, Szabolcs; Meza, Andres; Miglio, Andrea; Minchev, Ivan; Molaverdikhani, Karan; Montero-Dorta, Antonio D.; Mosser, Benoit; Muna, Demitri; Myers, Adam; Nair, Preethi; Nandra, Kirpal; Ness, Melissa; Newman, Jeffrey A.; Nichol, Robert C.; Nidever, David L.; Nitschelm, Christian; O’Connell, Julia; Oravetz, Audrey; Oravetz, Daniel J.; Pace, Zachary; Padilla, Nelson; Palanque-Delabrouille, Nathalie; Pan, Kaike; Parejko, John; Paris, Isabelle; Park, Changbom; Peacock, John A.; Peirani, Sebastien; Pellejero-Ibanez, Marcos; Penny, Samantha; Percival, Will J.; Percival, Jeffrey W.; Perez-Fournon, Ismael; Petitjean, Patrick; Pieri, Matthew; Pinsonneault, Marc H.; Pisani, Alice; Prada, Francisco; Prakash, Abhishek; Price-Jones, Natalie; Raddick, M. Jordan; Rahman, Mubdi; Raichoor, Anand; Barboza Rembold, Sandro; Reyna, A. M.; Rich, James; Richstein, Hannah; Ridl, Jethro; Riffel, Rogemar A.; Riffel, Rogério; Rix, Hans-Walter; Robin, Annie C.; Rockosi, Constance M.; Rodríguez-Torres, Sergio; Rodrigues, Thaíse S.; Roe, Natalie; Lopes, A. Roman; Román-Zúñiga, Carlos; Ross, Ashley J.; Rossi, Graziano; Ruan, John; Ruggeri, Rossana; Runnoe, Jessie C.; Salazar-Albornoz, Salvador; Salvato, Mara; Sanchez, Sebastian F.; Sanchez, Ariel G.; Sanchez-Gallego, José R.; Santiago, Basílio Xavier; Schiavon, Ricardo; Schimoia, Jaderson S.; Schlafly, Eddie; Schlegel, David J.; Schneider, Donald P.; Schönrich, Ralph; Schultheis, Mathias; Schwope, Axel; Seo, Hee-Jong; Serenelli, Aldo; Sesar, Branimir; Shao, Zhengyi; Shetrone, Matthew; Shull, Michael; Silva Aguirre, Victor; Skrutskie, M. F.; Slosar, Anže; Smith, Michael; Smith, Verne V.; Sobeck, Jennifer; Somers, Garrett; Souto, Diogo; Stark, David V.; Stassun, Keivan G.; Steinmetz, Matthias; Stello, Dennis; Storchi Bergmann, Thaisa; Strauss, Michael A.; Streblyanska, Alina; Stringfellow, Guy S.; Suarez, Genaro; Sun, Jing; Taghizadeh-Popp, Manuchehr; Tang, Baitian; Tao, Charling; Tayar, Jamie; Tembe, Mita; Thomas, Daniel; Tinker, Jeremy; Tojeiro, Rita; Tremonti, Christy; Troup, Nicholas; Trump, Jonathan R.; Unda-Sanzana, Eduardo; Valenzuela, O.; Van den Bosch, Remco; Vargas-Magaña, Mariana; Vazquez, Jose Alberto; Villanova, Sandro; Vivek, M.; Vogt, Nicole; Wake, David; Walterbos, Rene; Wang, Yuting; Wang, Enci; Weaver, Benjamin Alan; Weijmans, Anne-Marie; Weinberg, David H.; Westfall, Kyle B.; Whelan, David G.; Wilcots, Eric; Wild, Vivienne; Williams, Rob A.; Wilson, John; Wood-Vasey, W. M.; Wylezalek, Dominika; Xiao, Ting; Yan, Renbin; Yang, Meng; Ybarra, Jason E.; Yeche, Christophe; Yuan, Fang-Ting; Zakamska, Nadia; Zamora, Olga; Zasowski, Gail; Zhang, Kai; Zhao, Cheng; Zhao, Gong-Bo; Zheng, Zheng; Zheng, Zheng; Zhou, Zhi-Min; Zhu, Guangtun; Zinn, Joel C.; Zou, Hu

2017-12-01

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in 2014 July. It pursues three core programs: the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2), Mapping Nearby Galaxies at APO (MaNGA), and the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). As well as its core program, eBOSS contains two major subprograms: the Time Domain Spectroscopic Survey (TDSS) and the SPectroscopic IDentification of ERosita Sources (SPIDERS). This paper describes the first data release from SDSS-IV, Data Release 13 (DR13). DR13 makes publicly available the first 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA. It includes new observations from eBOSS, completing the Sloan Extended QUasar, Emission-line galaxy, Luminous red galaxy Survey (SEQUELS), which also targeted variability-selected objects and X-ray-selected objects. DR13 includes new reductions of the SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification, and new reductions of the SDSS-III APOGEE-1 data, improving stellar parameters for dwarf stars and cooler stars. DR13 provides more robust and precise photometric calibrations. Value-added target catalogs relevant for eBOSS, TDSS, and SPIDERS and an updated red-clump catalog for APOGEE are also available. This paper describes the location and format of the data and provides references to important technical papers. The SDSS web site, http://www.sdss.org, provides links to the data, tutorials, examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ∼6 yr operations of SDSS-IV.
EVALUATION OF REGISTRATION, COMPRESSION AND CLASSIFICATION ALGORITHMS

NASA Technical Reports Server (NTRS)

Jayroe, R. R.

1994-01-01

Several types of algorithms are generally used to process digital imagery such as Landsat data. The most commonly used algorithms perform the task of registration, compression, and classification. Because there are different techniques available for performing registration, compression, and classification, imagery data users need a rationale for selecting a particular approach to meet their particular needs. This collection of registration, compression, and classification algorithms was developed so that different approaches could be evaluated and the best approach for a particular application determined. Routines are included for six registration algorithms, six compression algorithms, and two classification algorithms. The package also includes routines for evaluating the effects of processing on the image data. This collection of routines should be useful to anyone using or developing image processing software. Registration of image data involves the geometrical alteration of the imagery. Registration routines available in the evaluation package include image magnification, mapping functions, partitioning, map overlay, and data interpolation. The compression of image data involves reducing the volume of data needed for a given image. Compression routines available in the package include adaptive differential pulse code modulation, two-dimensional transforms, clustering, vector reduction, and picture segmentation. Classification of image data involves analyzing the uncompressed or compressed image data to produce inventories and maps of areas of similar spectral properties within a scene. The classification routines available include a sequential linear technique and a maximum likelihood technique. The choice of the appropriate evaluation criteria is quite important in evaluating the image processing functions. The user is therefore given a choice of evaluation criteria with which to investigate the available image processing functions. All of the available evaluation criteria basically compare the observed results with the expected results. For the image reconstruction processes of registration and compression, the expected results are usually the original data or some selected characteristics of the original data. For classification processes the expected result is the ground truth of the scene. Thus, the comparison process consists of determining what changes occur in processing, where the changes occur, how much change occurs, and the amplitude of the change. The package includes evaluation routines for performing such comparisons as average uncertainty, average information transfer, chi-square statistics, multidimensional histograms, and computation of contingency matrices. This collection of routines is written in FORTRAN IV for batch execution and has been implemented on an IBM 360 computer with a central memory requirement of approximately 662K of 8 bit bytes. This collection of image processing and evaluation routines was developed in 1979.
Extension of the classical classification of β-turns

PubMed Central

de Brevern, Alexandre G.

2016-01-01

The functional properties of a protein primarily depend on its three-dimensional (3D) structure. These properties have classically been assigned, visualized and analysed on the basis of protein secondary structures. The β-turn is the third most important secondary structure after helices and β-strands. β-turns have been classified according to the values of the dihedral angles φ and ψ of the central residue. Conventionally, eight different types of β-turns have been defined, whereas those that cannot be defined are classified as type IV β-turns. This classification remains the most widely used. Nonetheless, the miscellaneous type IV β-turns represent 1/3rd of β-turn residues. An unsupervised specific clustering approach was designed to search for recurrent new turns in the type IV category. The classical rules of β-turn type assignment were central to the approach. The four most frequently occurring clusters defined the new β-turn types. Unexpectedly, these types, designated IV1, IV2, IV3 and IV4, represent half of the type IV β-turns and occur more frequently than many of the previously established types. These types show convincing particularities, in terms of both structures and sequences that allow for the classical β-turn classification to be extended for the first time in 25 years. PMID:27627963
Extension of the classical classification of β-turns.

PubMed

de Brevern, Alexandre G

2016-09-15

The functional properties of a protein primarily depend on its three-dimensional (3D) structure. These properties have classically been assigned, visualized and analysed on the basis of protein secondary structures. The β-turn is the third most important secondary structure after helices and β-strands. β-turns have been classified according to the values of the dihedral angles φ and ψ of the central residue. Conventionally, eight different types of β-turns have been defined, whereas those that cannot be defined are classified as type IV β-turns. This classification remains the most widely used. Nonetheless, the miscellaneous type IV β-turns represent 1/3(rd) of β-turn residues. An unsupervised specific clustering approach was designed to search for recurrent new turns in the type IV category. The classical rules of β-turn type assignment were central to the approach. The four most frequently occurring clusters defined the new β-turn types. Unexpectedly, these types, designated IV1, IV2, IV3 and IV4, represent half of the type IV β-turns and occur more frequently than many of the previously established types. These types show convincing particularities, in terms of both structures and sequences that allow for the classical β-turn classification to be extended for the first time in 25 years.
Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.
EXhype: A tool for mineral classification using hyperspectral data

NASA Astrophysics Data System (ADS)

Adep, Ramesh Nityanand; shetty, Amba; Ramesh, H.

2017-02-01

Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named 'EXhype (Expert system for hyperspectral data classification)' to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals.
Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

NASA Astrophysics Data System (ADS)

Li, Long; Solana, Carmen; Canters, Frank; Kervyn, Matthieu

2017-10-01

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of > 20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping.
Single-Frame Terrain Mapping Software for Robotic Vehicles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.

2011-01-01

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.
A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808
Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lai, Wen-li; Wang, Hong-rui; Wang, Cheng

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated withmore » waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.« less
Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

DOE PAGES

Lai, Wen-li; Wang, Hong-rui; Wang, Cheng; ...

2017-05-05

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated withmore » waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.« less
A comparison of delirium diagnosis in elderly medical inpatients using the CAM, DRS-R98, DSM-IV and DSM-5 criteria.

PubMed

Adamis, Dimitrios; Rooney, Siobhan; Meagher, David; Mulligan, Owen; McCarthy, Geraldine

2015-06-01

The recently published DSM-5 criteria for delirium may lead to different case identification and rates of delirium than previous classifications. The aims of this study are to determine how the new DSM-5 criteria compare with DSM-IV in identification of delirium in elderly medical inpatients and to investigate the agreement between different methods, using CAM, DRS-R98, DSM-IV, and DSM-5 criteria. Prospective, observational study of elderly patients aged 70+ admitted under the acute medical teams in a regional general hospital. Each participant was assessed within 3 days of admission using the DSM-5, and DSM-IV criteria plus the DRS-R98, and CAM scales. We assessed 200 patients [mean age 81.1±6.5; 50% female; pre-existing cognitive impairment in 63%]. The prevalence rates of delirium for each diagnostic method were: 13.0% (n = 26) for DSM-5; 19.5% (n = 39) for DSM-IV; 13.5% (n = 27) for DRS-R98 and 17.0%, (n = 34) for CAM. Using tetrachoric correlation coefficients the agreement between DSM-5 and DSM-IV was statistically significant (ρtetr = 0.64, SE = 0.1, p < 0.0001). Similar significant agreement was found between the four methods. DSM-IV is the most inclusive diagnostic method for delirium, while DSM-5 is the most restrictive. In addition, these classification systems identify different cases of delirium. This could have clinical, financial, and research implications. However, both classification systems have significant agreement in the identification of the same concept (delirium). Clarity of diagnosis is required for classification but also further research considering the relevance in predicting outcomes can allow for more detailed evaluation of the DSM-5 criteria.
Examining the Stability of "DSM-IV" and Empirically Derived Eating Disorder Classification: Implications for "DSM-5"

ERIC Educational Resources Information Center

Peterson, Carol B.; Crow, Scott J.; Swanson, Sonja A.; Crosby, Ross D.; Wonderlich, Stephen A.; Mitchell, James E.; Agras, W. Stewart; Halmi, Katherine A.

2011-01-01

Objective: The purpose of this investigation was to derive an empirical classification of eating disorder symptoms in a heterogeneous eating disorder sample using latent class analysis (LCA) and to examine the longitudinal stability of these latent classes (LCs) and the stability of DSM-IV eating disorder (ED) diagnoses. Method: A total of 429…
A Mapping from the Human Factors Analysis and Classification System (DOD-HFACS) to the Domains of Human Systems Integration (HSI)

DTIC Science & Technology

2009-11-01

Equation Chapter 1 Section 1 A MAPPING FROM THE HUMAN FACTORS ANALYSIS AND CLASSIFICATION SYSTEM (DOD...OMB control number. 1. REPORT DATE NOV 2009 2. REPORT TYPE 3. DATES COVERED 4. TITLE AND SUBTITLE A Mapping from the Human Factors Analysis ...7 The Human Factors Analysis and Classification System .................................................. 7 Mapping of DoD
Comparison of LSS-IV and LISS-III+LISS-IV merged data for classification of crops

NASA Astrophysics Data System (ADS)

Hebbar, R.; Sesha Sai, M. V. R.

2014-11-01

Resourcesat-1 satellite with its unique capability of simultaneous acquisition of multispectral images at different spatial resolutions (AWiFS, LISS-III and LISS-IV MX / Mono) has immense potential for crop inventory. The present study was carried for selection of suitable LISS-IV MX band for data fusion and its evaluation for delineation different crops in a multi-cropped area. Image fusion techniques namely intensity hue saturation (IHS), principal component analysis (PCA), brovey, high pass filter (HPF) and wavelet methods were used for merging LISS-III and LISS-IV Mono data. The merged products were evaluated visually and through universal image quality index, ERGAS and classification accuracy. The study revealed that red band of LISS-IV MX data was found to be optimal band for merging with LISS-III data in terms of maintaining both spectral and spatial information and thus, closely matching with multispectral LISS-IVMX data. Among the five data fusion techniques, wavelet method was found to be superior in retaining image quality and higher classification accuracy compared to commonly used methods of IHS, PCA and Brovey. The study indicated that LISS-IV data in mono mode with wider swath of 70 km could be exploited in place of 24km LISS-IVMX data by selection of appropriate fusion techniques by acquiring monochromatic data in the red band.

Classification

NASA Astrophysics Data System (ADS)

Oza, Nikunj

2012-03-01

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example would represent one sunspot’s classification (y_i) and the corresponding set of measurements (x_i). The output of a supervised learning algorithm is a model h that approximates the unknown mapping from the inputs to the outputs. In our example, h would map from the sunspot measurements to the type of sunspot. We may have a test set S—a set of examples not used in training that we use to test how well the model h predicts the outputs on new examples. Just as with the examples in T, the examples in S are assumed to be independent and identically distributed (i.i.d.) draws from the distribution D. We measure the error of h on the test set as the proportion of test cases that h misclassifies: 1/|S| Sigma(x,y union S)[I(h(x)!= y)] where I(v) is the indicator function—it returns 1 if v is true and 0 otherwise. In our sunspot classification example, we would identify additional examples of sunspots that were not used in generating the model, and use these to determine how accurate the model is—the fraction of the test samples that the model classifies correctly. An example of a classification model is the decision tree shown in Figure 23.1. We will discuss the decision tree learning algorithm in more detail later—for now, we assume that, given a training set with examples of sunspots, this decision tree is derived. This can be used to classify previously unseen examples of sunpots. For example, if a new sunspot’s inputs indicate that its "Group Length" is in the range 10-15, then the decision tree would classify the sunspot as being of type “E,” whereas if the "Group Length" is "NULL," the "Magnetic Type" is "bipolar," and the "Penumbra" is "rudimentary," then it would be classified as type "C." In this chapter, we will add to the above description of classification problems. We will discuss decision trees and several other classification models. In particular, we will discuss the learning algorithms that generate these classification models, how to use them to classify new examples, and the strengths and weaknesses of these models. We will end with pointers to further reading on classification methods applied to astronomy data.
Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification

USGS Publications Warehouse

Faber-Langendoen, D.; Aaseng, N.; Hop, K.; Lew-Smith, M.; Drake, J.

2007-01-01

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with 'alliance' and 'association' as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification. ?? IAVS; Opulus Press.
Mapping South San Francisco Bay's seabed diversity for use in wetland restoration planning

USGS Publications Warehouse

Fregoso, Theresa A.; Jaffe, B.; Rathwell, G.; Collins, W.; Rhynas, K.; Tomlin, V.; Sullivan, S.

2006-01-01

Data for an acoustic seabed classification were collected as a part of a California Coastal Conservancy funded bathymetric survey of South Bay in early 2005. A QTC VIEW seabed classification system recorded echoes from a sungle bean 50 kHz echosounder. Approximately 450,000 seabed classification records were generated from an are of of about 30 sq. miles. Ten district acoustic classes were identified through an unsupervised classification system using principle component and cluster analyses. One hundred and sixty-one grab samples and forty-five benthic community composition data samples collected in the study area shortly before and after the seabed classification survey, further refined the ten classes into groups based on grain size. A preliminary map of surficial grain size of South Bay was developed from the combination of the seabed classification and the grab and benthic samples. The initial seabed classification map, the grain size map, and locations of sediment samples will be displayed along with the methods of acousitc seabed classification.
Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD).

PubMed

Ohayon, Maurice M; Reynolds, Charles F

2009-10-01

Although the epidemiology of insomnia in the general population has received considerable attention in the past 20 years, few studies have investigated the prevalence of insomnia using operational definitions such as those set forth in the ICSD and DSM-IV, specifying what proportion of respondents satisfied the criteria to reach a diagnosis of insomnia disorder. This is a cross-sectional study involving 25,579 individuals aged 15 years and over representative of the general population of France, the United Kingdom, Germany, Italy, Portugal, Spain and Finland. The participants were interviewed on sleep habits and disorders managed by the Sleep-EVAL expert system using DSM-IV and ICSD classifications. At the complaint level, too short sleep (20.2%), light sleep (16.6%), and global sleep dissatisfaction (8.2%) were reported by 37% of the subjects. At the symptom level (difficulty initiating or maintaining sleep and non-restorative sleep at least 3 nights per week), 34.5% of the sample reported at least one of them. At the criterion level, (symptoms+daytime consequences), 9.8% of the total sample reported having them. At the diagnostic level, 6.6% satisfied the DSM-IV requirement for positive and differential diagnosis. However, many respondents failed to meet diagnostic criteria for duration, frequency and severity in the two classifications, suggesting that multidimensional measures are needed. A significant proportion of the population with sleep complaints do not fit into DSM-IV and ICSD classifications. Further efforts are needed to identify diagnostic criteria and dimensional measures that will lead to insomnia diagnoses and thus provide a more reliable, valid and clinically relevant classification.
U.S. Level III and IV Ecoregions (U.S. EPA)

EPA Pesticide Factsheets

This map service displays Level III and Level IV Ecoregions of the United States and was created from ecoregion data obtained from the U.S. Environmental Protection Agency Office of Research and Development's Western Ecology Division. The original ecoregion data was projected from Albers to Web Mercator for this map service. To download shapefiles of ecoregion data (in Albers), please go to: ftp://newftp.epa.gov/EPADataCommons/ORD/Ecoregions/. IMPORTANT NOTE ABOUT LEVEL IV POLYGON LEGEND DISPLAY IN ARCMAP: Due to the limitations of Graphical Device Interface (GDI) resources per application on Windows, ArcMap does not display the legend in the Table of Contents for the ArcGIS Server service layer if the legend has more than 100 items. As of December 2011, there are 968 unique legend items in the Level IV Ecoregion Polygon legend. Follow this link (http://support.esri.com/en/knowledgebase/techarticles/detail/33741) for instructions about how to increase the maximum number of ArcGIS Server service layer legend items allowed for display in ArcMap. Note the instructions at this link provide a slightly incorrect path to Maximum Legend Count. The correct path is HKEY_CURRENT_USER > Software > ESRI > ArcMap > Server > MapServerLayer > Maximum Legend Count. When editing the Maximum Legend Count, update the field, Value data to 1000. To download a PDF version of the Level IV ecoregion map and legend, go to ftp://newftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/Eco_Level_IV
A simulation of Earthquake Loss Estimation in Southeastern Korea using HAZUS and the local site classification Map

NASA Astrophysics Data System (ADS)

Kang, S.; Kim, K.

2013-12-01

Regionally varying seismic hazards can be estimated using an earthquake loss estimation system (e.g. HAZUS-MH). The estimations for actual earthquakes help federal and local authorities develop rapid, effective recovery measures. Estimates for scenario earthquakes help in designing a comprehensive earthquake hazard mitigation plan. Local site characteristics influence the ground motion. Although direct measurements are desirable to construct a site-amplification map, such data are expensive and time consuming to collect. Thus we derived a site classification map of the southern Korean Peninsula using geologic and geomorphologic data, which are readily available for the entire southern Korean Peninsula. Class B sites (mainly rock) are predominant in the area, although localized areas of softer soils are found along major rivers and seashores. The site classification map is compared with independent site classification studies to confirm our site classification map effectively represents the local behavior of site amplification during an earthquake. We then estimated the losses due to a magnitude 6.7 scenario earthquake in Gyeongju, southeastern Korea, with and without the site classification map. Significant differences in loss estimates were observed. The loss without the site classification map decreased without variation with increasing epicentral distance, while the loss with the site classification map varied from region to region, due to both the epicentral distance and local site effects. The major cause of the large loss expected in Gyeongju is the short epicentral distance. Pohang Nam-Gu is located farther from the earthquake source region. Nonetheless, the loss estimates in the remote city are as large as those in Gyeongju and are attributed to the site effect of soft soil found widely in the area.
Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT

PubMed Central

Nelson, Scott D; Parker, Jaqui; Lario, Robert; Winnenburg, Rainer; Erlbaum, Mark S.; Lincoln, Michael J.; Bodenreider, Olivier

2018-01-01

Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had an equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings. PMID:29295234
Mapping forest types in Worcester County, Maryland, using LANDSAT data

NASA Technical Reports Server (NTRS)

Burtis, J., Jr.; Witt, R. G.

1981-01-01

The feasibility of mapping Level 2 forest cover types for a county-sized area on Maryland's Eastern Shore was demonstrated. A Level 1 land use/land cover classification was carried out for all of Worcester County as well. A June 1978 LANDSAT scene was utilized in a classification which employed two software packages on different computers (IDIMS on an HP 3000 and ASTEP-II on a Univac 1108). A twelve category classification scheme was devised for the study area. Resulting products include black and white line printer maps, final color coded classification maps, digitally enhanced color imagery and tabulated acreage statistics for all land use and land cover types.
Refining Landsat classification results using digital terrain data

USGS Publications Warehouse

Miller, Wayne A.; Shasby, Mark

1982-01-01

Scientists at the U.S. Geological Survey's Earth Resources Observation systems (EROS) Data Center have recently completed two land-cover mapping projects in which digital terrain data were used to refine Landsat classification results. Digital ter rain data were incorporated into the Landsat classification process using two different procedures that required developing decision criteria either subjectively or quantitatively. The subjective procedure was used in a vegetation mapping project in Arizona, and the quantitative procedure was used in a forest-fuels mapping project in Montana. By incorporating digital terrain data into the Landsat classification process, more spatially accurate landcover maps were produced for both projects.
Alabama-Mississippi Coastal Classification Maps - Perdido Pass to Cat Island

USGS Publications Warehouse

Morton, Robert A.; Peterson, Russell L.

2005-01-01

The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high-priority because they have dense populations or valuable resources that are at risk from storm waves. Another purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States. A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability. The map above shows the areas covered by this web site. Click on any of the location names or outlines to view the Coastal Classification Map for that area.
Impact of H3.3 K27M Mutation on Prognosis and Survival of Grade IV Spinal Cord Glioma on the Basis of New 2016 World Health Organization Classification of the Central Nervous System.

PubMed

Yi, Seong; Choi, Sunkyu; Shin, Dong Ah; Kim, Du Su; Choi, Junjeong; Ha, Yoon; Kim, Keung Nyun; Suh, Chang-Ok; Chang, Jong Hee; Kim, Se Hoon; Yoon, Do Heum

2018-05-01

Spinal cord glioma grade IV is a rare, diffuse midline glioma. H3 K27M-mutant was classified in a different entity in the 2016 World Health Organization (WHO) classification recently. No reports about prognosis of spinal cord glioma grade IV are available yet. To analyze the prognostic factors for spinal cord glioma grade IV. Twenty-five patients with spinal cord glioma of grade IV who underwent surgery in a single institute were selected. All grade IV spinal cord glioma histologically confirmed as glioblastoma or "diffuse midline glioma with H3 K27M-mutant" by the 2016 WHO classification of the central nervous system were included. Basic demographics, treatment modalities, and pathological tumor molecular profiles were investigated for prognosis. Mean age was 39.1 yr; male to female ratio was 18 : 7. Tumor was located in thoracic cord (53.3%), cervical cord (40%), and lumbar area (6.7%). Median overall survival was 37.1 mo; median disease-free survival was 18.5 mo. Treatment modality showed no statistical difference. Only K27M profile showed significant prognostic value, 20 patients (80%) showed K27M mutation positive, K27M mutation patients showed longer overall survival (40.07 mo) than K27M negative patients (11.63 mo, P < .0001), and disease-free survival (20.85 vs 8.72 mo, P = .0241). This study is the first and largest report of the prognosis of primary spinal cord grade IV glioma using the new WHO classification. This study reported survival analysis and prognostic factors, and revealed that H3.3 K27M mutation is not a major poor prognostic factor. Further studies to explore K27M mutations needed for risk stratification and therapy optimization.
[Preoperative risk assessment with the ASA classification. A prospective study of morbidity and mortality in various ASA classes in 2,937 patients in general surgery].

PubMed

Menke, H; John, K D; Klein, A; Lorenz, W; Junginger, T

1992-12-01

The value of ASA classification in assessment of perioperative risk, i.e. especially postoperative morbidity, was analyzed prospectively using the data of 2937 patients. The analysis took into account the criteria validity, reliability, and sensitivity. The incidence of post-operative morbidity after elective surgery rose from 3.9% in ASA class I to 36% in ASA class IV. Mortality was 0.6% in ASA class II, whereas 9.3% died in ASA class IV. Morbidity, mortality respectively, after emergency surgery was 10.2% in ASA class II compared to 69% in class IV, mortality 1.4% compared to 21.5%. Differences between the ASA classes were confirmed (p-value < 0.05) considering separate kinds of complications and different periods. Furthermore, ASA classification was a valuable reference to length of stay and severity of necessary therapy at the ICU.
Alport syndrome: a unified classification of genetic disorders of collagen IV α345: a position paper of the Alport Syndrome Classification Working Group.

PubMed

Kashtan, Clifford E; Ding, Jie; Garosi, Guido; Heidet, Laurence; Massella, Laura; Nakanishi, Koichi; Nozu, Kandai; Renieri, Alessandra; Rheault, Michelle; Wang, Fang; Gross, Oliver

2018-05-01

Mutations in the genes COL4A3, COL4A4, and COL4A5 affect the synthesis, assembly, deposition, or function of the collagen IV α345 molecule, the major collagenous constituent of the mature mammalian glomerular basement membrane. These mutations are associated with a spectrum of nephropathy, from microscopic hematuria to progressive renal disease leading to ESRD, and with extrarenal manifestations such as sensorineural deafness and ocular anomalies. The existing nomenclature for these conditions is confusing and can delay institution of appropriate nephroprotective therapy. Herein we propose a new classification of genetic disorders of the collagen IV α345 molecule with the goal of improving renal outcomes through regular monitoring and early treatment. Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
Classification and spatial mapping of riparian habitat with applications toward management of streams impacted by nonpoint source pollution

NASA Astrophysics Data System (ADS)

Delong, Michael D.; Brusven, Merlyn A.

1991-07-01

Management of riparian habitats has been recognized for its importance in reducing instream effects of agricultural nonpoint source pollution. By serving as a buffer, well structured riparian habitats can reduce nonpoint source impacts by filtering surface runoff from field to stream. A system has been developed where key characteristics of riparian habitat, vegetation type, height, width, riparian and shoreline bank slope, and land use are classified as discrete categorical units. This classification system recognizes seven riparian vegetation types, which are determined by dominant plant type. Riparian and shoreline bank slope, in addition to riparian width and height, each consist of five categories. Classification by discrete units allows for ready digitizing of information for production of spatial maps using a geographic information system (GIS). The classification system was tested for field efficiency on Tom Beall Creek watershed, an agriculturally impacted third-order stream in the Clearwater River drainage, Nez Perce County, Idaho, USA. The classification system was simple to use during field applications and provided a good inventory of riparian habitat. After successful field tests, spatial maps were produced for each component using the Professional Map Analysis Package (pMAP), a GIS program. With pMAP, a map describing general riparian habitat condition was produced by combining the maps of components of riparian habitat, and the condition map was integrated with a map of soil erosion potential in order to determine areas along the stream that are susceptible to nonpoint source pollution inputs. Integration of spatial maps of riparian classification and watershed characteristics has great potential as a tool for aiding in making management decisions for mitigating off-site impacts of agricultural nonpoint source pollution.
[Generalized anxiety disorder, now and the future: a perspective to the DSM-5].

PubMed

Otsubo, Tempei

2012-01-01

Generalized, persistent, and free-floating anxiety was first described by Freud in 1894. The diagnostic term generalized anxiety disorder (GAD) was not in classification systems until the publication of the diagnostic and statistical manual for mental disorders, third edition (DSM-III) in 1980. Initially considered as a residual category to be used when no other diagnosis could be made, it is not accepted that GAD represents a distinct diagnostic category yet. Since 1980, revisions to the diagnostic criteria for GAD in the DSM-III-R, DSM-IV and DSM-5 classifications have slightly redefined this disorder. The classification is fluid. The duration criterion has increased to 6 months in DSM-IV, but decreased to 3 months in DSM-5. This article reviews the development of diagnostic criteria for defining GAD from Freud to DSM-5 and compares the DSM-5 criterion with DSM-IV and the tenth revision of the International Classification of Disease. The impact of the changes in diagnostic criteria on research into GAD, and on diagnosis, differential diagnosis, will be discussed.
Mapping the Natchez Trace Parkway

USGS Publications Warehouse

Rangoonwala, Amina; Bannister, Terri; Ramsey, Elijah W.

2011-01-01

Based on a National Park Service (NPS) landcover classification, a landcover map of the 715-km (444-mile) NPS Natchez Trace Parkway (hereafter referred to as the "Parkway") was created. The NPS landcover classification followed National Vegetation Classification (NVC) protocols. The landcover map, which extended the initial landcover classification to the entire Parkway, was based on color-infrared photography converted to 1-m raster-based digital orthophoto quarter quadrangles, according to U.S. Geological Survey mapping standards. Our goal was to include as many alliance classes as possible in the Parkway landcover map. To reach this goal while maintaining a consistent and quantifiable map product throughout the Parkway extent, a mapping strategy was implemented based on the migration of class-based spectral textural signatures and the congruent progressive refinement of those class signatures along the Parkway. Progressive refinement provided consistent mapping by evaluating the spectral textural distinctiveness of the alliance-association classes, and where necessary, introducing new map classes along the Parkway. By following this mapping strategy, the use of raster-based image processing and geographic information system analyses for the map production provided a quantitative and reproducible product. Although field-site classification data were severely limited, the combination of spectral migration of class membership along the Parkway and the progressive classification strategy produced an organization of alliances that was internally highly consistent. The organization resulted from the natural patterns or alignments of spectral variance and the determination of those spectral patterns that were compositionally similar in the dominant species as NVC alliances. Overall, the mapped landcovers represented the existent spectral textural patterns that defined and encompassed the complex variety of compositional alliances and associations of the Parkway. Based on that mapped representation, forests dominate the Parkway landscape. Grass is the second largest Parkway land cover, followed by scrub-shrub and shrubland classes and pine plantations. The map provides a good representation of the landcover patterns and their changes over the extent of the Parkway, south to north.
Linkage mapping of a mouse gene, iv, that controls left-right asymmetry of the heart and viscera.

PubMed Central

Brueckner, M; D'Eustachio, P; Horwich, A L

1989-01-01

Inherited single gene defects have been identified in both humans and mice that lead to loss of developmental control over the left-right asymmetry of the heart and viscera. In mice the recessively inherited mutation iv leads to such apparent loss of control over situs: 50% of iv/iv mice exhibit situs inversus and 50% exhibit normal situs. The affected gene product has not been identified in these animals. To study the normal function of iv, we have taken an approach directed to the gene itself. As a first step, we have mapped iv genetically, by examining its segregation in backcrosses with respect to markers defined by restriction fragment length polymorphisms. The iv locus lies 3 centimorgans (cM) from the immunoglobulin heavy-chain constant-region gene complex (Igh-C) on chromosome 12. A multilocus map of the region suggests the gene order centromere-Aat (alpha 1-antitrypsin gene complex)-(11 cM)-iv-(3 cM)-Igh-C-(1 cM)-Igh-V (immunoglobulin heavy-chain variable-region gene complex). Images PMID:2740340
Essential core of the Hawking–Ellis types

NASA Astrophysics Data System (ADS)

Martín-Moruno, Prado; Visser, Matt

2018-06-01

The Hawking–Ellis (Segre–Plebański) classification of possible stress–energy tensors is an essential tool in analyzing the implications of the Einstein field equations in a more-or-less model-independent manner. In the current article the basic idea is to simplify the Hawking–Ellis type I, II, III, and IV classification by isolating the ‘essential core’ of the type II, type III, and type IV stress–energy tensors; this being done by subtracting (special cases of) type I to simplify the (Lorentz invariant) eigenvalue structure as much as possible without disturbing the eigenvector structure. We will denote these ‘simplified cores’ type II0, type III0, and type IV0. These ‘simplified cores’ have very nice and simple algebraic properties. Furthermore, types I and II0 have very simple classical interpretations, while type IV0 is known to arise semi-classically (in renormalized expectation values of standard stress–energy tensors). In contrast type III0 stands out in that it has neither a simple classical interpretation, nor even a simple semi-classical interpretation. We will also consider the robustness of this classification considering the stability of the different Hawking–Ellis types under perturbations. We argue that types II and III are definitively unstable, whereas types I and IV are stable.
An automated approach to mapping corn from Landsat imagery

USGS Publications Warehouse

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M.

2004-01-01

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.
Multidate mapping of mosquito habitat. [Nebraska, South Dakota

NASA Technical Reports Server (NTRS)

Woodzick, T. L.; Maxwell, E. L.

1977-01-01

LANDSAT data from three overpasses formed the data base for a multidate classification of 15 ground cover categories in the margins of Lewis and Clark Lake, a fresh water impoundment between South Dakota and Nebraska. When scaled to match topographic maps of the area, the ground cover classification maps were used as a general indicator of potential mosquito-breeding habitat by distinguishing productive wetlands areas from nonproductive nonwetlands areas. The 12 channel multidate classification was found to have an accuracy 23% higher than the average of the three single date 4 channel classifications.

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

NASA Astrophysics Data System (ADS)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.
Agricultural conservation planning framework: 2. Classification of riparian buffer design types with application to assess and map stream corridors.

PubMed

Tomer, M D; Boomer, K M B; Porter, S A; Gelder, B K; James, D E; McLellan, E

2015-05-01

A watershed's riparian corridor presents opportunities to stabilize streambanks, intercept runoff, and influence shallow groundwater with riparian buffers. This paper presents a system to classify these riparian opportunities and apply them toward riparian management planning in hydrologic unit code 12 watersheds. In two headwater watersheds from each of three landform regions found in Iowa and Illinois, high-resolution (3-m grid) digital elevation models were analyzed to identify spatial distributions of surface runoff contributions and zones with shallow water tables (SWTs) (within 1.5 m of the channel elevation) along the riparian corridors. Results were tabulated, and a cross classification was applied. Classes of buffers include those primarily placed to (i) trap runoff and sediment, (ii) influence shallow groundwater, (iii) address both runoff and shallow groundwater, and (iv) maintain/improve stream bank stability. Riparian buffers occupying about 2.5% of these six watersheds could effectively intercept runoff contributions from 81 to 94% of the watersheds' contributing areas. However, extents of riparian zones where a narrow buffer (<10 m wide) would adequately intercept runoff but where >25 m width of buffer vegetation could root to a SWT varied according to landform region ( < 0.10). Yet, these wide-SWT riparian zones were widespread and occupied 23 to 53% of the lengths of stream banks among the six watersheds. The wide-SWT setting provides opportunities to reduce dissolved nutrients (particularly NO-N) carried via groundwater. This riparian classification and mapping system is part of a ArcGIS toolbox and could provide a consistent basis to identify riparian management opportunities in Midwestern headwater catchments wherever high-resolution elevation data are available. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Exploring new classification criteria for the earliest type stars: the 3400 Aregion

NASA Astrophysics Data System (ADS)

Morrell, Nidia I.; Walborn, Nolan R.; Arias, Julia I.

2002-02-01

We propose spectroscopic observations of a sample of standard O2-O4 stars in the wavelength region containing the N IV 3479-83-85 Aand O IV 3381-85-3412 Alines, in order to analyze the behavior of these spectral features as a function of the spectral type. We aim to define new classification criteria for the hottest stars, evaluating these N IV and O IV lines near 3400 Aas possible temperature and luminosity discriminators. The former spectral class O3 has just been split into three different classes: O2, O3 and O3.5 (Walborn et al. 2001). The paucity of classification criteria at these types in the traditional wavelength domain (4000 - 4700 Å), makes clear the need to explore other spectral ranges in order to define additional constraints on the determination of spectral types and luminosity classes. The wavelength range around 3400 Ahas been observed in many faint, crowded early O-type stars by HST/FOS, the corresponding data being available from the HST archive. This enhances our interest in observing this spectral range in the classification standards for the early O-type stars in order to make these existing HST observations even more useful, allowing the determination of accurate spectral types for unknown objects from them, once the behavior of the new criteria in the standards has been charted.
The Effect of Draft DSM-5 Criteria on Posttraumatic Stress Disorder Prevalence

PubMed Central

Calhoun, Patrick S.; Hertzberg, Jeffrey S.; Kirby, Angela C.; Dennis, Michelle F.; Hair, Lauren P.; Dedert, Eric A.; Beckham, Jean C.

2012-01-01

Background This study was designed to examine the concordance of proposed DSM-5 posttraumatic stress disorder (PTSD) criteria with DSM-IV classification rules and examine the impact of the proposed DSM-5 PTSD criteria on prevalence. Method The sample (N=185) included participants who were recruited for studies focused on trauma and health conducted at an academic medical center and VA medical center in the southeastern United States. The prevalence and concordance between DSM-IV and the proposed DSM-5 classifications were calculated based on results from structured clinical interviews. Prevalence rates and diagnostic efficiency indices including sensitivity, specificity, area under the curve (AUC), and Kappa were calculated for each of the possible ways to define DSM-5 PTSD. Results Ninety-five percent of the sample reported an event that met both DSM-IV PTSD Criterion A1 and A2, but only 89% reported a trauma that met Criterion A on DSM-5. Results examining concordance between DSM-IV and DSM-5 algorithms indicated that several of the algorithms had AUCs above .90. The requirement of two symptoms from both Clusters D and E provided strong concordance to DSM-IV (AUC = .93; Kappa = .86) and a greater balance between sensitivity and specificity than requiring three symptoms in both Clusters D and E. Conclusions Despite several significant changes to the diagnostic criteria for PTSD for DSM-5, several possible classification rules provided good concordance with DSM-IV. The magnitude of the impact of DSM-5 decision rules on prevalence will be largely affected by the DSM-IV PTSD base rate in the population of interest. PMID:23109002
ReadXplorer—visualization and analysis of mapped sequences

PubMed Central

Hilker, Rolf; Stadermann, Kai Bernd; Doppmeier, Daniel; Kalinowski, Jörn; Stoye, Jens; Straube, Jasmin; Winnebald, Jörn; Goesmann, Alexander

2014-01-01

Motivation: Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. Results: ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion–insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. Availability and implementation: ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual. Contact: rhilker@mikrobio.med.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24790157
Semi-automated landform classification for hazard mapping of soil liquefaction by earthquake

NASA Astrophysics Data System (ADS)

Nakano, Takayuki

2018-05-01

Soil liquefaction damages were caused by huge earthquake in Japan, and the similar damages are concerned in near future huge earthquake. On the other hand, a preparation of soil liquefaction risk map (soil liquefaction hazard map) is impeded by the difficulty of evaluation of soil liquefaction risk. Generally, relative soil liquefaction risk should be able to be evaluated from landform classification data by using experimental rule based on the relationship between extent of soil liquefaction damage and landform classification items associated with past earthquake. Therefore, I rearranged the relationship between landform classification items and soil liquefaction risk intelligibly in order to enable the evaluation of soil liquefaction risk based on landform classification data appropriately and efficiently. And I developed a new method of generating landform classification data of 50-m grid size from existing landform classification data of 250-m grid size by using digital elevation model (DEM) data and multi-band satellite image data in order to evaluate soil liquefaction risk in detail spatially. It is expected that the products of this study contribute to efficient producing of soil liquefaction hazard map by local government.
Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).
A 3-tier classification of cerebral arteriovenous malformations. Clinical article.

PubMed

Spetzler, Robert F; Ponce, Francisco A

2011-03-01

The authors propose a 3-tier classification for cerebral arteriovenous malformations (AVMs). The classification is based on the original 5-tier Spetzler-Martin grading system, and reflects the treatment paradigm for these lesions. The implications of this modification in the literature are explored. Class A combines Grades I and II AVMs, Class B are Grade III AVMs, and Class C combines Grades IV and V AVMs. Recommended management is surgery for Class A AVMs, multimodality treatment for Class B, and observation for Class C, with exceptions to the latter including recurrent hemorrhages and progressive neurological deficits. To evaluate whether combining grades is warranted from the perspective of surgical outcomes, the 3-tier system was applied to 1476 patients from 7 surgical series in which results were stratified according to Spetzler-Martin grades. Pairwise comparisons of individual Spetzler-Martin grades in the series analyzed showed the fewest significant differences (p < 0.05) in outcomes between Grades I and II AVMs and between Grades IV and V AVMs. In the pooled data analysis, significant differences in outcomes were found between all grades except IV and V (p = 0.38), and the lowest relative risks were found between Grades I and II (1.066) and between Grades IV and V (1.095). Using the pooled data, the predictive accuracies for surgical outcomes of the 5-tier and 3-tier systems were equivalent (receiver operating characteristic curve area 0.711 and 0.713, respectively). Combining Grades I and II AVMs and combining Grades IV and V AVMs is justified in part because the differences in surgical results between these respective pairs are small. The proposed 3-tier classification of AVMs offers simplification of the Spetzler-Martin system, provides a guide to treatment, and is predictive of outcome. The revised classification not only simplifies treatment recommendations; by placing patients into 3 as opposed to 5 groups, statistical power is markedly increased for series comparisons.
Asiago spectroscopic classification of 5 ASASSN SNe

NASA Astrophysics Data System (ADS)

Tomasella, L.; Benetti, S.; Cappellaro, E.; Turatto, M.

2018-04-01

The Asiago Transient Classification Program (Tomasella et al. 2014, AN, 335, 841) reports the spectroscopic classification of ASASSN-18ii,ASASSN-18it, ASASSN-18iv, ASASN-18iw, ASASSN-18iu discovered during the ongoing All Sky Automated Survey for SuperNovae (ASAS-SN, Shappee et al. 2014) (Atel #11178).
Indicators of Terrorism Vulnerability in Africa

DTIC Science & Technology

2015-03-26

the terror threat and vulnerabilities across Africa. Key words: Terrorism, Africa, Negative Binomial Regression, Classification Tree iv I would like...31 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Log -likelihood...70 viii Page 5.3 Classification Tree Description
GEOBIA For Land Use Mapping Using Worldview2 Image In Bengkak Village Coastal, Banyuwangi Regency, East Java

NASA Astrophysics Data System (ADS)

Alrassi, Fitzastri; Salim, Emil; Nina, Anastasia; Alwi, Luthfi; Danoedoro, Projo; Kamal, Muhammad

2016-11-01

The east coast of Banyuwangi regency has a diverse variety of land use such as ponds, mangroves, agricultural fields and settlements. WorldView-2 is a multispectral image with high spatial resolution that can display detailed information of land use. Geographic Object Based Image Analysis (GEOBIA) classification technique uses object segments as the smallest unit of analysis. The segmentation and classification process is not only based on spectral value of the image but also considering other elements of the image interpretation. This gives GEOBIA an opportunities and challenges in the mapping and monitoring of land use. This research aims to assess the GEOBIA classification method for generating the classification of land use in coastal areas of Banyuwangi. The result of this study is land use classification map produced by GEOBIA classification. We verified the accuracy of the resulted land use map by comparing the map with result from visual interpretation of the image that have been validated through field surveys. Variation of land use in most of the east coast of Banyuwangi regency is dominated by mangrove, agricultural fields, mixed farms, settlements and ponds.
Implementing Classification on a Munitions Response Project

DTIC Science & Technology

2011-12-01

Detection Dig List IVS/Seed Site Planning Decisions Dig All Anomalies Site Characterization Implementing Classification on a Munitions Response...Details ● Seed emplacement ● EM61-MK2 detection survey  RTK GPS ● Select anomalies for further investigation ● Collect cued data using MetalMapper...5.2 mV in channel 2  938 anomalies selected ● All QC seeds detected using this threshold  Some just inside the 60-cm halo ● IVS reproducibility
Mapping fuels at multiple scales: landscape application of the fuel characteristic classification system.

Treesearch

D. McKenzie; C.L. Raymond; L.-K.B. Kellogg; R.A. Norheim; A.G. Andreu; A.C. Bayard; K.E. Kopper; E. Elman

2007-01-01

Fuel mapping is a complex and often multidisciplinary process, involving remote sensing, ground-based validation, statistical modeling, and knowledge-based systems. The scale and resolution of fuel mapping depend both on objectives and availability of spatial data layers. We demonstrate use of the Fuel Characteristic Classification System (FCCS) for fuel mapping at two...
Proposal for a new classification of variations in the iliac venous system based on internal iliac veins: a case series and a review of double and left inferior vena cava.

PubMed

Hayashi, Shogo; Naito, Munekazu; Hirai, Shuichi; Terayama, Hayato; Miyaki, Takayoshi; Itoh, Masahiro; Fukuzawa, Yoshitaka; Nakano, Takashi

2013-09-01

There are many reports on variations in the inferior vena cava (IVC), particularly double IVC (DIVC) and left IVC (LIVC). However, no systematic report has recorded iliac vein (IV) flow patterns in the DIVC and LIVC. In this study, we examined IV flow patterns in both DIVC and LIVC observed during gross anatomy courses conducted for medical students and in previously reported cases. During the gross anatomy courses, three cases of DIVC and one case of LIVC were found in 618 cadavers. The IV flow pattern from these four cases and all other previously reported cases can be classified into one of the following three types according to the vein into which the internal iliac vein drained: the ipsilateral external IV; confluence of the ipsilateral external IV and IVC; and the communicating vein, which connects the IVC and the contralateral IVC or its iliac branch. This classification, which is based on the internal IV course, is considered to be useful because IV variations have the potential to cause clinical problems during related retroperitoneal surgery, venous interventional radiology, and diagnostic procedures for pelvic cancer.
[Alcohol-related cognitive impairment and the DSM-5].

PubMed

Walvoort, S J W; Wester, A J; Doorakkers, M C; Kessels, R P C; Egger, J I M

2016-01-01

It is evident from the dsm-iv-tr that alcohol-related impairment is extremely difficult to classify accurately. As a result, cognitive deficits can easily be overlooked. The dsm-5, however, incorporates a new category, namely 'neurocognitive disorders', which may lead to significant improvements in clinical practice. To compare the classification of alcohol-related cognitive dysfunction in dsm-iv-tr and dsm-5 and to discuss the clinical relevance of the revised classification in the dsm-5. We compare the chapters of the dsm-iv-tr and the dsm-5 concerning alcohol-related cognitive impairment and describe the changes that have been made. The dsm-5 puts greater emphasis on alcohol-related neurocognitive impairment. Not only does dsm-5 distinguish between the degree of severity (major or minor neurocognitive disorder), it also distinguishes between the type of impairment (non-amnestic-type versus confabulating-amnestic type). It also makes a distinction between the durations of impairment (behavioural and/or persistent disorders). The dsm-5 gives a clearer description of alcohol-related neurocognitive dysfunction than does dsm-iv-tr and it stresses the essential role of neuropsychological assessment in the classification, diagnosis, and treatment of neurocognitive disorders.
[Crops: Classifying, Selecting, Harvesting, Grading, and Packing.] Student Materials. V.A. III. [IV-B-1 through IV-B-2; IV-C-1 through IV-C-2].

ERIC Educational Resources Information Center

Texas A and M Univ., College Station. Vocational Instructional Services.

Part of a series of eight student learning modules in vocational agriculture, this booklet deals with crop-related activities. The first section is on harvesting methods and equipment. The following portions address the handling, grading, and packing of crops; and the classification and selection of fruits, vegetables, and ornamental plants. There…
A comparison of outcomes according to different diagnostic systems for delirium (DSM-5, DSM-IV, CAM, and DRS-R98).

PubMed

Adamis, Dimitrios; Meagher, David; Rooney, Siobhan; Mulligan, Owen; McCarthy, Geraldine

2018-04-01

ABSTRACTStudies indicate that DSM-5 criteria for delirium are relatively restrictive, and identify different cases of delirium compared with previous systems. We evaluate four outcomes of delirium (mortality, length of hospital stay, institutionalization, and cognitive improvement) in relation to delirium defined by different DSM classification systems.Prospective, longitudinal study of patients aged 70+ admitted to medical wards of a general hospital. Participants were assessed up to a maximum of four times during two weeks, using DSM-5 and DSM-IV criteria, DRS-R98 and CAM scales as proxies for DSM III-R and DSM III.Of the 200 assessed patients (mean age 81.1, SD = 6.5; and 50% female) during hospitalization, delirium was identified in 41 (20.5%) using DSM-5, 45 (22.5%) according to DSM-IV, 46 (23%) with CAM positive, and 37 (18.5%) with DRS-R98 severity score >15. Mortality was significantly associated with delirium according to any classification system, but those identified with DSM-5 were at greater risk. Length of stay was significantly longer for those with DSM-IV delirium. Discharge to a care home was associated only with DRS-R98 defined delirium. Cognitive improvement was only associated with CAM and DSM-IV. Different classification systems for delirium identify populations with different outcomes.
Information analysis of a spatial database for ecological land classification

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Dozier, Jeff

1990-01-01

An ecological land classification was developed for a complex region in southern California using geographic information system techniques of map overlay and contingency table analysis. Land classes were identified by mutual information analysis of vegetation pattern in relation to other mapped environmental variables. The analysis was weakened by map errors, especially errors in the digital elevation data. Nevertheless, the resulting land classification was ecologically reasonable and performed well when tested with higher quality data from the region.
What is generalized anxiety disorder?

PubMed

Rickels, K; Rynn, M A

2001-01-01

Generalized, persistent, and free-floating anxiety was first described by Freud in 1894, although the diagnostic term generalized anxiety disorder (GAD) was not included in classification systems until 1980 (Diagnostic and Statistical Manual for Mental Disorders, Third Edition [DSM-III]). Initially considered a residual category to be used when no other diagnosis could be made, it is now widely accepted that GAD represents a distinct diagnostic category. Since 1980, revisions to the diagnostic criteria for GAD in the DSM-III-R and DSM-IV classifications have markedly redefined this disorder, increasing the duration criterion to 6 months and increasing the emphasis on worry and psychic symptoms. This article reviews the development of the diagnostic criteria for defining GAD from Freud to DSM-IV and compares the DSM-IV criteria with the criteria set forth in the tenth revision of the International Classification of Diseases. The impact of the changes in diagnostic criteria on research into GAD, and on diagnosis, differential diagnosis, and treatment of GAD, will be discussed.
The effect of draft DSM-V criteria on posttraumatic stress disorder prevalence.

PubMed

Calhoun, Patrick S; Hertzberg, Jeffrey S; Kirby, Angela C; Dennis, Michelle F; Hair, Lauren P; Dedert, Eric A; Beckham, Jean C

2012-12-01

This study was designed to examine the concordance of proposed DSM-V posttraumatic stress disorder (PTSD) criteria with DSM-IV classification rules and examine the impact of the proposed DSM-V PTSD criteria on prevalence. The sample (N = 185) included participants who were recruited for studies focused on trauma and health conducted at an academic medical center and VA medical center in the southeastern United States. The prevalence and concordance between DSM-IV and the proposed DSM-V classifications were calculated based on results from structured clinical interviews. Prevalence rates and diagnostic efficiency indices including sensitivity, specificity, area under the curve (AUC), and Kappa were calculated for each of the possible ways to define DSM-V PTSD. Ninety-five percent of the sample reported an event that met both DSM-IV PTSD Criterion A1 and A2, but only 89% reported a trauma that met Criterion A on DSM-V. Results examining concordance between DSM-IV and DSM-V algorithms indicated that several of the algorithms had AUCs above 0.90. The requirement of two symptoms from both Clusters D and E provided strong concordance to DSM-IV (AUC = 0.93; Kappa = 0.86) and a greater balance between sensitivity and specificity than requiring three symptoms in both Clusters D and E. Despite several significant changes to the diagnostic criteria for PTSD for DSM-V, several possible classification rules provided good concordance with DSM-IV. The magnitude of the impact of DSM-V decision rules on prevalence will be largely affected by the DSM-IV PTSD base rate in the population of interest. © 2012 Wiley Periodicals, Inc.

Five systems of psychiatric classification for preschool children: do differences in validity, usefulness and reliability make for competitive or complimentary constellations?

PubMed

Postert, Christian; Averbeck-Holocher, Marlies; Beyer, Thomas; Müller, Jörg; Furniss, Tilman

2009-03-01

DSM-IV and ICD-10 have limitations in the diagnostic classification of psychiatric disorders at preschool age (0-5 years). The publication of the Diagnostic Classification 0-3 (DC:0-3) in 1994, its basically revised second edition (DC:0-3R) in 2005 and the Research Diagnostic Criteria-Preschool Age (RDC-PA) in 2004 have provided several modifications of these manuals. Taking into account the growing empirical evidence highlighting the need for a diagnostic classification system for psychiatric disorders in preschool children, the main categorical classification systems in preschool psychiatry will be presented and discussed. The paper will focus on issues of validity, usefulness and reliability in DSM-IV, ICD-10, RDC-PA, DC:0-3, and DC:0-3R. The reasons for including or excluding postulated psychiatric disorder categories for preschool children with variable degrees of empirical evidence into the different diagnostic systems will be discussed.
The impact of endorsing Spitzer's proposed criteria for PTSD in the forthcoming DSM-V on male and female Veterans.

PubMed

Miller, Lyndsey N; Chard, Kathleen M; Schumm, Jeremiah A; O'Brien, Carol

2011-06-01

This study explored differences between Spitzer's proposed model of posttraumatic stress disorder (PTSD) and the current DSM-IV diagnostic classification scheme in 353 Veterans. The majority of Veterans (89%) diagnosed with PTSD as specified in the DSM-IV also met Spitzer's proposed criteria. Veterans who met both DSM-IV and Spitzer's proposed criteria had significantly higher Clinician Administered PTSD Scale severity scores than Veterans only meeting DSM-IV criteria. Logistic regression indicated that being African American and having no comorbid diagnosis of major depressive disorder or history of a substance use disorder were found to predict those Veterans who met current, but not proposed criteria. These findings have important implications regarding proposed changes to the diagnostic classification criteria for PTSD in the forthcoming DSM-V. Copyright © 2011 Elsevier Ltd. All rights reserved.
Analyzing thematic maps and mapping for accuracy

USGS Publications Warehouse

Rosenfield, G.H.

1982-01-01

Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by either the row totals or the column totals from the original classification error matrices. In hypothesis testing, when the results of tests of multiple sample cases prove to be significant, some form of statistical test must be used to separate any results that differ significantly from the others. In the past, many analyses of the data in this error matrix were made by comparing the relative magnitudes of the percentage of correct classifications, for either individual categories, the entire map or both. More rigorous analyses have used data transformations and (or) two-way classification analysis of variance. A more sophisticated step of data analysis techniques would be to use the entire classification error matrices using the methods of discrete multivariate analysis or of multiviariate analysis of variance.
A hierarchical framework of aquatic ecological units in North America (Nearctic Zone).

Treesearch

James R. Maxwell; Clayton J. Edwards; Mark E. Jensen; Steven J. Paustian; Harry Parrott; Donley M. Hill

1995-01-01

Proposes a framework for classifying and mapping aquatic systems at various scales using ecologically significant physical and biological criteria. Classification and mapping concepts follow tenets of hierarchical theory, pattern recognition, and driving variables. Criteria are provided for the hierarchical classification and mapping of aquatic ecological units of...
Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas.

PubMed

Mathieu, Renaud; Aryal, Jagannath; Chong, Albert K

2007-11-20

Effective assessment of biodiversity in cities requires detailed vegetation maps.To date, most remote sensing of urban vegetation has focused on thematically coarse landcover products. Detailed habitat maps are created by manual interpretation of aerialphotographs, but this is time consuming and costly at large scale. To address this issue, wetested the effectiveness of object-based classifications that use automated imagesegmentation to extract meaningful ground features from imagery. We applied thesetechniques to very high resolution multispectral Ikonos images to produce vegetationcommunity maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and amulti-scale segmentation algorithm used to produce a hierarchical network of image objects.The upper level included four coarse strata: industrial/commercial (commercial buildings),residential (houses and backyard private gardens), vegetation (vegetation patches larger than0.8/1ha), and water. We focused on the vegetation stratum that was segmented at moredetailed level to extract and classify fifteen classes of vegetation communities. The firstclassification yielded a moderate overall classification accuracy (64%, κ = 0.52), which ledus to consider a simplified classification with ten vegetation classes. The overallclassification accuracy from the simplified classification was 77% with a κ value close tothe excellent range (κ = 0.74). These results compared favourably with similar studies inother environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.
Postprocessing classification images

NASA Technical Reports Server (NTRS)

Kan, E. P.

1979-01-01

Program cleans up remote-sensing maps. It can be used with existing image-processing software. Remapped images closely resemble familiar resource information maps and can replace or supplement classification images not postprocessed by this program.
sA Comparison of DSM-IV-TR and DSM-5 Diagnostic Classifications in the Clinical Diagnosis of Autistic Spectrum Disorder

ERIC Educational Resources Information Center

Yaylaci, Ferhat; Miral, Suha

2017-01-01

Aim of this study was to compare children diagnosed with Pervasive Developmental Disorder (PDD) according to DSM-IV-TR and DSM-5 diagnostic systems. One hundred fifty children aged between 3 and 15 years diagnosed with PDD by DSM-IV-TR were included. PDD symptoms were reviewed through psychiatric assessment based on DSM-IV-TR and DSM-5 criteria.…
Field Guide to the Plant Community Types of Voyageurs National Park

USGS Publications Warehouse

Faber-Langendoen, Don; Aaseng, Norman; Hop, Kevin; Lew-Smith, Michael

2007-01-01

INTRODUCTION The objective of the U.S. Geological Survey-National Park Service Vegetation Mapping Program is to classify, describe, and map vegetation for most of the park units within the National Park Service (NPS). The program was created in response to the NPS Natural Resources Inventory and Monitoring Guidelines issued in 1992. Products for each park include digital files of the vegetation map and field data, keys and descriptions to the plant communities, reports, metadata, map accuracy verification summaries, and aerial photographs. Interagency teams work in each park and, following standardized mapping and field sampling protocols, develop products and vegetation classification standards that document the various vegetation types found in a given park. The use of a standard national vegetation classification system and mapping protocol facilitate effective resource stewardship by ensuring compatibility and widespread use of the information throughout the NPS as well as by other Federal and state agencies. These vegetation classifications and maps and associated information support a wide variety of resource assessment, park management, and planning needs, and provide a structure for framing and answering critical scientific questions about plant communities and their relation to environmental processes across the landscape. This field guide is intended to make the classification accessible to park visitors and researchers at Voyageurs National Park, allowing them to identify any stand of natural vegetation and showing how the classification can be used in conjunction with the vegetation map (Hop and others, 2001).
Benthic Habitat Mapping by Combining Lyzenga’s Optical Model and Relative Water Depth Model in Lintea Island, Southeast Sulawesi

NASA Astrophysics Data System (ADS)

Hafizt, M.; Manessa, M. D. M.; Adi, N. S.; Prayudha, B.

2017-12-01

Benthic habitat mapping using satellite data is one challenging task for practitioners and academician as benthic objects are covered by light-attenuating water column obscuring object discrimination. One common method to reduce this water-column effect is by using depth-invariant index (DII) image. However, the application of the correction in shallow coastal areas is challenging as a dark object such as seagrass could have a very low pixel value, preventing its reliable identification and classification. This limitation can be solved by specifically applying a classification process to areas with different water depth levels. The water depth level can be extracted from satellite imagery using Relative Water Depth Index (RWDI). This study proposed a new approach to improve the mapping accuracy, particularly for benthic dark objects by combining the DII of Lyzenga’s water column correction method and the RWDI of Stumpt’s method. This research was conducted in Lintea Island which has a high variation of benthic cover using Sentinel-2A imagery. To assess the effectiveness of the proposed new approach for benthic habitat mapping two different classification procedures are implemented. The first procedure is the commonly applied method in benthic habitat mapping where DII image is used as input data to all coastal area for image classification process regardless of depth variation. The second procedure is the proposed new approach where its initial step begins with the separation of the study area into shallow and deep waters using the RWDI image. Shallow area was then classified using the sunglint-corrected image as input data and the deep area was classified using DII image as input data. The final classification maps of those two areas were merged as a single benthic habitat map. A confusion matrix was then applied to evaluate the mapping accuracy of the final map. The result shows that the new proposed mapping approach can be used to map all benthic objects in all depth ranges and shows a better accuracy compared to that of classification map produced using only with DII.
An advanced method for classifying atmospheric circulation types based on prototypes connectivity graph

NASA Astrophysics Data System (ADS)

Zagouras, Athanassios; Argiriou, Athanassios A.; Flocas, Helena A.; Economou, George; Fotopoulos, Spiros

2012-11-01

Classification of weather maps at various isobaric levels as a methodological tool is used in several problems related to meteorology, climatology, atmospheric pollution and to other fields for many years. Initially the classification was performed manually. The criteria used by the person performing the classification are features of isobars or isopleths of geopotential height, depending on the type of maps to be classified. Although manual classifications integrate the perceptual experience and other unquantifiable qualities of the meteorology specialists involved, these are typically subjective and time consuming. Furthermore, during the last years different approaches of automated methods for atmospheric circulation classification have been proposed, which present automated and so-called objective classifications. In this paper a new method of atmospheric circulation classification of isobaric maps is presented. The method is based on graph theory. It starts with an intelligent prototype selection using an over-partitioning mode of fuzzy c-means (FCM) algorithm, proceeds to a graph formulation for the entire dataset and produces the clusters based on the contemporary dominant sets clustering method. Graph theory is a novel mathematical approach, allowing a more efficient representation of spatially correlated data, compared to the classical Euclidian space representation approaches, used in conventional classification methods. The method has been applied to the classification of 850 hPa atmospheric circulation over the Eastern Mediterranean. The evaluation of the automated methods is performed by statistical indexes; results indicate that the classification is adequately comparable with other state-of-the-art automated map classification methods, for a variable number of clusters.
Alaska Interim Land Cover Mapping Program; final report

USGS Publications Warehouse

Fitzpatrick-Lins, Katherine; Doughty, E.F.; Shasby, Mark; Benjamin, Susan

1989-01-01

In 1985, the U.S. Geological Survey initiated a research project to develop an interim land cover data base for Alaska as an alternative to the nationwide Land Use and Land Cover Mapping Program. The Alaska Interim Land Cover Mapping Program was subsequently created to develop methods for producing a series of land cover maps that utilized the existing Landsat digital land cover classifications produced by and for the major land management agencies for mapping the vegetation of Alaska. The program was successful in producing digital land cover classifications and statistical summaries using a common statewide classification and in reformatting these data to produce l:250,000-scale quadrangle-based maps directly from the Scitex laser plotter. A Federal and State agency review of these products found considerable user support for the maps. Presently the Geological Survey is committed to digital processing of six to eight quadrangles each year.
Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

NASA Astrophysics Data System (ADS)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.
Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Technical Reports Server (NTRS)

Kumar, Uttam; Nemani, Ramakrishna R.; Ganguly, Sangram; Kalia, Subodh; Michaelis, Andrew

2017-01-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.
Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2017-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.
Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models

NASA Astrophysics Data System (ADS)

Porto, C. D. N.; Costa Filho, C. F. F.; Macedo, M. M. G.; Gutierrez, M. A.; Costa, M. G. F.

2017-03-01

Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.
Object oriented classification of high resolution data for inventory of horticultural crops

NASA Astrophysics Data System (ADS)

Hebbar, R.; Ravishankar, H. M.; Trivedi, S.; Subramoniam, S. R.; Uday, R.; Dadhwal, V. K.

2014-11-01

High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, object oriented techniques are powerful and promising methods for classification. In the present study, a semi-automatic object oriented feature extraction model has been used for delineation of horticultural fruit and plantation crops using Erdas Objective Imagine. Multi-resolution data from Resourcesat LISS-IV and Cartosat-1 have been used as source data in the feature extraction model. Spectral and textural information along with NDVI were used as inputs for generation of Spectral Feature Probability (SFP) layers using sample training pixels. The SFP layers were then converted into raster objects using threshold and clump function resulting in pixel probability layer. A set of raster and vector operators was employed in the subsequent steps for generating thematic layer in the vector format. This semi-automatic feature extraction model was employed for classification of major fruit and plantations crops viz., mango, banana, citrus, coffee and coconut grown under different agro-climatic conditions. In general, the classification accuracy of about 75-80 per cent was achieved for these crops using object based classification alone and the same was further improved using minimal visual editing of misclassified areas. A comparison of on-screen visual interpretation with object oriented approach showed good agreement. It was observed that old and mature plantations were classified more accurately while young and recently planted ones (3 years or less) showed poor classification accuracy due to mixed spectral signature, wider spacing and poor stands of plantations. The results indicated the potential use of object oriented approach for classification of high resolution data for delineation of horticultural fruit and plantation crops. The present methodology is applicable at local levels and future development is focused on up-scaling the methodology for generation of fruit and plantation crop maps at regional and national level which is important for creation of database for overall horticultural crop development.
Concordances and discrepancies between ICD-10 and DSM-IV criteria for anxiety disorders in childhood and adolescence

PubMed Central

2012-01-01

Background Mental disorders are classified by two major nosological systems, the ICD-10 and the DSM-IV-TR, consisting of different diagnostic criteria. The present study investigated the diagnostic concordance between the two systems for anxiety disorders in childhood and adolescence, in particular for separation anxiety disorder (SAD), specific phobia, social phobia, and generalized anxiety disorder (GAD). Methods A structured clinical interview, the Kinder-DIPS, was administered to 210 children and 258 parents. The percentage of agreement, kappa, and Yule’s Y coefficients were calculated for all diagnoses. Specific criteria causing discrepancies between the two classification systems were identified. Results DSM-IV-TR consistently classified more children than ICD-10 with an anxiety disorder, with a higher concordance between DSM-IV-TR and the ICD-10 child section (F9) than with the adult section (F4) of the ICD-10. This result was found for all four investigated anxiety disorders. The results revealed low to high levels of concordance and poor to good agreement between the classification systems, depending on the anxiety disorder. Conclusions The two classification systems identify different children with an anxiety disorder. However, it remains an open question, whether the research results can be generalized to clinical practice since DSM-IV-TR is mainly used in research while ICD-10 is widely established in clinical practice in Europe. Therefore, the population investigated by the DSM (research population) is not identical with the population examined using the ICD (clinical population). PMID:23267678
Revisiting flow maps: a classification and a 3D alternative to visual clutter

NASA Astrophysics Data System (ADS)

Gu, Yuhang; Kraak, Menno-Jan; Engelhardt, Yuri

2018-05-01

Flow maps have long been servicing people in exploring movement by representing origin-destination data (OD data). Due to recent developments in data collecting techniques the amount of movement data is increasing dramatically. With such huge amounts of data, visual clutter in flow maps is becoming a challenge. This paper revisits flow maps, provides an overview of the characteristics of OD data and proposes a classification system for flow maps. For dealing with problems of visual clutter, 3D flow maps are proposed as potential alternative to 2D flow maps.
Current trends in geomorphological mapping

NASA Astrophysics Data System (ADS)

Seijmonsbergen, A. C.

2012-04-01

Geomorphological mapping is a world currently in motion, driven by technological advances and the availability of new high resolution data. As a consequence, classic (paper) geomorphological maps which were the standard for more than 50 years are rapidly being replaced by digital geomorphological information layers. This is witnessed by the following developments: 1. the conversion of classic paper maps into digital information layers, mainly performed in a digital mapping environment such as a Geographical Information System, 2. updating the location precision and the content of the converted maps, by adding more geomorphological details, taken from high resolution elevation data and/or high resolution image data, 3. (semi) automated extraction and classification of geomorphological features from digital elevation models, broadly separated into unsupervised and supervised classification techniques and 4. New digital visualization / cartographic techniques and reading interfaces. Newly digital geomorphological information layers can be based on manual digitization of polygons using DEMs and/or aerial photographs, or prepared through (semi) automated extraction and delineation of geomorphological features. DEMs are often used as basis to derive Land Surface Parameter information which is used as input for (un) supervised classification techniques. Especially when using high-res data, object-based classification is used as an alternative to traditional pixel-based classifications, to cluster grid cells into homogeneous objects, which can be classified as geomorphological features. Classic map content can also be used as training material for the supervised classification of geomorphological features. In the classification process, rule-based protocols, including expert-knowledge input, are used to map specific geomorphological features or entire landscapes. Current (semi) automated classification techniques are increasingly able to extract morphometric, hydrological, and in the near future also morphogenetic information. As a result, these new opportunities have changed the workflows for geomorphological mapmaking, and their focus have shifted from field-based techniques to using more computer-based techniques: for example, traditional pre-field air-photo based maps are now replaced by maps prepared in a digital mapping environment, and designated field visits using mobile GIS / digital mapping devices now focus on gathering location information and attribute inventories and are strongly time efficient. The resulting 'modern geomorphological maps' are digital collections of geomorphological information layers consisting of georeferenced vector, raster and tabular data which are stored in a digital environment such as a GIS geodatabase, and are easily visualized as e.g. 'birds' eye' views, as animated 3D displays, on virtual globes, or stored as GeoPDF maps in which georeferenced attribute information can be easily exchanged over the internet. Digital geomorphological information layers are increasingly accessed via web-based services distributed through remote servers. Information can be consulted - or even build using remote geoprocessing servers - by the end user. Therefore, it will not only be the geomorphologist anymore, but also the professional end user that dictates the applied use of digital geomorphological information layers.
A surge observed in H alpha and C IV

NASA Technical Reports Server (NTRS)

Schmieder, B.; Mein, P.; Vial, J. C.; Tandberg-Hanssen, E.

1982-01-01

Results are presented of simultaneous measurements of H-alpha (MSDP at Meudon) and C IV (UVSP onboard SMM) of Active Region 2701 made on October 2, 1980. Isodensity and velocity maps were obtained for both lines and these maps were superimposed. Results show a good correlation between the H-alpha and C IV velocities with a surge being observed for 10 minutes. The base of the surge was determined to be located in a bright point in C IV and H-alpha, while the escaping matter followed the same channel ('absorbing' in H-alpha, 'emitting' in C IV). It was found that the velocity along the surge was about 80 km/s in H-alpha and 100 km/s in C IV. In addition, a loop appeared in C IV during the surge. It is concluded that the vertical pressure gradient was capable of driving the surge.

Land cover mapping of North and Central America—Global Land Cover 2000

USGS Publications Warehouse

Latifovic, Rasim; Zhu, Zhi-Liang

2004-01-01

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.
Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia

NASA Astrophysics Data System (ADS)

Hutchings, Joanne; Kendall, Catherine; Shepherd, Neil; Barr, Hugh; Stone, Nicholas

2010-11-01

Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7% (sensitivity 95.0 to 100% and specificity 98.6 to 100%) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cells/lamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application.
Integrating Vegetation Classification, Mapping, and Strategic Inventory for Forest Management

Treesearch

C. K. Brewer; R. Bush; D. Berglund; J. A. Barber; S. R. Brown

2006-01-01

Many of the analyses needed to address multiple resource issues are focused on vegetation pattern and process relationships and most rely on the data models produced from vegetation classification, mapping, and/or inventory. The Northern Region Vegetation Mapping Project (R1-VMP) data models are based on these three integrally related, yet separate processes. This...
CIMOSA process classification for business process mapping in non-manufacturing firms: A case study

NASA Astrophysics Data System (ADS)

Latiffianti, Effi; Siswanto, Nurhadi; Wiratno, Stefanus Eko; Saputra, Yudha Andrian

2017-11-01

A business process mapping is one important means to enable an enterprise to effectively manage the value chain. One of widely used approaches to classify business process for mapping purpose is Computer Integrated Manufacturing System Open Architecture (CIMOSA). CIMOSA was initially designed for Computer Integrated Manufacturing (CIM) system based enterprises. This paper aims to analyze the use of CIMOSA process classification for business process mapping in the firms that do not fall within the area of CIM. Three firms of different business area that have used CIMOSA process classification were observed: an airline firm, a marketing and trading firm for oil and gas products, and an industrial estate management firm. The result of the research has shown that CIMOSA can be used in non-manufacturing firms with some adjustment. The adjustment includes addition, reduction, or modification of some processes suggested by CIMOSA process classification as evidenced by the case studies.
Selecting healthcare information systems provided by third-party vendors: a mind map beyond the manuals.

PubMed

Gortzis, Lefteris G

2010-01-01

The selection of a new healthcare information system (HIS) has always been a daunting process for clinicians, health care providers and policy makers. The objective of this study is to present the lessons learned and the main findings from several relevant case studies to support this process. Data were collected by retrospectively reviewing the summative results of three well-established systems, acquiring feedback from two E.U. projects, and conducting semi-structured interviews with a number of collaborators involved in electronic healthcare interventions. Selection issues were identified and classified into the following five categories: (i) data creation, (ii) data management, (iii) data sharing, (iv) data presentation and (v) modules management. A mind map was also structured to provide a more manageable list of issues concerning the most common electronic clinical technologies (e-CT). The vendor manual is intended as an overview of the merchandise e-CT and therefore has limited potential in supporting effectively the selection process of a new HIS. The present classification and the mind map - based on lessons learned - provide a ready-to-use toolkit for supporting the HIS selection process when healthcare organisations are unable to employ research development groups to lay the groundwork for building a new HIS from scratch.
Bosniak Classification for Complex Renal Cysts Reevaluated: A Systematic Review.

PubMed

Schoots, Ivo G; Zaccai, Keren; Hunink, Myriam G; Verhagen, Paul C M S

2017-07-01

We systematically evaluated the Bosniak classification system with malignancy rates of each Bosniak category, and assessed the effectiveness related to surgical treatment and oncologic outcome based on recurrence and/or metastasis. In a systematic review according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement and the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) criteria, we selected 39 publications for inclusion in this analysis and categorized them into 1) surgical cohorts-all cysts treated surgically and 2) radiological cohorts-cysts with surgical treatment or radiological followup. A total of 3,036 complex renal cysts were categorized into Bosniak II, IIF, III and IV. In surgical and radiological cohorts pooled estimates showed a malignancy prevalence of 0.51 (0.44, 0.58) in Bosniak III and 0.89 (0.83, 0.92) in Bosniak IV cysts, respectively. Stable Bosniak IIF cysts showed a malignancy rate of less than 1% during radiological followup (surveillance). Bosniak IIF cysts, which showed reclassification to the Bosniak III/IV category during radiological followup (12%), showed malignancy in 85%, comparable to Bosniak IV cysts. The estimated surgical number needed to treat to avoid metastatic disease of Bosniak III and IV cysts was 140 and 40, respectively. The effectiveness of the Bosniak classification system for complex renal cysts was high in categories II, IIF and IV, but low in category III, and 49% of Bosniak III cysts was overtreated because of a benign outcome. This surgical overtreatment combined with the excellent outcome for Bosniak III cysts may suggest that surveillance is a rational alternative to surgery. This will require further study to assess whether surveillance of Bosniak III cysts will prove safe. Copyright © 2017 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
[Classification of mental disorders of soldiers participating in peace missions and warfare actions].

PubMed

Gruszczyński, Wojciech; Florkowski, Antoni; Gruszczyński, Bartosz; Wysokiński, Adam

2008-01-01

Numerous media reports (press, radio, television) and several scientific publications on psychiatric disorders among Polish soldiers participating in peace missions in Iraq indicate that there is a serious threat caused by the disorders defined in the DSM-IV classification as: acute stress disorder (ASD) and post-traumatic stress disorder (PTSD). The authors analyzed psychiatric documentation and conducted their own researches, which revealed that adjustment disorders, especially with anxiety, are the main psychiatric problem among Polish soldiers in Iraq, while incidence of ASD and PTSD is very low. The aim of this publication is to present and compare mental disorders which occur during peace missions and welfare actions according to the international ICD-10 and American DSM-IV classifications. The authors paid attention to the role and significance of hitherto diagnosed impulsive disorders, which occur among the soldiers in Iraq as the intermittent explosive disorder, according to DSM-IV. The general and essential conclusions of the presented publication is that the guidelines of diagnosing mental disorders that occur during peace missions and welfare actions should be developed and introduced quickly.
Automated structural classification of lipids by machine learning.

PubMed

Taylor, Ryan; Miller, Ryan H; Miller, Ryan D; Porter, Michael; Dalgleish, James; Prince, John T

2015-03-01

Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Mapping patent classifications: portfolio and statistical analysis, and the comparison of strengths and weaknesses.

PubMed

Leydesdorff, Loet; Kogler, Dieter Franz; Yan, Bowen

2017-01-01

The Cooperative Patent Classifications (CPC) recently developed cooperatively by the European and US Patent Offices provide a new basis for mapping patents and portfolio analysis. CPC replaces International Patent Classifications (IPC) of the World Intellectual Property Organization. In this study, we update our routines previously based on IPC for CPC and use the occasion for rethinking various parameter choices. The new maps are significantly different from the previous ones, although this may not always be obvious on visual inspection. We provide nested maps online and a routine for generating portfolio overlays on the maps; a new tool is provided for "difference maps" between patent portfolios of organizations or firms. This is illustrated by comparing the portfolios of patents granted to two competing firms-Novartis and MSD-in 2016. Furthermore, the data is organized for the purpose of statistical analysis.
The Revisited Classification of GN in SLE at 10 Years: Time to Re-Evaluate Histopathologic Lesions

PubMed Central

Alpers, Charles E.; Cook, H. Terence; Ferrario, Franco; Fogo, Agnes B.; Haas, Mark; Joh, Kensuke; Noël, Laure-Hélène; Seshan, Surya V.; Bruijn, Jan A.; Bajema, Ingeborg M.

2015-01-01

Over 10 years have passed since the latest revision of the histopathologic classification of lupus nephritis. This revision was a significant improvement compared with the previous version, mainly because of clearer and more concise definitions and the elimination of mixed subclasses. Despite these improvements, there are still some difficulties in the classification for lupus nephritis, many of which are in the definitions provided. In this review, we focus on the difficulties surrounding the evaluation of classes III and IV lesions, particularly the definitions of endocapillary and extracapillary proliferation, the use of the terms endocapillary proliferation and hypercellularity, the clinical relevance of segmental and global subdivision in class IV, and the value of distinguishing lesions that indicate activity and chronicity. Vascular and tubulointerstitial lesions are also discussed. Furthermore, we give an overview of the history of the classification to provide background on the origin and development of the definitions in lupus nephritis. The issues raised in this review as well as the suggestions for improvements may assist with a revision of the lupus nephritis classification in the near future. PMID:26152271
Spectral Analysis, Synthesis, & Energy Distributions of Nearby E+A Galaxies Using SDSS-IV MaNGA

NASA Astrophysics Data System (ADS)

Weaver, Olivia A.; Anderson, Miguel Ricardo; Wally, Muhammad; James, Olivia; Falcone, Julia; Liu, Allen; Wallack, Nicole; Liu, Charles; SDSS Collaboration

2017-01-01

Utilizing data from the Mapping Nearby Galaxies at APO (MaNGA) Survey (MaNGA Product Launch-4, or MPL-4), of the latest generation of the Sloan Digital Sky Survey (SDSS-IV), we identified nine post-starburst (E+A) systems that lie within the Green Valley transition zone. We identify the E+A galaxies by their SDSS single fiber spectrum and u-r color, then confirmed their classification as post-starburst by coding/plotting methods and spectral synthesis codes (FIREFLY and PIPE3D), as well as with their Spectral Energy Distributions (SEDs) from 0.15 µm to 22 µm, using GALEX, SDSS, 2MASS, and WISE data. We produced maps of gaussian-fitted fluxes, equivalent widths, stellar velocities, metallicities and age. We also produced spectral line ratio diagrams to classify regions of stellar populations of the galaxies. We found that our sample of E+As retain their post-starburst properties across the entire galaxy, not just at their center. We detected matching a trend line in the ultraviolet and optical bands, consistent with the expected SEDs for an E+A galaxy, and also through the J, H and Ks bands, except for one object. We classified one of the nine galaxies as a luminous infrared galaxy, unusual for a post-starburst object. Our group seeks to further study stellar population properties, spectral energy distributions and quenching properties in E+A galaxies, and investigate their role in galaxy evolution as a whole. This work was supported by the Alfred P. Sloan Foundation via the SDSS-IV Faculty and Student Team (FAST) initiative, ARC Agreement #SSP483 to the CUNY College of Staten Island. This work was also supported by grants to The American Museum of Natural History, and the CUNY College of Staten Island through from National Science Foundation.
Comparison of Sub-pixel Classification Approaches for Crop-specific Mapping

EPA Science Inventory

The Moderate Resolution Imaging Spectroradiometer (MODIS) data has been increasingly used for crop mapping and other agricultural applications. Phenology-based classification approaches using the NDVI (Normalized Difference Vegetation Index) 16-day composite (250 m) data product...
Comparison of Classifier Architectures for Online Neural Spike Sorting.

PubMed

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.
Land cover mapping for development planning in Eastern and Southern Africa

NASA Astrophysics Data System (ADS)

Oduor, P.; Flores Cordova, A. I.; Wakhayanga, J. A.; Kiema, J.; Farah, H.; Mugo, R. M.; Wahome, A.; Limaye, A. S.; Irwin, D.

2016-12-01

Africa continues to experience intensification of land use, driven by competition for resources and a growing population. Land cover maps are some of the fundamental datasets required by numerous stakeholders to inform a number of development decisions. For instance, they can be integrated with other datasets to create value added products such as vulnerability impact assessment maps, and natural capital accounting products. In addition, land cover maps are used as inputs into Greenhouse Gas (GHG) inventories to inform the Agriculture, Forestry and other Land Use (AFOLU) sector. However, the processes and methodologies of creating land cover maps consistent with international and national land cover classification schemes can be challenging, especially in developing countries where skills, hardware and software resources can be limiting. To meet this need, SERVIR Eastern and Southern Africa developed methodologies and stakeholder engagement processes that led to a successful initiative in which land cover maps for 9 countries (Malawi, Rwanda, Namibia, Botswana, Lesotho, Ethiopia, Uganda, Zambia and Tanzania) were developed, using 2 major classification schemes. The first sets of maps were developed based on an internationally acceptable classification system, while the second sets of maps were based on a nationally defined classification system. The mapping process benefited from reviews from national experts and also from technical advisory groups. The maps have found diverse uses, among them the definition of the Forest Reference Levels in Zambia. In Ethiopia, the maps have been endorsed by the national mapping agency as part of national data. The data for Rwanda is being used to inform the Natural Capital Accounting process, through the WAVES program, a World Bank Initiative. This work illustrates the methodologies and stakeholder engagement processes that brought success to this land cover mapping initiative.
Global land cover mapping: a review and uncertainty analysis

USGS Publications Warehouse

Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu

2014-01-01

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.
Producing Alaska interim land cover maps from Landsat digital and ancillary data

USGS Publications Warehouse

Fitzpatrick-Lins, Katherine; Doughty, Eileen Flanagan; Shasby, Mark; Loveland, Thomas R.; Benjamin, Susan

1987-01-01

In 1985, the U.S. Geological Survey initiated a research program to produce 1:250,000-scale land cover maps of Alaska using digital Landsat multispectral scanner data and ancillary data and to evaluate the potential of establishing a statewide land cover mapping program using this approach. The geometrically corrected and resampled Landsat pixel data are registered to a Universal Transverse Mercator (UTM) projection, along with arc-second digital elevation model data used as an aid in the final computer classification. Areas summaries of the land cover classes are extracted by merging the Landsat digital classification files with the U.S. Bureau of Land Management's Public Land Survey digital file. Registration of the digital land cover data is verified and control points are identified so that a laser plotter can products screened film separate for printing the classification data at map scale directly from the digital file. The final land cover classification is retained both as a color map at 1:250,000 scale registered to the U.S. Geological Survey base map, with area summaries by township and range on the reverse, and as a digital file where it may be used as a category in a geographic information system.
Cross-cultural and comparative epidemiology of insomnia: the Diagnostic and statistical manual (DSM), International classification of diseases (ICD) and International classification of sleep disorders (ICSD).

PubMed

Chung, Ka-Fai; Yeung, Wing-Fai; Ho, Fiona Yan-Yee; Yung, Kam-Ping; Yu, Yee-Man; Kwok, Chi-Wa

2015-04-01

To compare the prevalence of insomnia according to symptoms, quantitative criteria, and Diagnostic and Statistical Manual of Mental Disorders, 4th and 5th Edition (DSM-IV and DSM-5), International Classification of Diseases, 10th Revision (ICD-10), and International Classification of Sleep Disorders, 2nd Edition (ICSD-2), and to compare the prevalence of insomnia disorder between Hong Kong and the United States by adopting a similar methodology used by the America Insomnia Survey (AIS). Population-based epidemiological survey respondents (n = 2011) completed the Brief Insomnia Questionnaire (BIQ), a validated scale generating DSM-IV, DSM-5, ICD-10, and ICSD-2 insomnia disorder. The weighted prevalence of difficulty falling asleep, difficulty staying asleep, waking up too early, and non-restorative sleep that occurred ≥3 days per week was 14.0%, 28.3%, 32.1%, and 39.9%, respectively. When quantitative criteria were included, the prevalence dropped the most from 39.9% to 8.4% for non-restorative sleep, and the least from 14.0% to 12.9% for difficulty falling asleep. The weighted prevalence of DSM-IV, ICD-10, ICSD-2, and any of the three insomnia disorders was 22.1%, 4.7%, 15.1%, and 22.1%, respectively; for DSM-5 insomnia disorder, it was 10.8%. Compared with 22.1%, 3.9%, and 14.7% for DSM-IV, ICD-10, and ICSD-2 in the AIS, cross-cultural difference in the prevalence of insomnia disorder is less than what is expected. The prevalence is reduced by half from DSM-IV to DSM-5. ICD-10 insomnia disorder has the lowest prevalence, perhaps because excessive concern and preoccupation, one of its diagnostic criteria, is not always present in people with insomnia. Copyright © 2014 Elsevier B.V. All rights reserved.
Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.
Wildlife management by habitat units: A preliminary plan of action

NASA Technical Reports Server (NTRS)

Frentress, C. D.; Frye, R. G.

1975-01-01

Procedures for yielding vegetation type maps were developed using LANDSAT data and a computer assisted classification analysis (LARSYS) to assist in managing populations of wildlife species by defined area units. Ground cover in Travis County, Texas was classified on two occasions using a modified version of the unsupervised approach to classification. The first classification produced a total of 17 classes. Examination revealed that further grouping was justified. A second analysis produced 10 classes which were displayed on printouts which were later color-coded. The final classification was 82 percent accurate. While the classification map appeared to satisfactorily depict the existing vegetation, two classes were determined to contain significant error. The major sources of error could have been eliminated by stratifying cluster sites more closely among previously mapped soil associations that are identified with particular plant associations and by precisely defining class nomenclature using established criteria early in the analysis.
An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

Kohonen's self-organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes, the quantization and dimensionality reduction of date. A short description of its history and its biological context is given. We show that the inherent classification properties of the feature map make it a suitable candidate for solving the classification task in power system areas like load forecasting, fault diagnosis and security assessment.

Korean coastal water depth/sediment and land cover mapping (1:25,000) by computer analysis of LANDSAT imagery

NASA Technical Reports Server (NTRS)

Park, K. Y.; Miller, L. D.

1978-01-01

Computer analysis was applied to single date LANDSAT MSS imagery of a sample coastal area near Seoul, Korea equivalent to a 1:50,000 topographic map. Supervised image processing yielded a test classification map from this sample image containing 12 classes: 5 water depth/sediment classes, 2 shoreline/tidal classes, and 5 coastal land cover classes at a scale of 1:25,000 and with a training set accuracy of 76%. Unsupervised image classification was applied to a subportion of the site analyzed and produced classification maps comparable in results in a spatial sense. The results of this test indicated that it is feasible to produce such quantitative maps for detailed study of dynamic coastal processes given a LANDSAT image data base at sufficiently frequent time intervals.
Correlation of the New York Heart Association Classification and the 6-Minute Walk Distance: A Systematic Review.

PubMed

Yap, Jonathan; Lim, Fang Yi; Gao, Fei; Teo, Ling Li; Lam, Carolyn Su Ping; Yeo, Khung Keong

2015-10-01

Functional status assessment is the cornerstone of heart failure management and trials. The New York Heart Association (NYHA) classification and 6-minute walk distance (6MWD) are commonly used tools; however, the correlation between them is not well understood. We hypothesised that the relationship between the NYHA classification and 6MWD might vary across studies. A systematic literature search was performed to identify all studies reporting both NYHA class and 6MWD. Two reviewers independently assessed study eligibility and extracted data. Thirty-seven studies involving 5678 patients were included. There was significant heterogeneity across studies in 6MWD within all NYHA classes: I (n = 16, Q = 934.2; P < 0.001), II (n = 25, Q = 1658.3; P < 0.001), III (n = 30, Q = 1020.1; P < 0.001), and IV (n = 6, Q = 335.5; P < 0.001). There was no significant difference in average 6MWD between NYHA I and II (420 m vs 393 m; P = 0.416). There was a significant difference in average 6MWD between NYHA II and III (393 m vs 321 m; P = 0.014) and III and IV (321 m vs 224 m; P = 0.027). This remained significant after adjusting for region of study, age, and sex. Although there is an inverse correlation between NYHA II-IV and 6MWD, there is significant heterogeneity across studies in 6MWD within each NYHA class and overlap in 6MWD between NYHA I and II. The NYHA classification performs well in more symptomatic patients (NYHA III/IV) but less so in asymptomatic/mildly symptomatic patients (NYHA I/II). Nonetheless, the NYHA classification is an easily applied first-line tool in everyday clinical practice, but its potential subjectivity should be considered when performing comparisons across studies. © 2015 Wiley Periodicals, Inc.
Identification of phenological stages and vegetative types for land use classification

NASA Technical Reports Server (NTRS)

Mckendrick, J. D. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Classification of digital data for mapping Alaskan vegetation has been compared to ground truth data and found to have accuracies as high as 90%. These classifications are broad scale types as are currently being used on the Major Ecosystems of Alaska map prepared by the Joint Federal-State Land Use Planning Commission for Alaska. Cost estimates for several options using the ERTS-1 digital data to map the Alaskan land mass at the 1:250,000 scale ranged between $2.17 to $1.49 per square mile.
Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

NASA Astrophysics Data System (ADS)

Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello

2013-01-01

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.
Nursing interventions for rehabilitation in Parkinson's disease: cross mapping of terms

PubMed Central

Tosin, Michelle Hyczy de Siqueira; Campos, Débora Moraes; de Andrade, Leonardo Tadeu; de Oliveira, Beatriz Guitton Renaud Baptista; Santana, Rosimere Ferreira

2016-01-01

ABSTRACT Objective: to perform a cross-term mapping of nursing language in the patient record with the Nursing Interventions Classification system, in rehabilitation patients with Parkinson's disease. Method: a documentary research study to perform cross mapping. A probabilistic, simple random sample composed of 67 records of patients with Parkinson's disease who participated in a rehabilitation program, between March of 2009 and April of 2013. The research was conducted in three stages, in which the nursing terms were mapped to natural language and crossed with the Nursing Interventions Classification. Results: a total of 1,077 standard interventions that, after crossing with the taxonomy and refinement performed by the experts, resulted in 32 interventions equivalent to the Nursing Interventions Classification (NIC) system. The NICs, "Education: The process of the disease.", "Contract with the patient", and "Facilitation of Learning" were present in 100% of the records. For these interventions, 40 activities were described, representing 13 activities by intervention. Conclusion: the cross mapping allowed for the identification of corresponding terms with the nursing interventions used every day in rehabilitation nursing, and compared them to the Nursing Interventions Classification. PMID:27508903
Simulation of seagrass bed mapping by satellite images based on the radiative transfer model

NASA Astrophysics Data System (ADS)

Sagawa, Tatsuyuki; Komatsu, Teruhisa

2015-06-01

Seagrass and seaweed beds play important roles in coastal marine ecosystems. They are food sources and habitats for many marine organisms, and influence the physical, chemical, and biological environment. They are sensitive to human impacts such as reclamation and pollution. Therefore, their management and preservation are necessary for a healthy coastal environment. Satellite remote sensing is a useful tool for mapping and monitoring seagrass beds. The efficiency of seagrass mapping, seagrass bed classification in particular, has been evaluated by mapping accuracy using an error matrix. However, mapping accuracies are influenced by coastal environments such as seawater transparency, bathymetry, and substrate type. Coastal management requires sufficient accuracy and an understanding of mapping limitations for monitoring coastal habitats including seagrass beds. Previous studies are mainly based on case studies in specific regions and seasons. Extensive data are required to generalise assessments of classification accuracy from case studies, which has proven difficult. This study aims to build a simulator based on a radiative transfer model to produce modelled satellite images and assess the visual detectability of seagrass beds under different transparencies and seagrass coverages, as well as to examine mapping limitations and classification accuracy. Our simulations led to the development of a model of water transparency and the mapping of depth limits and indicated the possibility for seagrass density mapping under certain ideal conditions. The results show that modelling satellite images is useful in evaluating the accuracy of classification and that establishing seagrass bed monitoring by remote sensing is a reliable tool.
Terrain classification and land hazard mapping in Kalsi-Chakrata area (Garhwal Himalaya), India

NASA Astrophysics Data System (ADS)

Choubey, Vishnu D.; Litoria, Pradeep K.

Terrain classification and land system mapping of a part of the Garhwal Himalaya (India) have been used to provide a base map for land hazard evaluation, with special reference to landslides and other mass movements. The study was based on MSS images, aerial photographs and 1:50,000 scale maps, followed by detailed field-work. The area is composed of two groups of rocks: well exposed sedimentary Precambrian formations in the Himalayan Main Boundary Thrust Belt and the Tertiary molasse deposits of the Siwaliks. Major tectonic boundaries were taken as the natural boundaries of land systems. A physiographic terrain classification included slope category, forest cover, occurrence of landslides, seismicity and tectonic activity in the area.
Study of USGS/NASA land use classification system. [computer analysis from LANDSAT data

NASA Technical Reports Server (NTRS)

Spann, G. W.

1975-01-01

The results of a computer mapping project using LANDSAT data and the USGS/NASA land use classification system are summarized. During the computer mapping portion of the project, accuracies of 67 percent to 79 percent were achieved using Level II of the classification system and a 4,000 acre test site centered on Douglasville, Georgia. Analysis of response to a questionaire circulated to actual and potential LANDSAT data users reveals several important findings: (1) there is a substantial desire for additional information related to LANDSAT capabilities; (2) a majority of the respondents feel computer mapping from LANDSAT data could aid present or future projects; and (3) the costs of computer mapping are substantially less than those of other methods.
A Proposal for a Dimensional Classification System Based on the Shared Features of the "DSM-IV" Anxiety and Mood Disorders: Implications for Assessment and Treatment

ERIC Educational Resources Information Center

Brown, Timothy A.; Barlow, David H.

2009-01-01

A wealth of evidence attests to the extensive current and lifetime diagnostic comorbidity of the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed., "DSM-IV") anxiety and mood disorders. Research has shown that the considerable cross-sectional covariation of "DSM-IV" emotional disorders is accounted for by common higher order…
VLBI Analysis with the Multi-Technique Software GEOSAT

NASA Technical Reports Server (NTRS)

Kierulf, Halfdan Pascal; Andersen, Per-Helge; Boeckmann, Sarah; Kristiansen, Oddgeir

2010-01-01

GEOSAT is a multi-technique geodetic analysis software developed at Forsvarets Forsknings Institutt (Norwegian defense research establishment). The Norwegian Mapping Authority has now installed the software and has, together with Forsvarets Forsknings Institutt, adapted the software to deliver datum-free normal equation systems in SINEX format. The goal is to be accepted as an IVS Associate Analysis Center and to provide contributions to the IVS EOP combination on a routine basis. GEOSAT is based on an upper diagonal factorized Kalman filter which allows estimation of time variable parameters like the troposphere and clocks as stochastic parameters. The tropospheric delays in various directions are mapped to tropospheric zenith delay using ray-tracing. Meteorological data from ECMWF with a resolution of six hours is used to perform the ray-tracing which depends both on elevation and azimuth. Other models are following the IERS and IVS conventions. The Norwegian Mapping Authority has submitted test SINEX files produced with GEOSAT to IVS. The results have been compared with the existing IVS combined products. In this paper the outcome of these comparisons is presented.
Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.
The use of old and recent DSM definitions of premature ejaculation in observational studies: a contribution to the present debate for a new classification of PE in the DSM-V.

PubMed

Waldinger, Marcel D; Schweitzer, Dave H

2008-05-01

The DSM-III definition of premature ejaculation (PE) contains the criterion "control" but not that of "ejaculation time." In contrast, the Diagnostic and Statistical Manual of Mental Disorders (4th edition, Text Revision) (DSM-IV-TR) contains the criterion "short ejaculation time," while it lacks "control." To review the adequacy and consequent use of all criteria of the DSM-IV-TR definition in previously published PE Internet surveys. Reviewing all published cohort studies on PE from 2004 to 2007. MEDLINE and EMBASE computer bibliographies were used. Definitions of DSM-III, DSM-IV-TR, and International Classification of Diseases. Five papers, of which three are original studies, reported inclusion of men with PE according to DSM-IV-TR definition but omitted to apply the required "short ejaculation time" criterion. These studies, which have defined PE according to subjective criteria such as control, actually referred to the DSM-III definition. Using DSM-III-like definitions in three different studies revealed a highly variable prevalence of PE (32.5%, 27.6%, and 13.0%). In contrast, based on studies using a 1-minute cutoff point, being the time that is required to call ejaculation time "short" or using the criterion "persistent occurrence," PE revealed to be far less prevalent (5-6%). Unacceptable discrepancies of PE definitions according to DSM-III (abandoned but still used) and DSM-IV-TR argue strongly in favor of a multidimensional new classification of PE for the DSM-V.
Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

NASA Astrophysics Data System (ADS)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty. The results indicate that uncertainty estimates provide a valuable addition to traditional accuracy assessments and helps the user to allocate error in crop maps.
3D tissue engineered micro-tumors for optical-based therapeutic screening platform

NASA Astrophysics Data System (ADS)

Spano, Joseph L.; Schmitt, Trevor J.; Bailey, Ryan C.; Hannon, Timothy S.; Elmajdob, Mohamed; Mason, Eric M.; Ye, Guochang; Das, Soumen; Seal, Sudipta; Fenn, Michael B.

2016-03-01

Melanoma is an underserved area of cancer research, with little focus on studying the effects of tumor extracellular matrix (ECM) properties on melanoma tumor progression, metastasis, and treatment efficacy. We've developed a Raman spectral mapping-based in-vitro screening platform that allows for nondestructive in-situ, multi-time point assessment of a novel potential nanotherapeutic adjuvant, nanoceria (cerium oxide nanoparticles), for treating melanoma. We've focused primarily on understanding melanoma tumor ECM composition and how it influences cell morphology and ICC markers. Furthermore, we aim to correlate this with studies on nanotherapeutic efficacy to coincide with the goal of predicting and preventing metastasis based on ECM composition. We've compiled a Raman spectral database for substrates containing varying compositions of fibronectin, elastin, laminin, and collagens type I and IV. Furthermore, we've developed a machine learning-based semi-quantitative analysis platform utilizing dimensionality reduction with subsequent pixel classification and semi-quantitation of ECM composition using Direct Classical Least Squares for classification and estimation of the reorganization of these components by taking 2D maps using Raman spectroscopy. Gaining an understanding of how tissue properties influence ECM organization has laid the foundation for future work utilizing Raman spectroscopy to assess therapeutic efficacy and matrix reorganization imparted by nanoceria. Specifically, this will allow us to better understand the role of HIF1a in matrix reorganization of the tumor microenvironment. By studying the relationship between substrate modulus and nanoceria's ability to inhibit an ECM that is conducive to tumor formation, we endeavor to show that nanoceria may prevent or even revert tumor conducive microenvironments.
Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

PubMed Central

Bricher, Phillippa K.; Lucieer, Arko; Shaw, Justine; Terauds, Aleks; Bergstrom, Dana M.

2013-01-01

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments. PMID:23940805
Classification of Liss IV Imagery Using Decision Tree Methods

NASA Astrophysics Data System (ADS)

Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

2016-06-01

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.
Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

NASA Astrophysics Data System (ADS)

Jiao, Xianfeng; Kovacs, John M.; Shang, Jiali; McNairn, Heather; Walters, Dan; Ma, Baoluo; Geng, Xiaoyuan

2014-10-01

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude-Pottier and Freeman-Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude-Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman-Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.
Spectrophotometric Properties of E+A Galaxies in SDSS-IV MaNGA

NASA Astrophysics Data System (ADS)

Marinelli, Mariarosa; Dudley, Raymond; Edwards, Kay; Gonzalez, Andrea; Johnson, Amalya; Kerrison, Nicole; Melchert, Nancy; Ojanen, Winonah; Weaver, Olivia; Liu, Charles; SDSS-IV MaNGA

2018-01-01

Quenched post-starburst galaxies, or E+A galaxies, represent a unique and informative phase in the evolution of galaxies. We used a qualitative rubric-based methodology, informed by the literature, to manually select galaxies from the SDSS-IV IFU survey Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) using the single-fiber spectra from the Sloan Digital Sky Survey Data Release 8. Of the 2,812 galaxies observed so far in MaNGA, we found 39 galaxies meeting our criteria for E+A classification. Spectral energy distributions of these 39 galaxies from the far-UV to the mid-infrared demonstrate a heterogeneity in our sample emerging in the infrared, indicating many distinct paths to visually similar optical spectra. We used SDSS-IV MaNGA Pipe3D data products to analyze stellar population ages, and found that 34 galaxies exhibited stellar populations that were older at 1 effective radius than at the center of the galaxy. Given that our sample was manually chosen based on E+A markers in the single-fiber spectra aimed at the center of each galaxy, our E+A galaxies may have only experienced their significant starbursts in the central region, with a disk of quenched or quenching material further outward. This work was supported by grants AST-1460860 from the National Science Foundation and SDSS FAST/SSP-483 from the Alfred P. Sloan Foundation to the CUNY College of Staten Island.
A new computer approach to mixed feature classification for forestry application

NASA Technical Reports Server (NTRS)

Kan, E. P.

1976-01-01

A computer approach for mapping mixed forest features (i.e., types, classes) from computer classification maps is discussed. Mixed features such as mixed softwood/hardwood stands are treated as admixtures of softwood and hardwood areas. Large-area mixed features are identified and small-area features neglected when the nominal size of a mixed feature can be specified. The computer program merges small isolated areas into surrounding areas by the iterative manipulation of the postprocessing algorithm that eliminates small connected sets. For a forestry application, computer-classified LANDSAT multispectral scanner data of the Sam Houston National Forest were used to demonstrate the proposed approach. The technique was successful in cleaning the salt-and-pepper appearance of multiclass classification maps and in mapping admixtures of softwood areas and hardwood areas. However, the computer-mapped mixed areas matched very poorly with the ground truth because of inadequate resolution and inappropriate definition of mixed features.
Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andonov, Rumen; Djidjev, Hristo Nikolov; Klau, Gunnar W.

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifiesmore » up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.« less

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

DOE PAGES

Andonov, Rumen; Djidjev, Hristo Nikolov; Klau, Gunnar W.; ...

2015-10-09

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifiesmore » up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.« less
Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report. [Taylor's Island, Maryland

NASA Technical Reports Server (NTRS)

Mulligan, P. J.; Gervin, J. C.; Lu, Y. C.

1985-01-01

An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.
Application of satellite data and LARS's data processing techniques to mapping vegetation of the Dismal Swamp. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Messmore, J. A.

1976-01-01

The feasibility of using digital satellite imagery and automatic data processing techniques as a means of mapping swamp forest vegetation was considered, using multispectral scanner data acquired by the LANDSAT-1 satellite. The site for this investigation was the Dismal Swamp, a 210,000 acre swamp forest located south of Suffolk, Va. on the Virginia-North Carolina border. Two basic classification strategies were employed. The initial classification utilized unsupervised techniques which produced a map of the swamp indicating the distribution of thirteen forest spectral classes. These classes were later combined into three informational categories: Atlantic white cedar (Chamaecyparis thyoides), Loblolly pine (Pinus taeda), and deciduous forest. The subsequent classification employed supervised techniques which mapped Atlantic white cedar, Loblolly pine, deciduous forest, water and agriculture within the study site. A classification accuracy of 82.5% was produced by unsupervised techniques compared with 89% accuracy using supervised techniques.
Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

NASA Astrophysics Data System (ADS)

Mücher, C. A.; Roupioz, L.; Kramer, H.; Bogers, M. M. B.; Jongman, R. H. G.; Lucas, R. M.; Kosmidou, V. E.; Petrou, Z.; Manakos, I.; Padoa-Schioppa, E.; Adamo, M.; Blonda, P.

2015-05-01

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.
Five Systems of Psychiatric Classification for Preschool Children: Do Differences in Validity, Usefulness and Reliability Make for Competitive or Complimentary Constellations?

ERIC Educational Resources Information Center

Postert, Christian; Averbeck-Holocher, Marlies; Beyer, Thomas; Muller, Jorg; Furniss, Tilman

2009-01-01

"DSM-IV" and "ICD-10" have limitations in the diagnostic classification of psychiatric disorders at preschool age (0-5 years). The publication of the "Diagnostic Classification 0-3 (DC:0-3)" in 1994, its basically revised second edition ("DC:0-3R") in 2005 and the "Research Diagnostic Criteria-Preschool Age (RDC-PA)" in 2004 have provided several…
Ecological Land Classification: Applications to Identify the Productive Potential of Southern Forests

Treesearch

Dennis L. Mengel; D. Thompson Tew; [Editors

1991-01-01

Eighteen papers representing four categories-Regional Overviews; Classification System Development; Classification System Interpretation; Mapping/GIS Applications in Classification Systems-present the state of the art in forest-land classification and evaluation in the South. In addition, nine poster papers are presented.
Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2010-01-01

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.
Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.
Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

USGS Publications Warehouse

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.
Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

NASA Technical Reports Server (NTRS)

Rignot, E.; Chellappa, R.

1993-01-01

We present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization SAR intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computer-simulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.
Diagnosis and Classification in Autism.

ERIC Educational Resources Information Center

Waterhouse, Lynn; And Others

1996-01-01

This study compared four systems for diagnosis of autism (Diagnostic and Statistical Manuals of Mental Disorders III, III-R, and IV, and the International Classification of Disabilities-10) with 2 empirically derived taxa and 3 social subgroups (aloof, passive, and active but odd) in 194 preschool children with social impairment. Findings support…
Eating Disorder Diagnoses: Empirical Approaches to Classification

ERIC Educational Resources Information Center

Wonderlich, Stephen A.; Joiner, Thomas E., Jr.; Keel, Pamela K.; Williamson, Donald A.; Crosby, Ross D.

2007-01-01

Decisions about the classification of eating disorders have significant scientific and clinical implications. The eating disorder diagnoses in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) reflect the collective wisdom of experts in the field but are frequently not supported in…
Study of USGS/NASA land use classification system. [compatibility of land use classification system with computer processing techniques employed for land use mapping from ERTS data

NASA Technical Reports Server (NTRS)

Spann, G. W.; Faust, N. L.

1974-01-01

It is known from several previous investigations that many categories of land-use can be mapped via computer processing of Earth Resources Technology Satellite data. The results are presented of one such experiment using the USGS/NASA land-use classification system. Douglas County, Georgia, was chosen as the test site for this project. It was chosen primarily because of its recent rapid growth and future growth potential. Results of the investigation indicate an overall land-use mapping accuracy of 67% with higher accuracies in rural areas and lower accuracies in urban areas. It is estimated, however, that 95% of the State of Georgia could be mapped by these techniques with an accuracy of 80% to 90%.
Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions

PubMed Central

Razali, Sheriza Mohd; Marin, Arnaldo; Nuruddin, Ahmad Ainuddin; Shafri, Helmi Zulhaidi Mohd; Hamid, Hazandy Abdul

2014-01-01

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions. PMID:24811079
Evaluating the Effectiveness of Flood Control Strategies in Contrasting Urban Watersheds and Implications for Houston's Future Flood Vulnerability

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2016-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.
Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions.

PubMed

Razali, Sheriza Mohd; Marin, Arnaldo; Nuruddin, Ahmad Ainuddin; Shafri, Helmi Zulhaidi Mohd; Hamid, Hazandy Abdul

2014-05-07

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.
Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

PubMed Central

Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results. PMID:28467468
Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

PubMed

Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.
Feasibility of Multispectral Airborne Laser Scanning for Land Cover Classification, Road Mapping and Map Updating

NASA Astrophysics Data System (ADS)

Matikainen, L.; Karila, K.; Hyyppä, J.; Puttonen, E.; Litkey, P.; Ahokas, E.

2017-10-01

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015-2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.
Diagnostic classification of macular ganglion cell and retinal nerve fiber layer analysis: differentiation of false-positives from glaucoma.

PubMed

Kim, Ko Eun; Jeoung, Jin Wook; Park, Ki Ho; Kim, Dong Myung; Kim, Seok Hwan

2015-03-01

To investigate the rate and associated factors of false-positive diagnostic classification of ganglion cell analysis (GCA) and retinal nerve fiber layer (RNFL) maps, and characteristic false-positive patterns on optical coherence tomography (OCT) deviation maps. Prospective, cross-sectional study. A total of 104 healthy eyes of 104 normal participants. All participants underwent peripapillary and macular spectral-domain (Cirrus-HD, Carl Zeiss Meditec Inc, Dublin, CA) OCT scans. False-positive diagnostic classification was defined as yellow or red color-coded areas for GCA and RNFL maps. Univariate and multivariate logistic regression analyses were used to determine associated factors. Eyes with abnormal OCT deviation maps were categorized on the basis of the shape and location of abnormal color-coded area. Differences in clinical characteristics among the subgroups were compared. (1) The rate and associated factors of false-positive OCT maps; (2) patterns of false-positive, color-coded areas on the GCA deviation map and associated clinical characteristics. Of the 104 healthy eyes, 42 (40.4%) and 32 (30.8%) showed abnormal diagnostic classifications on any of the GCA and RNFL maps, respectively. Multivariate analysis revealed that false-positive GCA diagnostic classification was associated with longer axial length and larger fovea-disc angle, whereas longer axial length and smaller disc area were associated with abnormal RNFL maps. Eyes with abnormal GCA deviation map were categorized as group A (donut-shaped round area around the inner annulus), group B (island-like isolated area), and group C (diffuse, circular area with an irregular inner margin in either). The axial length showed a significant increasing trend from group A to C (P=0.001), and likewise, the refractive error was more myopic in group C than in groups A (P=0.015) and B (P=0.014). Group C had thinner average ganglion cell-inner plexiform layer thickness compared with other groups (group A=B>C, P=0.004). Abnormal OCT diagnostic classification should be interpreted with caution, especially in eyes with long axial lengths, large fovea-disc angles, and small optic discs. Our findings suggest that the characteristic patterns of OCT deviation map can provide useful clues to distinguish glaucomatous changes from false-positive findings. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Mapping ecological states in a complex environment

NASA Astrophysics Data System (ADS)

Steele, C. M.; Bestelmeyer, B.; Burkett, L. M.; Ayers, E.; Romig, K.; Slaughter, A.

2013-12-01

The vegetation of northern Chihuahuan Desert rangelands is sparse, heterogeneous and for most of the year, consists of a large proportion of non-photosynthetic material. The soils in this area are spectrally bright and variable in their reflectance properties. Both factors provide challenges to the application of remote sensing for estimating canopy variables (e.g., leaf area index, biomass, percentage canopy cover, primary production). Additionally, with reference to current paradigms of rangeland health assessment, remotely-sensed estimates of canopy variables have limited practical use to the rangeland manager if they are not placed in the context of ecological site and ecological state. To address these challenges, we created a multifactor classification system based on the USDA-NRCS ecological site schema and associated state-and-transition models to map ecological states on desert rangelands in southern New Mexico. Applying this system using per-pixel image processing techniques and multispectral, remotely sensed imagery raised other challenges. Per-pixel image classification relies upon the spectral information in each pixel alone, there is no reference to the spatial context of the pixel and its relationship with its neighbors. Ecological state classes may have direct relevance to managers but the non-unique spectral properties of different ecological state classes in our study area means that per-pixel classification of multispectral data performs poorly in discriminating between different ecological states. We found that image interpreters who are familiar with the landscape and its associated ecological site descriptions perform better than per-pixel classification techniques in assigning ecological states. However, two important issues affect manual classification methods: subjectivity of interpretation and reproducibility of results. An alternative to per-pixel classification and manual interpretation is object-based image analysis. Object-based image analysis provides a platform for classification that more closely resembles human recognition of objects within a remotely sensed image. The analysis presented here compares multiple thematic maps created for test locations on the USDA-ARS Jornada Experimental Range ranch. Three study sites in different pastures, each 300 ha in size, were selected for comparison on the basis of their ecological site type (';Clayey', ';Sandy' and a combination of both) and the degree of complexity of vegetation cover. Thematic maps were produced for each study site using (i) manual interpretation of digital aerial photography (by five independent interpreters); (ii) object-oriented, decision-tree classification of fine and moderate spatial resolution imagery (Quickbird; Landsat Thematic Mapper) and (iii) ground survey. To identify areas of uncertainty, we compared agreement in location, areal extent and class assignation between 5 independently produced, manually-digitized ecological state maps and with the map created from ground survey. Location, areal extent and class assignation of the map produced by object-oriented classification was also assessed with reference to the ground survey map.
Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

2018-04-01

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be used to identify optimal datasets for vegetation community mapping.
Mapping the Content of the Patient Reported Outcomes Measurement Information System (PROMIS®) Using the International Classification of Functioning, Health and Disability

PubMed Central

Tucker, Carole A; Escorpizo, Reuben; Cieza, Alarcos; Lai, Jin Shei; Stucki, Gerold; Ustun, T. Bedirhan; Kostanjsek, Nenad; Cella, David; Forrest, Christopher B.

2014-01-01

Background The Patient Reported Outcomes Measurement Information System (PROMIS®) is a U.S. National Institutes of Health initiative that has produced self-reported item banks for physical, mental, and social health. Objective To describe the content of PROMIS at the item level using the World Health Organization’s International Classification of Functioning, Disability and Health (ICF). Methods All PROMIS adult items (publicly available as of 2012) were assigned to relevant ICF concepts. The content of the PROMIS adult item banks were then described using the mapped ICF code descriptors. Results The 1006 items in the PROMIS instruments could all be mapped to ICF concepts at the second level of classification, with the exception of 3 items of global or general health that mapped across the first-level classification of ICF activity and participation component (d categories). Individual PROMIS item banks mapped from 1 to 5 separate ICF codes indicating one-to-one, one-to-many and many-to-one mappings between PROMIS item banks and ICF second level classification codes. PROMIS supports measurement of the majority of major concepts in the ICF Body Functions (b) and Activity & Participation (d) components using PROMIS item banks or subsets of PROMIS items that could, with care, be used to develop customized instruments. Given the focus of PROMIS is on measurement of person health outcomes, concepts in body structures (s) and some body functions (b), as well as many ICF environmental factor have minimal coverage in PROMIS. Discussion The PROMIS-ICF mapped items provide a basis for users to evaluate the ICF related content of specific PROMIS instruments, and to select PROMIS instruments in ICF based measurement applications. PMID:24760532
Comparison of citrus orchard inventory using LISS-III and LISS-IV data

NASA Astrophysics Data System (ADS)

Singh, Niti; Chaudhari, K. N.; Manjunath, K. R.

2016-04-01

In India, in terms of area under cultivation, citrus is the third most cultivated fruit crop after Banana and Mango. Among citrus group, lime is one of the most important horticultural crops in India as the demand for its consumption is very high. Hence, preparing citrus crop inventories using remote sensing techniques would help in maintaining a record of its area and production statistics. This study shows how accurately citrus orchard can be classified using both IRS Resourcesat-2 LISS-III and LISS-IV data and depicts the optimum bio-widow for procuring satellite data to achieve high classification accuracy required for maintaining inventory of crop. Findings of the study show classification accuracy increased from 55% (using LISS-III) to 77% (using LISS-IV). Also, according to classified outputs and NDVI values obtained, April and May months were identified as optimum bio-window for citrus crop identification.
Efficiency of the spectral-spatial classification of hyperspectral imaging data

NASA Astrophysics Data System (ADS)

Borzov, S. M.; Potaturkin, O. I.

2017-01-01

The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.
Intraosseous Hydroxocobalamin versus Intravenous Hydroxocobalamin Compared to Intraosseous Whole Blood or No Treatment for Hemorrhagic Shock in a Swine Model

DTIC Science & Technology

2016-05-02

MAP significantly improved over time in both treated groups but not in the control group. By 60 minutes, mean HR was 116 and 135 bpm and MAP was 60...and 65 mm Hg for IV and IO HOC groups (not significantly different) whereas non-treated animals displayed a mean HR of 157 bpm and MAP of 43 mm Hg...group. By 60 minutes, mean HR was 116(9.9) and 100(11.2) bpm and MAP was 57(3.9) and 62(4.4) mm Hg for IV HOC and WB groups (not significantly
SENTINEL-1 and SENTINEL-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

NASA Astrophysics Data System (ADS)

Kaplan, G.; Avdan, U.

2018-04-01

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90 % in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.
Genetic organization of the unc-22 IV gene and the adjacent region in Caenorhabditis elegans.

PubMed

Rogalski, T M; Baillie, D L

1985-01-01

The genetic organization of the region immediately adjacent to the unc-22 IV gene in Caenorhabditis elegans has been studied. We have identified twenty essential genes in this interval of approximately 1.5-map units on Linkage Group IV. The mutations that define these genes were positioned by recombination mapping and complementation with several deficiencies. With few exceptions, the positions obtained by these two methods agreed. Eight of the twenty essential genes identified are represented by more than one allele. Three possible internal deletions of the unc-22 gene have been located by intra-genic mapping. In addition, the right end point of a deficiency or an inversion affecting the adjacent genes let-56 and unc-22 has been positioned inside the unc-22 gene.
Using EUNIS habitat classification for benthic mapping in European seas: present concerns and future needs.

PubMed

Galparsoro, Ibon; Connor, David W; Borja, Angel; Aish, Annabelle; Amorim, Patricia; Bajjouk, Touria; Chambers, Caroline; Coggan, Roger; Dirberg, Guillaume; Ellwood, Helen; Evans, Douglas; Goodin, Kathleen L; Grehan, Anthony; Haldin, Jannica; Howell, Kerry; Jenkins, Chris; Michez, Noëmie; Mo, Giulia; Buhl-Mortensen, Pål; Pearce, Bryony; Populus, Jacques; Salomidi, Maria; Sánchez, Francisco; Serrano, Alberto; Shumchenia, Emily; Tempera, Fernando; Vasquez, Mickaël

2012-12-01

The EUNIS (European Union Nature Information System) habitat classification system aims to provide a common European reference set of habitat types within a hierarchical classification, and to cover all terrestrial, freshwater and marine habitats of Europe. The classification facilitates reporting of habitat data in a comparable manner, for use in nature conservation (e.g. inventories, monitoring and assessments), habitat mapping and environmental management. For the marine environment the importance of a univocal habitat classification system is confirmed by the fact that many European initiatives, aimed at marine mapping, assessment and reporting, are increasingly using EUNIS habitat categories and respective codes. For this reason substantial efforts have been made to include information on marine benthic habitats from different regions, aiming to provide a comprehensive geographical coverage of European seas. However, there still remain many concerns on its applicability as only a small fraction of Europe's seas are fully mapped and increasing knowledge and application raise further issues to be resolved. This paper presents an overview of the main discussion and conclusions of a workshop, organised by the MeshAtlantic project, focusing upon the experience in using the EUNIS habitats classification across different countries and seas, together with case studies. The aims of the meeting were to: (i) bring together scientists with experience in the use of the EUNIS marine classification and representatives from the European Environment Agency (EEA); (ii) agree on enhancements to EUNIS that ensure an improved representation of the European marine habitats; and (iii) establish practices that make marine habitat maps produced by scientists more consistent with the needs of managers and decision-makers. During the workshop challenges for the future development of EUNIS were identified, which have been classified into five categories: (1) structure and hierarchy; (2) biology; (3) terminology; (4) mapping; and (5) future development. The workshop ended with a declaration from the attendees, with recommendations to the EEA and European Topic Centre on Biological Diversity, to take into account the outputs of the workshop, which identify weaknesses in the current classification and include proposals for its modification, and to devise a process to further develop the marine component of the EUNIS habitat classification. Copyright © 2012 Elsevier Ltd. All rights reserved.
Predictive Validity of DSM-IV and ICD-10 Criteria for ADHD and Hyperkinetic Disorder

ERIC Educational Resources Information Center

Lee, Soyoung I.; Schachar, Russell J.; Chen, Shirley X.; Ornstein, Tisha J.; Charach, Alice; Barr, Cathy; Ickowicz, Abel

2008-01-01

Background: The goal of this study was to compare the predictive validity of the two main diagnostic schemata for childhood hyperactivity--attention-deficit hyperactivity disorder (ADHD; "Diagnostic and Statistical Manual"-IV) and hyperkinetic disorder (HKD; "International Classification of Diseases"-10th Edition). Methods: Diagnostic criteria for…
A Vegetation Database for the Colorado River Ecosystem from Glen Canyon Dam to the Western Boundary of Grand Canyon National Park, Arizona

USGS Publications Warehouse

Ralston, Barbara E.; Davis, Philip A.; Weber, Robert M.; Rundall, Jill M.

2008-01-01

A vegetation database of the riparian vegetation located within the Colorado River ecosystem (CRE), a subsection of the Colorado River between Glen Canyon Dam and the western boundary of Grand Canyon National Park, was constructed using four-band image mosaics acquired in May 2002. A digital line scanner was flown over the Colorado River corridor in Arizona by ISTAR Americas, using a Leica ADS-40 digital camera to acquire a digital surface model and four-band image mosaics (blue, green, red, and near-infrared) for vegetation mapping. The primary objective of this mapping project was to develop a digital inventory map of vegetation to enable patch- and landscape-scale change detection, and to establish randomized sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). The vegetation base map was constructed through a combination of ground surveys to identify vegetation classes, image processing, and automated supervised classification procedures. Analysis of the imagery and subsequent supervised classification involved multiple steps to evaluate band quality, band ratios, and vegetation texture and density. Identification of vegetation classes involved collection of cover data throughout the river corridor and subsequent analysis using two-way indicator species analysis (TWINSPAN). Vegetation was classified into six vegetation classes, following the National Vegetation Classification Standard, based on cover dominance. This analysis indicated that total area covered by all vegetation within the CRE was 3,346 ha. Considering the six vegetation classes, the sparse shrub (SS) class accounted for the greatest amount of vegetation (627 ha) followed by Pluchea (PLSE) and Tamarix (TARA) at 494 and 366 ha, respectively. The wetland (WTLD) and Prosopis-Acacia (PRGL) classes both had similar areal cover values (227 and 213 ha, respectively). Baccharis-Salix (BAXX) was the least represented at 94 ha. Accuracy assessment of the supervised classification determined that accuracies varied among vegetation classes from 90% to 49%. Causes for low accuracies were similar spectral signatures among vegetation classes. Fuzzy accuracy assessment improved classification accuracies such that Federal mapping standards of 80% accuracies for all classes were met. The scale used to quantify vegetation adequately meets the needs of the stakeholder group. Increasing the scale to meet the U.S. Geological Survey (USGS)-National Park Service (NPS)National Mapping Program's minimum mapping unit of 0.5 ha is unwarranted because this scale would reduce the resolution of some classes (e.g., seep willow/coyote willow would likely be combined with tamarisk). While this would undoubtedly improve classification accuracies, it would not provide the community-level information about vegetation change that would benefit stakeholders. The identification of vegetation classes should follow NPS mapping approaches to complement the national effort and should incorporate the alternative analysis for community identification that is being incorporated into newer NPS mapping efforts. National Vegetation Classification is followed in this report for association- to formation-level categories. Accuracies could be improved by including more environmental variables such as stage elevation in the classification process and incorporating object-based classification methods. Another approach that may address the heterogeneous species issue and classification is to use spectral mixing analysis to estimate the fractional cover of species within each pixel and better quantify the cover of individual species that compose a cover class. Varying flights to capture vegetation at different times of the year might also help separate some vegetation classes, though the cost may be prohibitive. Lastly, photointerpretation instead of automated mapping could be tried. Photointerpretation would likely not improve accuracies in this case, howev
Adenocarcinoma of the lung with scattered consolidation: radiological-pathological correlation and prognosis.

PubMed

Jiang, Binghu; Takashima, Shodayu; Hakucho, Tomoaki; Hodaka, Numasaki; Yasuhiko, Tomita; Masahiko, Higashiyama

2013-10-01

To investigate the clinicopathological features and prognosis in patients with adenocarcinoma of the lung with scattered consolidation (ALSC). Between January 2006 and March 2010, 139 consecutive patients with lung adenocarcinoma of ≤3 cm, who underwent pulmonary resection for lung cancer, were investigated retrospectively. Radiologic classification was based on the findings of thin-section CT such as the presence of consolidation or ground-glass opacity (GGO). Type I (n=15) and Type II (n=14), showed a pure GGO and a mixed GGO with consolidation <50%, respectively. Type IV (n=38) and Type V (n=52) showed a mixed GGO with consolidation ≥50% and a pure consolidation, respectively. Type III (n=20) was the adenocarcinoma of the lung with scattered consolidation (ALSC). The clinicopathological features and prognosis of ALSC was investigated with comparative analysis and survival analysis. Because of the similar recurrence rate for Type I and Type II (P=1.000), Type IV and Type V (P=0.343), we merged Type I and Type II as Type I+II, Type IV and Type V as Type IV+V, respectively. In the 20 (14.4%) patients with ALSC, lymph node metastasis was not observed, and it was rare in lymphatic invasion and vascular invasion. On the basis of IASLC/ATS/ERS 2011 classification, 80% of the ALSC were preinvasive lesions. In Noguchi classification, there was no significant difference between Type I+II and ALSC (P=0.260). The prognosis of ALSC was similar to Type I+II (P=0.408), but better than Type IV+V (P=0.040). Adenocarcinoma of the lung with scattered consolidation (ALSC) on thin-section CT was a relatively favorable prognostic factor. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Development and characterization of a 3D high-resolution terrain database

NASA Astrophysics Data System (ADS)

Wilkosz, Aaron; Williams, Bryan L.; Motz, Steve

2000-07-01

A top-level description of methods used to generate elements of a high resolution 3D characterization database is presented. The database elements are defined as ground plane elevation map, vegetation height elevation map, material classification map, discrete man-made object map, and temperature radiance map. The paper will cover data collection by means of aerial photography, techniques of soft photogrammetry used to derive the elevation data, and the methodology followed to generate the material classification map. The discussion will feature the development of the database elements covering Fort Greely, Alaska. The developed databases are used by the US Army Aviation and Missile Command to evaluate the performance of various missile systems.
Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

PubMed Central

Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466
Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

PubMed

Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.
Large Scale Crop Mapping in Ukraine Using Google Earth Engine

NASA Astrophysics Data System (ADS)

Shelestov, A.; Lavreniuk, M. S.; Kussul, N.

2016-12-01

There are no globally available high resolution satellite-derived crop specific maps at present. Only coarse-resolution imagery (> 250 m spatial resolution) has been utilized to derive global cropland extent. In 2016 we are going to carry out a country level demonstration of Sentinel-2 use for crop classification in Ukraine within the ESA Sen2-Agri project. But optical imagery can be contaminated by cloud cover that makes it difficult to acquire imagery in an optimal time range to discriminate certain crops. Due to the Copernicus program since 2015, a lot of Sentinel-1 SAR data at high spatial resolution is available for free for Ukraine. It allows us to use the time series of SAR data for crop classification. Our experiment for one administrative region in 2015 showed much higher crop classification accuracy with SAR data than with optical only time series [1, 2]. Therefore, in 2016 within the Google Earth Engine Research Award we use SAR data together with optical ones for large area crop mapping (entire territory of Ukraine) using cloud computing capabilities available at Google Earth Engine (GEE). This study compares different classification methods for crop mapping for the whole territory of Ukraine using data and algorithms from GEE. Classification performance assessed using overall classification accuracy, Kappa coefficients, and user's and producer's accuracies. Also, crop areas from derived classification maps compared to the official statistics [3]. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297. N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "The use of satellite SAR imagery to crop classification in Ukraine within JECAM project," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1497-1500, 13-18 July 2014, Quebec City, Canada. F.J. Gallego, N. Kussul, S. Skakun, O. Kravchenko, A. Shelestov, O. Kussul, "Efficiency assessment of using satellite data for crop area estimation in Ukraine," International Journal of Applied Earth Observation and Geoinformation vol. 29, pp. 22-30, 2014.
Mapping regional distribution of a single tree species: Whitebark pine in the Greater Yellowstone Ecosystem

USGS Publications Warehouse

Landenburger, L.; Lawrence, R.L.; Podruzny, S.; Schwartz, C.C.

2008-01-01

Moderate resolution satellite imagery traditionally has been thought to be inadequate for mapping vegetation at the species level. This has made comprehensive mapping of regional distributions of sensitive species, such as whitebark pine, either impractical or extremely time consuming. We sought to determine whether using a combination of moderate resolution satellite imagery (Landsat Enhanced Thematic Mapper Plus), extensive stand data collected by land management agencies for other purposes, and modern statistical classification techniques (boosted classification trees) could result in successful mapping of whitebark pine. Overall classification accuracies exceeded 90%, with similar individual class accuracies. Accuracies on a localized basis varied based on elevation. Accuracies also varied among administrative units, although we were not able to determine whether these differences related to inherent spatial variations or differences in the quality of available reference data.
Mapping permafrost in the boreal forest with Thematic Mapper satellite data

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Strong, L. L.; Card, D. H.

1986-01-01

A geographic data base incorporating Landsat TM data was used to develop and evaluate logistic discriminant functions for predicting the distribution of permafrost in a boreal forest watershed. The data base included both satellite-derived information and ancillary map data. Five permafrost classifications were developed from a stratified random sample of the data base and evaluated by comparison with a photo-interpreted permafrost map using contingency table analysis and soil temperatures recorded at sites within the watershed. A classification using a TM thermal band and a TM-derived vegetation map as independent variables yielded the highest mapping accuracy for all permafrost categories.
Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Otvos, Ervin; Giardino, Marco

2002-01-01

A chain of barrier islands provides protection against hurricanes and severe storms along the south and southeastern shores of the United States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4-meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5-meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Gorges. Classification accuracy is being addressed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.
Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Otvos, Ervin; Giardino, Marco

2003-01-01

A chain of barrier islands provides protection against hurricanes and severe storms along the southern and southeastern shores of the Unites States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4 meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5 meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Georges. Classification accuracy is being assessed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.

Will it Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

NASA Astrophysics Data System (ADS)

Zlinszky, A.; Kania, A.

2016-06-01

Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with "dominance profiles" visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.
Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.
Impervious surface mapping with Quickbird imagery

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio

2010-01-01

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of “salt-and-pepper” pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance. PMID:21643434
Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data

PubMed Central

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-01-01

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification. PMID:28025525
Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

PubMed

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-12-22

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.
Vegetation inventory, mapping, and classification report, Fort Bowie National Historic Site

USGS Publications Warehouse

Studd, Sarah; Fallon, Elizabeth; Crumbacher, Laura; Drake, Sam; Villarreal, Miguel

2013-01-01

A vegetation mapping and characterization effort was conducted at Fort Bowie National Historic Site in 2008-10 by the Sonoran Desert Network office in collaboration with researchers from the Office of Arid lands studies, Remote Sensing Center at the University of Arizona. This vegetation mapping effort was completed under the National Park Service Vegetation Inventory program which aims to complete baseline mapping inventories at over 270 national park units. The vegetation map data was collected to provide park managers with a digital map product that met national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and even national context. Work comprised of three major field phases 1) concurrent field-based classification data collection and mapping (map unit delineation), 2) development of vegetation community types at the National Vegetation Classification alliance or association level and 3) map accuracy assessment. Phase 1 was completed in late 2008 and early 2009. Community type descriptions were drafted to meet the then-current hierarchy (version 1) of the National Vegetation Classification System (NVCS) and these were applied to each of the mapped areas. This classification was developed from both plot level data and censused polygon data (map units) as this project was conducted as a concurrent mapping and classification effort. The third stage of accuracy assessment completed in the fall of 2010 consisted of a complete census of each map unit and was conducted almost entirely by park staff. Following accuracy assessment the map was amended where needed and final products were developed including this report, a digital map and full vegetation descriptions. Fort Bowie National Historic Site covers only 1000 acres yet has a relatively complex landscape, topography and geology. A total of 16 distinct communities were described and mapped at Fort Bowie NHS. These ranged from lush riparian woodlands lining the ephemeral washes dominated by Ash (Fraxinus), Walnut (Juglans) and Hackberry (Celtis) to drier upland sites typical of desert scrub and semi-desert grassland communities. These shrublands boast a diverse mixture of shrubs, succulents and perennial grasses. In many places the vegetation could be seen to echo the history of the fort site, with management of shrub encroachment apparent in the grasslands and the paucity of trees evidence of historic cutting for timber and fire wood. Seven of the 16 vegetation types were ‘accepted’ types within the NVC while the others have been described here as specific to FOBO and have proposed status within the NVC. The map was designed to facilitate ecologically-based natural resources management and research. The map is in digital format within a geodatabase structure that allows for complex relationships to be established between spatial and tabular data, and makes accessing the product easy and seamless. The GIS format allows user flexibility and will also enable updates to be made as new information becomes available (such as revised NVC codes or vegetation type names) or in the event of major disturbance events that could impact the vegetation.
Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water

NASA Astrophysics Data System (ADS)

Li, Dong; Tang, Cheng; Xia, Chunlei; Zhang, Hua

2017-02-01

Artificial reefs (ARs) are effective means to maintain fishery resources and to restore ecological environment in coastal waters. ARs have been widely constructed along the Chinese coast. However, understanding of benthic habitats in the vicinity of ARs is limited, hindering effective fisheries and aquacultural management. Multibeam echosounder (MBES) is an advanced acoustic instrument capable of efficiently generating large-scale maps of benthic environments at fine resolutions. The objective of this study is to develop a technical approach to characterize, classify, and map shallow coastal areas with ARs using an MBES. An automated classification method is designed and tested to process bathymetric and backscatter data from MBES and transform the variables into simple, easily visualized maps. To reduce the redundancy in acoustic variables, a principal component analysis (PCA) is used to condense the highly collinear dataset. An acoustic benthic map of bottom sediments is classified using an iterative self-organizing data analysis technique (ISODATA). The approach is tested with MBES surveys in a 1.15 km2 fish farm with a high density of ARs off the Yantai coast in northern China. Using this method, 3 basic benthic habitats (sandy bottom, muddy sediments, and ARs) are distinguished. The results of the classification are validated using sediment samples and underwater surveys. Our study shows that the use of MBES is an effective method for acoustic mapping and classification of ARs.
Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.
Application of SEASAT-1 Synthetic Aperture Radar (SAR) data to enhance and detect geological lineaments and to assist LANDSAT landcover classification mapping. [Appalachian Region, West Virginia

NASA Technical Reports Server (NTRS)

Sekhon, R.

1981-01-01

Digital SEASAT-1 synthetic aperture radar (SAR) data were used to enhance linear features to extract geologically significant lineaments in the Appalachian region. Comparison of Lineaments thus mapped with an existing lineament map based on LANDSAT MSS images shows that appropriately processed SEASAT-1 SAR data can significantly improve the detection of lineaments. Merge MSS and SAR data sets were more useful fo lineament detection and landcover classification than LANDSAT or SEASAT data alone. About 20 percent of the lineaments plotted from the SEASAT SAR image did not appear on the LANDSAT image. About 6 percent of minor lineaments or parts of lineaments present in the LANDSAT map were missing from the SEASAT map. Improvement in the landcover classification (acreage and spatial estimation accuracy) was attained by using MSS-SAR merged data. The aerial estimation of residential/built-up and forest categories was improved. Accuracy in estimating the agricultural and water categories was slightly reduced.
Aggregation of Sentinel-2 time series classifications as a solution for multitemporal analysis

NASA Astrophysics Data System (ADS)

Lewiński, Stanislaw; Nowakowski, Artur; Malinowski, Radek; Rybicki, Marcin; Kukawska, Ewa; Krupiński, Michał

2017-10-01

The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.
Satellite Remote Sensing of Cropland Characteristics in 30m Resolution: The First North American Continental-Scale Classification on High Performance Computing Platforms

NASA Astrophysics Data System (ADS)

Massey, Richard

Cropland characteristics and accurate maps of their spatial distribution are required to develop strategies for global food security by continental-scale assessments and agricultural land use policies. North America is the major producer and exporter of coarse grains, wheat, and other crops. While cropland characteristics such as crop types are available at country-scales in North America, however, at continental-scale cropland products are lacking at fine sufficient resolution such as 30m. Additionally, applications of automated, open, and rapid methods to map cropland characteristics over large areas without the need of ground samples are needed on efficient high performance computing platforms for timely and long-term cropland monitoring. In this study, I developed novel, automated, and open methods to map cropland extent, crop intensity, and crop types in the North American continent using large remote sensing datasets on high-performance computing platforms. First, a novel method was developed in this study to fuse pixel-based classification of continental-scale Landsat data using Random Forest algorithm available on Google Earth Engine cloud computing platform with an object-based classification approach, recursive hierarchical segmentation (RHSeg) to map cropland extent at continental scale. Using the fusion method, a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010 was produced. In this map, the total cropland area for North America was estimated at 275.2 million hectares (Mha). This map was assessed for accuracy using randomly distributed samples derived from United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-Food Canada (AAFC) annual crop inventory (ACI), Servicio de Informacion Agroalimentaria y Pesquera (SIAP), Mexico's agricultural boundaries, and photo-interpretation of high-resolution imagery. The overall accuracies of the map are 93.4% with a producer's accuracy for crop class at 85.4% and user's accuracy of 74.5% across the continent. The sub-country statistics including state-wise and county-wise cropland statistics derived from this map compared well in regression models resulting in R2 > 0.84. Secondly, an automated phenological pattern matching (PPM) method to efficiently map cropping intensity was also developed in this study. This study presents a continental-scale cropping intensity map for the North American continent at 250m spatial resolution for 2010. In this map, the total areas for single crop, double crop, continuous crop, and fallow were estimated to be 123.5 Mha, 11.1 Mha, 64.0 Mha, and 83.4 Mha, respectively. This map was assessed using limited country-level reference datasets derived from United States Department of Agriculture cropland data layer and Agriculture and Agri-Food Canada annual crop inventory with overall accuracies of 79.8% and 80.2%, respectively. Third, two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification were developed. The classification approaches use similarities and dissimilarities in crop type phenology derived from NDVI time-series data for the two approaches. Annual crop type maps were produced for 8 major crop types in the United States using the generalized classification approach for 2001-2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies greater than 78%, while the generalized classifier had accuracies greater than 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year with overall accuracies > 70% across all independent years. Taken together, these cropland products of extent, cropping intensity, and crop types, are significantly beneficial in agricultural and water use planning and monitoring to formulate policies towards global and North American food security issues.
Integrating multisource imagery and GIS analysis for mapping Bermuda`s benthic habitats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vierros, M.K.

1997-06-01

Bermuda is a group of isolated oceanic situated in the northwest Atlantic Ocean and surrounded by the Sargasso Sea. Bermuda possesses the northernmost coral reefs and mangroves in the Atlantic Ocean, and because of its high population density, both the terrestrial and marine environments are under intense human pressure. Although a long record of scientific research exists, this study is the first attempt to comprehensively map the area`s benthic habitats, despite the need for such a map for resource assessment and management purposes. Multi-source and multi-date imagery were used for producing the habitat map due to lack of a completemore » up-to-date image. Classifications were performed with SPOT data, and the results verified from recent aerial photography and current aerial video, along with extensive ground truthing. Stratification of the image into regions prior to classification reduced the confusing effects of varying water depth. Classification accuracy in shallow areas was increased by derivation of a texture pseudo-channel, while bathymetry was used as a classification tool in deeper areas, where local patterns of zonation were well known. Because of seasonal variation in extent of seagrasses, a classification scheme based on density could not be used. Instead, a set of classes based on the seagrass area`s exposure to the open ocean were developed. The resulting habitat map is currently being assessed for accuracy with promising preliminary results, indicating its usefulness as a basis for future resource assessment studies.« less
Bolivian satellite technology program on ERTS natural resources

NASA Technical Reports Server (NTRS)

Brockmann, H. C. (Principal Investigator); Bartoluccic C., L.; Hoffer, R. M.; Levandowski, D. W.; Ugarte, I.; Valenzuela, R. R.; Urena E., M.; Oros, R.

1977-01-01

The author has identified the following significant results. Application of digital classification for mapping land use permitted the separation of units at more specific levels in less time. A correct classification of data in the computer has a positive effect on the accuracy of the final products. Land use unit comparison with types of soils as represented by the colors of the coded map showed a class relation. Soil types in relation to land cover and land use demonstrated that vegetation was a positive factor in soils classification. Groupings of image resolution elements (pixels) permit studies of land use at different levels, thereby forming parameters for the classification of soils.
Soil classification based on cone penetration test (CPT) data in Western Central Java

NASA Astrophysics Data System (ADS)

Apriyono, Arwan; Yanto, Santoso, Purwanto Bekti; Sumiyanto

2018-03-01

This study presents a modified friction ratio range for soil classification i.e. gravel, sand, silt & clay and peat, using CPT data in Western Central Java. The CPT data was obtained solely from Soil Mechanic Laboratory of Jenderal Soedirman University that covers more than 300 sites within the study area. About 197 data were produced from data filtering process. IDW method was employed to interpolated friction ratio values in a regular grid point for soil classification map generation. Soil classification map was generated and presented using QGIS software. In addition, soil classification map with respect to modified friction ratio range was validated using 10% of total measurements. The result shows that silt and clay dominate soil type in the study area, which is in agreement with two popular methods namely Begemann and Vos. However, the modified friction ratio range produces 85% similarity with laboratory measurements whereby Begemann and Vos method yields 70% similarity. In addition, modified friction ratio range can effectively distinguish fine and coarse grains, thus useful for soil classification and subsequently for landslide analysis. Therefore, modified friction ratio range proposed in this study can be used to identify soil type for mountainous tropical region.
Classification and Short-Term Course of DSM-IV Cannabis, Hallucinogen, Cocaine, and Opioid Disorders in Treated Adolescents

ERIC Educational Resources Information Center

Chung, Tammy; Martin, Christoper S.

2005-01-01

This study examined the latent class structure of Diagnostic and Statistical Manual of Mental Disorders (text rev.; DSM-IV; American Psychiatric Association, 2000) symptoms used to diagnose cannabis, hallucinogen, cocaine, and opiate disorders among 501 adolescents recruited from addictions treatment. Latent class results were compared with the…
The Brief Child and Family Phone Interview (BCFPI): 2. Usefulness in Screening for Child and Adolescent Psychopathology

ERIC Educational Resources Information Center

Boyle, Michael H.; Cunningham, Charles E.; Georgiades, Katholiki; Cullen, John; Racine, Yvonne; Pettingill, Peter

2009-01-01

Background: This study examines the use of the Brief Child and Family Phone Interview (BCFPI) to screen for childhood psychiatric disorder based on Diagnostic Interview Schedule for Children Version IV (DISC-IV) classifications of attention-deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD),…
Vegetation classification and distribution mapping report Mesa Verde National Park

USGS Publications Warehouse

Thomas, Kathryn A.; McTeague, Monica L.; Ogden, Lindsay; Floyd, M. Lisa; Schulz, Keith; Friesen, Beverly A.; Fancher, Tammy; Waltermire, Robert G.; Cully, Anne

2009-01-01

The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted during photointerpretation, and non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 5,007 polygons in the project area. A field-based accuracy assessment of the base map classes showed overall accuracy to be 43.5%. Seven map classes comprise 89.1% of the park vegetated land cover. The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, version 2 (Federal Geographic Data Committee 2007), and reflecting physiognomy and floristics. Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as the fi rst approximation of the group level. The project team identified 14 group map classes for this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 80.3%. In consultation with park staff , the team developed management map classes, consisting of park-defined groupings of base map classes intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 23 management map classes had an overall accuracy of 73.3%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary digital geographic information system and database products were also produced that can be used independently or to augment the main products. These products include shapefiles of the locations of field-collected data and relational databases of field-collected data.
Pattern of Cortical Fracture following Corticotomy for Distraction Osteogenesis.

PubMed

Luvan, M; Kanthan, S R; Roshan, G; Saw, A

2015-11-01

Corticotomy is an essential procedure for deformity correction and there are many techniques described. However there is no proper classification of the fracture pattern resulting from corticotomies to enable any studies to be conducted. We performed a retrospective study of corticotomy fracture patterns in 44 patients (34 tibias and 10 femurs) performed for various indications. We identified four distinct fracture patterns, Type I through IV classification based on the fracture propagation following percutaneous corticotomy. Type I transverse fracture, Type II transverse fracture with a winglet, Type III presence of butterfly fragment and Type IV fracture propagation to a fixation point. No significant correlation was noted between the fracture pattern and the underlying pathology or region of corticotomy.
Integrating remote sensing and terrain data in forest fire modeling

NASA Astrophysics Data System (ADS)

Medler, Michael Johns

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.
Dissecting the Molecular Mechanism of RhoC GTPase Expression in the Normal and Malignant Breast

DTIC Science & Technology

2010-09-01

Headquarters Services , Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202- 4302...transcriptome and categorized as (i) mapping to same gene, (ii) mapping to different genes (chimera candidates), (iii) nonmapping, (iv) mitochondrial, (v) quality ...mitochondrial, (iv) quality control, (v) chimera can- didates, and (vi) nonmapping. Chimera candidates and nonmapping categories were used for gene fusion

Lake water quality mapping from LANDSAT

NASA Technical Reports Server (NTRS)

Scherz, J. P.

1977-01-01

The lakes in three LANDSAT scenes were mapped by the Bendix MDAS multispectral analysis system. Field checking the maps by three separate individuals revealed approximately 90-95% correct classification for the lake categories selected. Variations between observers was about 5%. From the MDAS color coded maps the lake with the worst algae problem was easily located. This lake was closely checked and a pollution source of 100 cows was found in the springs which fed this lake. The theory, lab work and field work which made it possible for this demonstration project to be a practical lake classification procedure are presented.
Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

PubMed Central

Che Hasan, Rozaimi; Ierodiaconou, Daniel; Laurenson, Laurie; Schimel, Alexandre

2014-01-01

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management. PMID:24824155
Assessing Hurricane Katrina Vegetation Damage at Stennis Space Center using IKONOS Image Classification Techniques

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ross, Kenton W.; Graham, William D.

2006-01-01

Hurricane Katrina inflicted widespread damage to vegetation in southwestern coastal Mississippi upon landfall on August 29, 2005. Storm damage to surface vegetation types at the NASA John C. Stennis Space Center (SSC) was mapped and quantified using IKONOS data originally acquired on September 2, 2005, and later obtained via a Department of Defense ClearView contract. NASA SSC management required an assessment of the hurricane s impact to the 125,000-acre buffer zone used to mitigate rocket engine testing noise and vibration impacts and to manage forestry and fire risk. This study employed ERDAS IMAGINE software to apply traditional classification techniques to the IKONOS data. Spectral signatures were collected from multiple ISODATA classifications of subset areas across the entire region and then appended to a master file representative of major targeted cover type conditions. The master file was subsequently used with the IKONOS data and with a maximum likelihood algorithm to produce a supervised classification later refined using GIS-based editing. The final results enabled mapped, quantitative areal estimates of hurricane-induced damage according to general surface cover type. The IKONOS classification accuracy was assessed using higher resolution aerial imagery and field survey data. In-situ data and GIS analysis indicate that the results compare well to FEMA maps of flooding extent. The IKONOS classification also mapped open areas with woody storm debris. The detection of such storm damage categories is potentially useful for government officials responsible for hurricane disaster mitigation.
Satellite information on Orlando, Florida. [coordination of LANDSAT and Skylab data and EREP photography

NASA Technical Reports Server (NTRS)

Hannah, J. W.; Thomas, G. L.; Esparza, F.

1975-01-01

A land use map of Orange County, Florida was prepared from EREP photography while LANDSAT and EREP multispectral scanner data were used to provide more detailed information on Orlando and its suburbs. The generalized maps were prepared by tracing the patterns on an overlay, using an enlarging viewer. Digital analysis of the multispectral scanner data was basically the maximum likelihood classification method with training sample input and computer printer mapping of the results. Urban features delineated by the maps are discussed. It is concluded that computer classification, accompanied by human interpretation and manual simplification can produce land use maps which are useful on a regional, county, and city basis.
Comparing Forest/Nonforest Classifications of Landsat TM Imagery for Stratifying FIA Estimates of Forest Land Area

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Greg C. Liknes; Geoffrey R. Holden

2005-01-01

Landsat Thematic Mapper (TM) satellite imagery and Forest Inventory and Analysis (FIA) plot data were used to construct forest/nonforest maps of Mapping Zone 41, National Land Cover Dataset 2000 (NLCD 2000). Stratification approaches resulting from Maximum Likelihood, Fuzzy Convolution, Logistic Regression, and k-Nearest Neighbors classification/prediction methods were...
Field validation of Burned Area Reflectance Classification (BARC) products for post fire assessment

Treesearch

Andrew T. Hudak; Peter R. Robichaud; Jeffery B. Evans; Jess Clark; Keith Lannom; Penelope Morgan; Carter Stone

2004-01-01

The USFS Remote Sensing Applications Center (RSAC) and the USGS EROS Data Center (EDC) produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Rehabilitation (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of soil burn severity, but photosynthetic and nonphotosynthetic vegetation also influences the...
Three approaches to the classification of inland wetlands. [Dismal Swamp, Tennessee, and Florida

NASA Technical Reports Server (NTRS)

Gammon, P. T.; Malone, D.; Brooks, P. D.; Carter, V.

1977-01-01

In the Dismal Swamp project, seasonal, color-infrared aerial photographs and LANDSAT digital data were interpreted for a detailed analysis of the vegetative communities in a large, highly altered wetland. In Western Tennessee, seasonal high altitude color-infrared aerial photographs provided the hydrologic and vegetative information needed to map inland wetlands, using a classification system developed for the Tennessee Valley Region. In Florida, color-infrared aerial photographs were analyzed to produce wetland maps using three existing classification systems to evaluate the information content and mappability of each system. The methods used in each of the three projects can be extended or modified for use in the mapping of inland wetlands in other parts of the United States.
The 1-month prevalence of generalized anxiety disorder according to DSM-IV, DSM-V, and ICD-10 among nondemented 75-year-olds in Gothenburg, Sweden.

PubMed

Nilsson, Johan; Östling, Svante; Waern, Margda; Karlsson, Björn; Sigström, Robert; Guo, Xinxin; Skoog, Ingmar

2012-11-01

To examine the 1-month prevalence of generalized anxiety disorder (GAD) according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), Diagnostic and Statistical Manual of Mental, Fifth Edition (DSM-V), and International Classification of Diseases, Tenth Revision (ICD-10), and the overlap between these criteria, in a population sample of 75-year-olds. We also aimed to examine comorbidity between GAD and other psychiatric diagnoses, such as depression. During 2005-2006, a comprehensive semistructured psychiatric interview was conducted by trained nurses in a representative population sample of 75-year-olds without dementia in Gothenburg, Sweden (N = 777; 299 men and 478 women). All psychiatric diagnoses were made according to DSM-IV. GAD was also diagnosed according to ICD-10 and DSM-V. The 1-month prevalence of GAD was 4.1% (N = 32) according to DSM-IV, 4.5% (N = 35) according to DSM-V, and 3.7% (N = 29) according to ICD-10. Only 46.9% of those with DSM-IV GAD fulfilled ICD-10 criteria, and only 51.7% and 44.8% of those with ICD-10 GAD fulfilled DSM-IV/V criteria. Instead, 84.4% and 74.3% of those with DSM-IV/V GAD and 89.7% of those with ICD-10 GAD had depression. Also other psychiatric diagnoses were common in those with ICD-10 and DSM-IV GAD. Only a small minority with GAD, irrespective of criteria, had no other comorbid psychiatric disorder. ICD-10 GAD was related to an increased mortality rate. While GAD was common in 75-year-olds, DSM-IV/V and ICD-10 captured different individuals. Current definitions of GAD may comprise two different expressions of the disease. There was greater congruence between GAD in either classification system and depression than between DSM-IV/V GAD and ICD-10 GAD, emphasizing the close link between these entities. 2012 American Association for Geriatric Psychiatry
Mapping of epitopes and structural analysis of antigenic sites in the nucleoprotein of rabies virus.

PubMed

Goto, H; Minamoto, N; Ito, H; Ito, N; Sugiyama, M; Kinjo, T; Kawai, A

2000-01-01

Linear epitopes on the rabies virus nucleoprotein (N) recognized by six MAbs raised against antigenic sites I (MAbs 6-4, 12-2 and 13-27) and IV (MAbs 6-9, 7-12 and 8-1) were investigated. Based on our previous studies on sites I and IV, 24 consecutively overlapping octapeptides and N- and C-terminal-deleted mutant N proteins were prepared. Results showed that all three site I epitopes studied and two site IV epitopes (for MAbs 8-1 and 6-9) mapped to aa 358-367, and that the other site IV epitope of MAb 7-12 mapped to aa 375-383. Tests using chimeric and truncated proteins showed that MAb 8-1 also requires the N-terminal sequence of the N protein to recognize its binding region more efficiently. Immunofluorescence studies demonstrated that all three site I-specific MAbs and one site IV-specific MAb (7-12) stained the N antigen that was diffusely distributed in the whole cytoplasm; the other two site IV-specific MAbs (6-9 and 8-1) detected only the N antigen in the cytoplasmic inclusion bodies (CIB). An antigenic site II-specific MAb (6-17) also detected CIB-associated N antigen alone. Furthermore, the level of diffuse N antigens decreased after treatment of infected cells with cycloheximide. These results suggest that epitopes at site I are expressed on the immature form of the N protein, but epitope structures of site IV MAbs 6-9 and 8-1 are created and/or exposed only after maturation of the N protein.
Variance approximations for assessments of classification accuracy

Treesearch

R. L. Czaplewski

1994-01-01

Variance approximations are derived for the weighted and unweighted kappa statistics, the conditional kappa statistic, and conditional probabilities. These statistics are useful to assess classification accuracy, such as accuracy of remotely sensed classifications in thematic maps when compared to a sample of reference classifications made in the field. Published...
Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches

NASA Astrophysics Data System (ADS)

Diesing, Markus; Green, Sophie L.; Stephens, David; Lark, R. Murray; Stewart, Heather A.; Dove, Dayton

2014-08-01

Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment. Overall thematic classification accuracy ranged from 67% to 76% and Cohen's kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.
The craving withdrawal model for alcoholism: towards the DSM-V. Improving the discriminant validity of alcohol use disorder diagnosis.

PubMed

de Bruijn, Carla; van den Brink, Wim; de Graaf, Ron; Vollebergh, Wilma A M

2005-01-01

To compare the discriminant validity of the DSM-IV and the ICD-10 classification of alcohol use disorders (AUD) with an alternative classification, the craving withdrawal model (CWM). CWM requires craving and withdrawal for the diagnosis of alcohol dependence and raises the alcohol abuse threshold to two DSM-IV AUD criteria. Data were derived from The Netherlands Mental Health Survey and Incidence Study, a large representative sample of the general Dutch population. In the present study, only non-abstinent subjects were included (n=6041). Three diagnostic systems (DSM-IV, ICD-10, and CWM) were compared using the following discriminant variables: alcohol intake, psychiatric comorbidity, functional status, familial alcohol problems, and treatment sought. The year prevalence of CWM alcohol dependence was lower than the prevalence of ICD-10 and DSM-IV dependence (0.3% vs 1.4% and 1.4%). The year prevalence of abuse was similar for CWM and DSM-IV (4.7 and 4.9%), but lower for ICD-10 harmful use (1.7%). DSM-IV resulted in a poor distinction between normality and abuse and ICD-10 resulted in a poor distinction between harmful use and dependence. In contrast, the CWM distinctions between normality and abuse, and between abuse, and dependence were significant for most of the discriminant variables. This study indicates that CWM improves the discriminant validity of AUD diagnoses. The predictive validity of the CWM for alcohol and other substance use disorders remain to be studied.
Malaysia’s Experience in War Against Communist Insurgency and Its Relevance to the Present Situation in Iraq

DTIC Science & Technology

2005-01-01

Shangri - La Hotel , Makati City 48 Ibid. 49 Stanley S. Bedlington. Malaysia and Singapore. The Building of New States. Cornell University Press Ltd...Page Preface i Contents ii Executive Summary iii Map on Malaya iv INTRODUCTION 1 THE INSURGENCY AGAINST THE MCP 3 The First Emergency...will be a good start. MAP OF MALAYA (Peninsular Malaysia) iv 1 INTRODUCTION In mid June 1948 the
A detailed procedure for the use of small-scale photography in land use classification

NASA Technical Reports Server (NTRS)

Vegas, P. L.

1974-01-01

A procedure developed to produce accurate land use maps from available high-altitude, small-scale photography in a cost-effective manner is presented. An alternative procedure, for use when the capability for updating the resultant land use map is not required, is also presented. The technical approach is discussed in detail, and personnel and equipment needs are analyzed. Accuracy percentages are listed, and costs are cited. The experiment land use classification categories are explained, and a proposed national land use classification system is recommended.
Information Security Program Regulation

DTIC Science & Technology

1986-06-01

above. When an alarmed area is used for the storage of Top Secret material, the physical barrier must be adequate to prevent (a) surreptitious removal ...IV-9 4-304 Removable ADP and Word Processing Storage Media ---------- IV-10 4-305 Documents Produced by ADP Equipment...with a removal or cancellation of the classification designation. 1-315 Declassification Event An event that eliminates the need for continued
Hierarchical subdivisions of the Columbia Plateau and Blue Mountains ecoregions, Oregon and Washington.

Treesearch

Sharon E. Clarke; Sandra A. Bryce

1997-01-01

This document presents two spatial scales of a hierarchical, ecoregional framework and provides a connection to both larger and smaller scale ecological classifications. The two spatial scales are subregions (1:250,000) and landscape-level ecoregions (1:100,000), or Level IV and Level V ecoregions. Level IV ecoregions were developed by the Environmental Protection...
Level III and IV Ecoregions by State

EPA Pesticide Factsheets

Information and links to downloadable maps and datasets for Level III and IV ecoregions, listed by state. Ecoregions are areas of general similarity in the type, quality, and quantity of environmental resources.
Level III and IV Ecoregions by EPA Region

EPA Pesticide Factsheets

Information and downloadable maps and datasets for Level III and IV ecoregions, listed by EPA region. Ecoregions are areas of general similarity in the type, quality, and quantity of environmental resources.
Applicability of the ICD-11 proposal for PTSD: a comparison of prevalence and comorbidity rates with the DSM-IV PTSD classification in two post-conflict samples

PubMed Central

Stammel, Nadine; Abbing, Eva M.; Heeke, Carina; Knaevelsrud, Christine

2015-01-01

Background The World Health Organization recently proposed significant changes to the posttraumatic stress disorder (PTSD) diagnostic criteria in the 11th edition of the International Classification of Diseases (ICD-11). Objective The present study investigated the impact of these changes in two different post-conflict samples. Method Prevalence and rates of concurrent depression and anxiety, socio-demographic characteristics, and indicators of clinical severity according to ICD-11 in 1,075 Cambodian and 453 Colombian civilians exposed to civil war and genocide were compared to those according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results Results indicated significantly lower prevalence rates under the ICD-11 proposal (8.1% Cambodian sample and 44.4% Colombian sample) compared to the DSM-IV (11.2% Cambodian sample and 55.0% Colombian sample). Participants meeting a PTSD diagnosis only under the ICD-11 proposal had significantly lower rates of concurrent depression and a lower concurrent total score (depression and anxiety) compared to participants meeting only DSM-IV diagnostic criteria. There were no significant differences in socio-demographic characteristics and indicators of clinical severity between these two groups. Conclusions The lower prevalence of PTSD according to the ICD-11 proposal in our samples of persons exposed to a high number of traumatic events may counter criticism of previous PTSD classifications to overuse the PTSD diagnosis in populations exposed to extreme stressors. Also another goal, to better distinguish PTSD from comorbid disorders could be supported with our data. PMID:25989951
Applicability of the ICD-11 proposal for PTSD: a comparison of prevalence and comorbidity rates with the DSM-IV PTSD classification in two post-conflict samples.

PubMed

Stammel, Nadine; Abbing, Eva M; Heeke, Carina; Knaevelsrud, Christine

2015-01-01

The World Health Organization recently proposed significant changes to the posttraumatic stress disorder (PTSD) diagnostic criteria in the 11th edition of the International Classification of Diseases (ICD-11). The present study investigated the impact of these changes in two different post-conflict samples. Prevalence and rates of concurrent depression and anxiety, socio-demographic characteristics, and indicators of clinical severity according to ICD-11 in 1,075 Cambodian and 453 Colombian civilians exposed to civil war and genocide were compared to those according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results indicated significantly lower prevalence rates under the ICD-11 proposal (8.1% Cambodian sample and 44.4% Colombian sample) compared to the DSM-IV (11.2% Cambodian sample and 55.0% Colombian sample). Participants meeting a PTSD diagnosis only under the ICD-11 proposal had significantly lower rates of concurrent depression and a lower concurrent total score (depression and anxiety) compared to participants meeting only DSM-IV diagnostic criteria. There were no significant differences in socio-demographic characteristics and indicators of clinical severity between these two groups. The lower prevalence of PTSD according to the ICD-11 proposal in our samples of persons exposed to a high number of traumatic events may counter criticism of previous PTSD classifications to overuse the PTSD diagnosis in populations exposed to extreme stressors. Also another goal, to better distinguish PTSD from comorbid disorders could be supported with our data.

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.
Cardiovascular activity of rasagiline, a selective and potent inhibitor of mitochondrial monoamine oxidase B: comparison with selegiline

PubMed Central

Abassi, Zaid A; Binah, Ofer; Youdim, Moussa B H

2004-01-01

Selegiline is used for treating Parkinson's disease. Despite its efficacy, the clinical use of selegiline in combination with L-dihydroxphenylalanine in Parkinsonian patients is hampered by cardiovascular complications, such as hypotension. This study was designed to compare in rats the cardiovascular effects of selegiline and rasagiline, their metabolites L-methamphetamine and aminoindan (TVP-136), respectively, and the second rasagiline metabolite non-monoamine oxidase (MAO) inhibitor TVP-1022 (N-propargyl-1S(−)aminoindan). Intravenous (i.v.) administration of selegiline and rasagiline (1 mg kg−1) to anaesthetized rats (thiobutabarbital, 100 mg kg−1, i.p.) did not affect mean arterial pressure (MAP), carotid blood flow (CBF) or carotid vascular resistance (CVR). Selegiline (10 mg kg−1, i.v.) decreased MAP, CBF and increased CVR. In contrast, rasagiline (10 mg kg−1, i.v.) caused a small transient decrease in MAP, while CBF and CVR were unchanged. L-methamphetamine (1 mg kg−1, i.v.) administration provoked a dramatic and long-lasting depressor response, decreased CBF and increased CVR. In contrast, injection of aminoindan or TVP-1022 at a similar dose produced gradual nonsignificant decreases in MAP and CBF. Chronic oral treatment (21 days) of awake rats with selegiline at 10 mg kg−1 decreased systolic blood pressure (SBP), diastolic blood pressure (DBP), and MAP, whereas heart rate was unaffected. Since the effective MAO-B inhibitory and clinical dose of rasagiline is about one-tenth that of selegiline, administration of 1 mg kg−1 day−1 rasagiline resulted in moderate decreases in SBP, DBP, and MAP, which were significantly lower than those caused by the 10 mg kg−1 day−1 dose of selegiline. These findings indicate that rasagiline, when given at doses equivalent to selegiline, is less likely to be hypotensive. PMID:15339864
Cardiovascular activity of rasagiline, a selective and potent inhibitor of mitochondrial monoamine oxidase B: comparison with selegiline.

PubMed

Abassi, Zaid A; Binah, Ofer; Youdim, Moussa B H

2004-10-01

Selegiline is used for treating Parkinson's disease. Despite its efficacy, the clinical use of selegiline in combination with l-dihydroxphenylalanine in Parkinsonian patients is hampered by cardiovascular complications, such as hypotension. This study was designed to compare in rats the cardiovascular effects of selegiline and rasagiline, their metabolites l-methamphetamine and aminoindan (TVP-136), respectively, and the second rasagiline metabolite non-monoamine oxidase (MAO) inhibitor TVP-1022 (N-propargyl-1S(-)aminoindan). Intravenous (i.v.) administration of selegiline and rasagiline (1 mg kg(-1)) to anaesthetized rats (thiobutabarbital, 100 mg kg(-1), i.p.) did not affect mean arterial pressure (MAP), carotid blood flow (CBF) or carotid vascular resistance (CVR). Selegiline (10 mg kg(-1), i.v.) decreased MAP, CBF and increased CVR. In contrast, rasagiline (10 mg kg(-1), i.v.) caused a small transient decrease in MAP, while CBF and CVR were unchanged. l-methamphetamine (1 mg kg(-1), i.v.) administration provoked a dramatic and long-lasting depressor response, decreased CBF and increased CVR. In contrast, injection of aminoindan or TVP-1022 at a similar dose produced gradual nonsignificant decreases in MAP and CBF. Chronic oral treatment (21 days) of awake rats with selegiline at 10 mg kg(-1) decreased systolic blood pressure (SBP), diastolic blood pressure (DBP), and MAP, whereas heart rate was unaffected. Since the effective MAO-B inhibitory and clinical dose of rasagiline is about one-tenth that of selegiline, administration of 1 mg kg(-1) day(-1) rasagiline resulted in moderate decreases in SBP, DBP, and MAP, which were significantly lower than those caused by the 10 mg kg(-1) day(-1) dose of selegiline. These findings indicate that rasagiline, when given at doses equivalent to selegiline, is less likely to be hypotensive.
International VLBI Service for Geodesy and Astrometry 2000 Annual Report

NASA Technical Reports Server (NTRS)

Vandenberg, N. R. (Editor); Baver, K. D. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

This volume of reports is the 2000 Annual Report of the International Very Long Base Interferometry (VLBI) Service for Geodesy and Astrometry (IVS). The individual reports were contributed by VLBI groups in the international geodetic and astrometric community who constitute the permanent components of IVS. The IVS 2000 Annual Report documents the work of the IVS components for the period March 1, 1999 (the official inauguration date of IVS) through December 31, 2000. The reports document changes, activities, and progress of the IVS. The entire contents of this Annual Report also appear on the IVS web site at http://ivscc.gsfc.nasa.gov/publications/ar2000. This book and the web site are organized as follows: (1) The first section contains general information about IVS, a map showing the location of the components, information about the Directing Board members, and the report of the IVS Chair; (2) The second section of Special Reports contains a status report of the IVS Working Group on GPS phase center mapping, a reproduction of the resolution making IVS a Service of the International Astronomical Union (IAU), and a reprint of the VLBI Standard Interface (VSI); (3) The next seven sections hold the component reports from the Coordinators, Network Stations, Operation Centers, Correlators, Data Centers, Analysis Centers, and Technology Development Centers; and (4) The last section includes reference information about IVS: the Terms of Reference, the lists of Member and Affiliated organizations, the IVS Associate Member list, a complete list of IVS components, the list of institutions contributing to this report, and a list of acronyms. The 2000 Annual Report demonstrates the vitality of the IVS and the outstanding progress we have made during our first 22 months.
Accuracy assessment, using stratified plurality sampling, of portions of a LANDSAT classification of the Arctic National Wildlife Refuge Coastal Plain

NASA Technical Reports Server (NTRS)

Card, Don H.; Strong, Laurence L.

1989-01-01

An application of a classification accuracy assessment procedure is described for a vegetation and land cover map prepared by digital image processing of LANDSAT multispectral scanner data. A statistical sampling procedure called Stratified Plurality Sampling was used to assess the accuracy of portions of a map of the Arctic National Wildlife Refuge coastal plain. Results are tabulated as percent correct classification overall as well as per category with associated confidence intervals. Although values of percent correct were disappointingly low for most categories, the study was useful in highlighting sources of classification error and demonstrating shortcomings of the plurality sampling method.
Classification and area estimation of land covers in Kansas using ground-gathered and LANDSAT digital data

NASA Technical Reports Server (NTRS)

May, G. A.; Holko, M. L.; Anderson, J. E.

1983-01-01

Ground-gathered data and LANDSAT multispectral scanner (MSS) digital data from 1981 were analyzed to produce a classification of Kansas land areas into specific types called land covers. The land covers included rangeland, forest, residential, commercial/industrial, and various types of water. The analysis produced two outputs: acreage estimates with measures of precision, and map-type or photo products of the classification which can be overlaid on maps at specific scales. State-level acreage estimates were obtained and substate-level land cover classification overlays and estimates were generated for selected geographical areas. These products were found to be of potential use in managing land and water resources.
An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

USGS Publications Warehouse

Rey, Sergio J.; Stephens, Philip A.; Laura, Jason R.

2017-01-01

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets.
Binge eating disorder should be included in DSM-IV: a reply to Fairburn et al.'s "the classification of recurrent overeating: the binge eating disorder proposal".

PubMed

Spitzer, R L; Stunkard, A; Yanovski, S; Marcus, M D; Wadden, T; Wing, R; Mitchell, J; Hasin, D

1993-03-01

Extensive recent research supports a proposal that a new eating disorder, binge eating disorder (BED), be included in DSM-IV. BED criteria define a relatively pure group of individuals who are distressed by recurrent binge eating who do not exhibit the compensatory features of bulimia nervosa. This large number of patients currently can only be diagnosed as eating disorder not otherwise specified (EDNOS). Recognizing this new disorder will help stimulate research and clinical programs for these patients. Fairburn et al.'s critique of BED fails to acknowledge the large body of knowledge that indicates that BED represents a distinct and definable subgroup of eating disordered patients and that the diagnosis provides useful information about psychopathology, prognosis, and outcome (Fairburn, Welch, & Hay [in press]. The classification of recurrent overeating: The "binge eating disorder" proposal. International Journal of Eating Disorders.) Against any reasonable standard for adding a new diagnosis to DSM-IV, BED meets the test.
A study of the utilization of ERTS-1 data from the Wabash River Basin. [crop identification, water resources, urban land use, soil mapping, and atmospheric modeling

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The most significant results were obtained in the water resources research, urban land use mapping, and soil association mapping projects. ERTS-1 data was used to classify water bodies to determine acreages and high agreement was obtained with USGS figures. Quantitative evaluation was achieved of urban land use classifications from ERTS-1 data and an overall test accuracy of 90.3% was observed. ERTS-1 data classifications of soil test sites were compared with soil association maps scaled to match the computer produced map and good agreement was observed. In some cases the ERTS-1 results proved to be more accurate than the soil association map.
Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.
Mapping species of submerged aquatic vegetation with multi-seasonal satellite images and considering life history information

NASA Astrophysics Data System (ADS)

Luo, Juhua; Duan, Hongtao; Ma, Ronghua; Jin, Xiuliang; Li, Fei; Hu, Weiping; Shi, Kun; Huang, Wenjiang

2017-05-01

Spatial information of the dominant species of submerged aquatic vegetation (SAV) is essential for restoration projects in eutrophic lakes, especially eutrophic Taihu Lake, China. Mapping the distribution of SAV species is very challenging and difficult using only multispectral satellite remote sensing. In this study, we proposed an approach to map the distribution of seven dominant species of SAV in Taihu Lake. Our approach involved information on the life histories of the seven SAV species and eight distribution maps of SAV from February to October. The life history information of the dominant SAV species was summarized from the literature and field surveys. Eight distribution maps of the SAV were extracted from eight 30 m HJ-CCD images from February to October in 2013 based on the classification tree models, and the overall classification accuracies for the SAV were greater than 80%. Finally, the spatial distribution of the SAV species in Taihu in 2013 was mapped using multilayer erasing approach. Based on validation, the overall classification accuracy for the seven species was 68.4%, and kappa was 0.6306, which suggests that larger differences in life histories between species can produce higher identification accuracies. The classification results show that Potamogeton malaianus was the most widely distributed species in Taihu Lake, followed by Myriophyllum spicatum, Potamogeton maackianus, Potamogeton crispus, Elodea nuttallii, Ceratophyllum demersum and Vallisneria spiralis. The information is useful for planning shallow-water habitat restoration projects.
The Sloan Digital Sky Survey Reverberation Mapping Project: The C IV Blueshift, Its Variability, and Its Dependence Upon Quasar Properties

NASA Astrophysics Data System (ADS)

Sun, Mouyuan; Xue, Yongquan; Richards, Gordon T.; Trump, Jonathan R.; Shen, Yue; Brandt, W. N.; Schneider, D. P.

2018-02-01

We use the multi-epoch spectra of 362 quasars from the Sloan Digital Sky Survey Reverberation Mapping project to investigate the dependence of the blueshift of C IV relative to Mg II on quasar properties. We confirm that high-blueshift sources tend to have low C IV equivalent widths (EWs), and that the low-EW sources span a range of blueshift. Other high-ionization lines, such as He II, also show similar blueshift properties. The ratio of the line width (measured as both the full width at half maximum and the velocity dispersion) of C IV to that of Mg II increases with blueshift. Quasar variability enhances the connection between the C IV blueshift and quasar properties (e.g., EW). The variability of the Mg II line center (i.e., the wavelength that bisects the cumulative line flux) increases with blueshift. In contrast, the C IV line center shows weaker variability at the extreme blueshifts. Quasars with the high-blueshift C IV lines tend to have less variable continuum emission, when controlling for EW, luminosity, and redshift. Our results support the scenario that high-blueshift sources tend to have large Eddington ratios.
Digital classification of Landsat data for vegetation and land-cover mapping in the Blackfoot River watershed, southeastern Idaho

USGS Publications Warehouse

Pettinger, L.R.

1982-01-01

This paper documents the procedures, results, and final products of a digital analysis of Landsat data used to produce a vegetation and landcover map of the Blackfoot River watershed in southeastern Idaho. Resource classes were identified at two levels of detail: generalized Level I classes (for example, forest land and wetland) and detailed Levels II and III classes (for example, conifer forest, aspen, wet meadow, and riparian hardwoods). Training set statistics were derived using a modified clustering approach. Environmental stratification that separated uplands from lowlands improved discrimination between resource classes having similar spectral signatures. Digital classification was performed using a maximum likelihood algorithm. Classification accuracy was determined on a single-pixel basis from a random sample of 25-pixel blocks. These blocks were transferred to small-scale color-infrared aerial photographs, and the image area corresponding to each pixel was interpreted. Classification accuracy, expressed as percent agreement of digital classification and photo-interpretation results, was 83.0:t 2.1 percent (0.95 probability level) for generalized (Level I) classes and 52.2:t 2.8 percent (0.95 probability level) for detailed (Levels II and III) classes. After the classified images were geometrically corrected, two types of maps were produced of Level I and Levels II and III resource classes: color-coded maps at a 1:250,000 scale, and flatbed-plotter overlays at a 1:24,000 scale. The overlays are more useful because of their larger scale, familiar format to users, and compatibility with other types of topographic and thematic maps of the same scale.
Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.
Cross-mapping the ICNP with NANDA, HHCC, Omaha System and NIC for unified nursing language system development. International Classification for Nursing Practice. International Council of Nurses. North American Nursing Diagnosis Association. Home Health Care Classification. Nursing Interventions Classification.

PubMed

Hyun, S; Park, H A

2002-06-01

Nursing language plays an important role in describing and defining nursing phenomena and nursing actions. There are numerous vocabularies describing nursing diagnoses, interventions and outcomes in nursing. However, the lack of a standardized unified nursing language is considered a problem for further development of the discipline of nursing. In an effort to unify the nursing languages, the International Council of Nurses (ICN) has proposed the International Classification for Nursing Practice (ICNP) as a unified nursing language system. The purpose of this study was to evaluate the inclusiveness and expressiveness of the ICNP terms by cross-mapping them with the existing nursing terminologies, specifically the North American Nursing Diagnosis Association (NANDA) taxonomy I, the Omaha System, the Home Health Care Classification (HHCC) and the Nursing Interventions Classification (NIC). Nine hundred and seventy-four terms from these four classifications were cross-mapped with the ICNP terms. This was performed in accordance with the Guidelines for Composing a Nursing Diagnosis and Guidelines for Composing a Nursing Intervention, which were suggested by the ICNP development team. An expert group verified the results. The ICNP Phenomena Classification described 87.5% of the NANDA diagnoses, 89.7% of the HHCC diagnoses and 72.7% of the Omaha System problem classification scheme. The ICNP Action Classification described 79.4% of the NIC interventions, 80.6% of the HHCC interventions and 71.4% of the Omaha System intervention scheme. The results of this study suggest that the ICNP has a sound starting structure for a unified nursing language system and can be used to describe most of the existing terminologies. Recommendations for the addition of terms to the ICNP are provided.
Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Supervised classification of continental shelf sediment off western Donegal, Ireland

NASA Astrophysics Data System (ADS)

Monteys, X.; Craven, K.; McCarron, S. G.

2017-12-01

Managing human impacts on marine ecosystems requires natural regions to be identified and mapped over a range of hierarchically nested scales. In recent years (2000-present) the Irish National Seabed Survey (INSS) and Integrated Mapping for the Sustainable Development of Ireland's Marine Resources programme (INFOMAR) (Geological Survey Ireland and Marine Institute collaborations) has provided unprecedented quantities of high quality data on Ireland's offshore territories. The increasing availability of large, detailed digital representations of these environments requires the application of objective and quantitative analyses. This study presents results of a new approach for sea floor sediment mapping based on an integrated analysis of INFOMAR multibeam bathymetric data (including the derivatives of slope and relative position), backscatter data (including derivatives of angular response analysis) and sediment groundtruthing over the continental shelf, west of Donegal. It applies a Geographic-Object-Based Image Analysis software package to provide a supervised classification of the surface sediment. This approach can provide a statistically robust, high resolution classification of the seafloor. Initial results display a differentiation of sediment classes and a reduction in artefacts from previously applied methodologies. These results indicate a methodology that could be used during physical habitat mapping and classification of marine environments.
Ecosystem services provided by a complex coastal region: challenges of classification and mapping.

PubMed

Sousa, Lisa P; Sousa, Ana I; Alves, Fátima L; Lillebø, Ana I

2016-03-11

A variety of ecosystem services classification systems and mapping approaches are available in the scientific and technical literature, which needs to be selected and adapted when applied to complex territories (e.g. in the interface between water and land, estuary and sea). This paper provides a framework for addressing ecosystem services in complex coastal regions. The roadmap comprises the definition of the exact geographic boundaries of the study area; the use of CICES (Common International Classification of Ecosystem Services) for ecosystem services identification and classification; and the definition of qualitative indicators that will serve as basis to map the ecosystem services. Due to its complexity, the Ria de Aveiro coastal region was selected as case study, presenting an opportunity to explore the application of such approaches at a regional scale. The main challenges of implementing the proposed roadmap, together with its advantages are discussed in this research. The results highlight the importance of considering both the connectivity of natural systems and the complexity of the governance framework; the flexibility and robustness, but also the challenges when applying CICES at regional scale; and the challenges regarding ecosystem services mapping.
Ecosystem services provided by a complex coastal region: challenges of classification and mapping

PubMed Central

Sousa, Lisa P.; Sousa, Ana I.; Alves, Fátima L.; Lillebø, Ana I.

2016-01-01

A variety of ecosystem services classification systems and mapping approaches are available in the scientific and technical literature, which needs to be selected and adapted when applied to complex territories (e.g. in the interface between water and land, estuary and sea). This paper provides a framework for addressing ecosystem services in complex coastal regions. The roadmap comprises the definition of the exact geographic boundaries of the study area; the use of CICES (Common International Classification of Ecosystem Services) for ecosystem services identification and classification; and the definition of qualitative indicators that will serve as basis to map the ecosystem services. Due to its complexity, the Ria de Aveiro coastal region was selected as case study, presenting an opportunity to explore the application of such approaches at a regional scale. The main challenges of implementing the proposed roadmap, together with its advantages are discussed in this research. The results highlight the importance of considering both the connectivity of natural systems and the complexity of the governance framework; the flexibility and robustness, but also the challenges when applying CICES at regional scale; and the challenges regarding ecosystem services mapping. PMID:26964892
Classifying the Diversity of Bus Mapping Systems

NASA Astrophysics Data System (ADS)

Said, Mohd Shahmy Mohd; Forrest, David

2018-05-01

This study represents the first stage of an investigation into understanding the nature of different approaches to mapping bus routes and bus network, and how they may best be applied in different public transport situations. In many cities, bus services represent an important facet of easing traffic congestion and reducing pollution. However, with the entrenched car culture in many countries, persuading people to change their mode of transport is a major challenge. To promote this modal shift, people need to know what services are available and where (and when) they go. Bus service maps provide an invaluable element of providing suitable public transport information, but are often overlooked by transport planners, and are under-researched by cartographers. The method here consists of the creation of a map evaluation form and performing assessment of published bus networks maps. The analyses were completed by a combination of quantitative and qualitative data analysis of various aspects of cartographic design and classification. This paper focuses on the resulting classification, which is illustrated by a series of examples. This classification will facilitate more in depth investigations into the details of cartographic design for such maps and help direct areas for user evaluation.

Actions of the fall prevention protocol: mapping with the classification of nursing interventions.

PubMed

Alves, Vanessa Cristina; Freitas, Weslen Carlos Junior de; Ramos, Jeferson Silva; Chagas, Samantha Rodrigues Garbis; Azevedo, Cissa; Mata, Luciana Regina Ferreira da

2017-12-21

to analyze the correspondence between the actions contained in the fall prevention protocol of the Ministry of Health and the Nursing Interventions Classification (NIC) by a cross-mapping. this is a descriptive study carried out in four stages: protocol survey, identification of NIC interventions related to nursing diagnosis, the risk of falls, cross-mapping, and validation of the mapping from the Delphi technique. there were 51 actions identified in the protocol and 42 interventions in the NIC. Two rounds of mapping evaluation were carried out by the experts. There were 47 protocol actions corresponding to 25 NIC interventions. The NIC interventions that presented the highest correspondence with protocol actions were: fall prevention, environmental-safety control, and risk identification. Regarding the classification of similarity and comprehensiveness of the 47 actions of the protocol mapped, 44.7% were considered more detailed and specific than the NIC, 29.8% less specific than the NIC and 25.5% were classified as similar in significance to the NIC. most of the actions contained in the protocol are more specific and detailed, however, the NIC contemplates a greater diversity of interventions and may base a review of the protocol to increase actions related to falls prevention..
[Joint endoprosthesis pathology. Histopathological diagnostics and classification].

PubMed

Krenn, V; Morawietz, L; Jakobs, M; Kienapfel, H; Ascherl, R; Bause, L; Kuhn, H; Matziolis, G; Skutek, M; Gehrke, T

2011-05-01

Prosthesis durability has steadily increased with high 10-year rates of 88-95%. However, four pathogenetic groups of diseases can decrease prosthesis durability: (1) periprosthetic wear particle disease (aseptic loosening) (2) bacterial infection (septic loosening) (3) periprosthetic ossification, and (4) arthrofibrosis. The histopathological "extended consensus classification of periprosthetic membranes" includes four types of membranes, arthrofibrosis, and osseous diseases of endoprosthetics: The four types of neosynovia are: wear particle-induced type (type I), mean prosthesis durability (MPD) in years 12.0; infectious type (type II), MPD 2.5; combined type (type III) MPD 4.2; and indeterminate type (type IV), MPD 5.5. Arthrofibrosis can be determined in three grades: grade 1 needs clinical information to be differentiated from a type IV membrane, and grades 2 & 3 can be diagnosed histopathologically. Periprosthetic ossification, osteopenia-induced fractures, and aseptic osteonecrosis can be histopathologically diagnosed safely with clinical information. The extended consensus classification of periprosthetic membranes may be a diagnostic groundwork for a future national endoprosthesis register.
Pattern of Cortical Fracture following Corticotomy for Distraction Osteogenesis

PubMed Central

Luvan, M; Roshan, G; Saw, A

2015-01-01

Corticotomy is an essential procedure for deformity correction and there are many techniques described. However there is no proper classification of the fracture pattern resulting from corticotomies to enable any studies to be conducted. We performed a retrospective study of corticotomy fracture patterns in 44 patients (34 tibias and 10 femurs) performed for various indications. We identified four distinct fracture patterns, Type I through IV classification based on the fracture propagation following percutaneous corticotomy. Type I transverse fracture, Type II transverse fracture with a winglet, Type III presence of butterfly fragment and Type IV fracture propagation to a fixation point. No significant correlation was noted between the fracture pattern and the underlying pathology or region of corticotomy. PMID:28611907
Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

NASA Astrophysics Data System (ADS)

Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

2015-03-01

The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.
Evaluating the Generality and Limits of Blind Return-Oriented Programming Attacks

DTIC Science & Technology

2015-12-01

consider a recently proposed information disclosure vulnerability called blind return-oriented programming (BROP). Under certain conditions, this...implementation disclosure attacks 15. NUMBER OF PAGES 75 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF...Science iii THIS PAGE INTENTIONALLY LEFT BLANK iv ABSTRACT We consider a recently proposed information disclosure vulnerability called blind return
Feature generation and representations for protein-protein interaction classification.

PubMed

Lan, Man; Tan, Chew Lim; Su, Jian

2009-10-01

Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.
Identification and Mapping of Tree Species in Urban Areas Using WORLDVIEW-2 Imagery

NASA Astrophysics Data System (ADS)

Mustafa, Y. T.; Habeeb, H. N.; Stein, A.; Sulaiman, F. Y.

2015-10-01

Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree species were identified and mapped at a satisfactory accuracy in urban areas of this study.
Spectroscopic parallaxes of MAP region stars from UBVRI, DDO, and uvbyH-beta photometry. [Multichannel Astrometric Photometer for astronomical observation

NASA Technical Reports Server (NTRS)

Persinger, Tim; Castelaz, Michael W.

1990-01-01

This paper presents the results of spectral type and luminosity classification of reference stars in the Allegheny Observatory MAP parallax program, using broadband and intermediate-band photometry. In addition to the use of UBVRI and DDO photometric systems, the uvbyH-beta photometric system was included for classification of blue (B - V less than 0.6) reference stars. The stellar classifications made from the photometry are used to determine spectroscopic parallaxes. The spectroscopic parallaxes are used in turn to adjust the relative parallaxes measured with the MAP to absolute parallaxes. A new method for dereddening stars using more than one photometric system is presented. In the process of dereddening, visual extinctions, spectral types, and luminosity classes are determined, as well as a measure of the goodness of fit. The measure of goodness of fit quantifies confidence in the stellar classifications. It is found that the spectral types are reliable to within 2.5 spectral subclasses.
Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Postadjian, T.; Le Bris, A.; Sahbi, H.; Mallet, C.

2017-05-01

Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.
Revision of the International Society of Nephrology/Renal Pathology Society classification for lupus nephritis: clarification of definitions, and modified National Institutes of Health activity and chronicity indices.

PubMed

Bajema, Ingeborg M; Wilhelmus, Suzanne; Alpers, Charles E; Bruijn, Jan A; Colvin, Robert B; Cook, H Terence; D'Agati, Vivette D; Ferrario, Franco; Haas, Mark; Jennette, J Charles; Joh, Kensuke; Nast, Cynthia C; Noël, Laure-Hélène; Rijnink, Emilie C; Roberts, Ian S D; Seshan, Surya V; Sethi, Sanjeev; Fogo, Agnes B

2018-04-01

We present a consensus report pertaining to the improved clarity of definitions and classification of glomerular lesions in lupus nephritis that derived from a meeting of 18 members of an international nephropathology working group in Leiden, Netherlands, in 2016. Here we report detailed recommendations on issues for which we can propose adjustments based on existing evidence and current consensus opinion (phase 1). New definitions are provided for mesangial hypercellularity and for cellular, fibrocellular, and fibrous crescents. The term "endocapillary proliferation" is eliminated and the definition of endocapillary hypercellularity considered in some detail. We also eliminate the class IV-S and IV-G subdivisions of class IV lupus nephritis. The active and chronic designations for class III/IV lesions are replaced by a proposal for activity and chronicity indices that should be applied to all classes. In the activity index, we include fibrinoid necrosis as a specific descriptor. We also make recommendations on issues for which there are limited data at present and that can best be addressed in future studies (phase 2). We propose to proceed to these investigations, with clinicopathologic studies and tests of interobserver reproducibility to evaluate the applications of the proposed definitions and to classify lupus nephritis lesions. Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
Feature Extraction and Classification of Magnetic and EMI Data, Camp Beale, CA

DTIC Science & Technology

2012-05-01

and non-specialists. However, as part of ESTCP 1004 we are presently working on transitioning our inversion algorithms to an API that will be...10 0 Time (ms) Cell 663 - Target 1965 - Model 1 (SOI) ISO IVS 0.001 0.005 10 0 Time (ms) Cell 1104 - Target 2532 - Model 1 (SOI) ISO IVS...0.0 1 0.005 10 0 Time (ms) Cell 663 - Target 1965 - Model 1 (SOI) ISO IVS 0.0 1 0.005 10 0 Time (ms) Cell 1104 - Target 2532 - Model 1 (SOI
Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.
Cross-domain question classification in community question answering via kernel mapping

NASA Astrophysics Data System (ADS)

Su, Lei; Hu, Zuoliang; Yang, Bin; Li, Yiyang; Chen, Jun

2015-10-01

An increasingly popular method for retrieving information is via the community question answering (CQA) systems such as Yahoo! Answers and Baidu Knows. In CQA, question classification plays an important role to find the answers. However, the labeled training examples for statistical question classifier are fairly expensive to obtain, as they require the experienced human efforts. Meanwhile, unlabeled data are readily available. This paper employs the method of domain adaptation via kernel mapping to solve this problem. In detail, the kernel approach is utilized to map the target-domain data and the source-domain data into a common space, where the question classifiers are trained under the closer conditional probabilities. The kernel mapping function is constructed by domain knowledge. Therefore, domain knowledge could be transferred from the labeled examples in the source domain to the unlabeled ones in the targeted domain. The statistical training model can be improved by using a large number of unlabeled data. Meanwhile, the Hadoop Platform is used to construct the mapping mechanism to reduce the time complexity. Map/Reduce enable kernel mapping for domain adaptation in parallel in the Hadoop Platform. Experimental results show that the accuracy of question classification could be improved by the method of kernel mapping. Furthermore, the parallel method in the Hadoop Platform could effective schedule the computing resources to reduce the running time.
Towards the Optimal Pixel Size of dem for Automatic Mapping of Landslide Areas

NASA Astrophysics Data System (ADS)

Pawłuszek, K.; Borkowski, A.; Tarolli, P.

2017-05-01

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification.
Level III and IV Ecoregions of the Continental United States

EPA Pesticide Factsheets

Information and downloadable maps and datasets for Level III and IV ecoregions of the continental United States. Ecoregions are areas of general similarity in the type, quality, and quantity of environmental resources.
Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073
Cardiovascular tolerance of intravenous bupivacaine in broiler chickens (Gallus gallus domesticus) anesthetized with isoflurane.

PubMed

DiGeronimo, Peter M; da Cunha, Anderson F; Pypendop, Bruno; Brandão, João; Stout, Rhett; Rinaldi, Max; Tully, Thomas N

2017-03-01

To determine the median effective dose (ED 50 ) of intravenous (IV) bupivacaine associated with a 50% probability of causing clinically relevant cardiovascular effects [defined as 30% change in heart rate (HR) or mean arterial pressure (MAP)] in chickens anesthetized with isoflurane. Randomized up-and-down study. A total of 14 Ross-708 broiler chickens (Gallus gallus domesticus) weighing 1.70-2.75 kg. Anesthesia was induced and maintained with isoflurane. Monitoring included the electrocardiogram and invasive arterial pressures. Chickens were administered bupivacaine IV over 2 minutes using a dose based on the response of the previous animal. Dose was decreased when HR and/or MAP in the previous animal increased or decreased ≥30% after bupivacaine administration, or increased when HR or MAP changed <30%. The ED 50 was defined as the dose resulting in ≥30% variation in HR or MAP in 50% of the population studied. The IV ED 50 of bupivacaine was 1.94 mg kg -1 using Dixon's up-and-down method and 1.96 mg kg -1 by logistic regression. These results suggest that 1.33 and 1.96 mg kg -1 of IV bupivacaine are associated with a respective 1 or 50% probability of a clinically significant change in MAP in isoflurane-anesthetized chickens. Identification of the cardiovascular changes associated with different doses of bupivacaine can be used as the basis for studies of therapeutic applications in the domestic chicken. Further studies are required to determine interspecies variation. Published by Elsevier Ltd.
The intercrater plains of Mercury and the Moon: Their nature, origin and role in terrestrial planet evolution. Geologic mapping of Mercury and the Moon. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Leake, M. A.

1982-01-01

The geologic framework of the intercrater plains on Mercury and the Moon as determined through geologic mapping is presented. The strategies used in such mapping are discussed first. Then, because the degree of crater degradation is applied to both mapping and crater statistics, the correlation of degradation classification of lunar and Mercurian craters is thoroughly addressed. Different imaging systems can potentially affect this classification, and are therefore also discussed. The techniques used in mapping Mercury are discussed in Section 2, followed by presentation of the Geologic Map of Mercury in Section 3. Material units, structures, and relevant albedo and color data are discussed therein. Preliminary conclusions regarding plains' origins are given there. The last section presents the mapping analyses of the lunar intercrater plains, including tentative conclusions of their origin.
Diagnostic Classification of Eating Disorders in Children and Adolescents: How Does DSM-IV-TR Compare to Empirically-Derived Categories?

ERIC Educational Resources Information Center

Eddy, Kamryn T.; Le Grange, Daniel; Crosby, Ross D.; Hoste, Renee Rienecke; Doyle, Angela Celio; Smyth, Angela; Herzog, David B.

2010-01-01

Objective: The purpose of this study was to empirically derive eating disorder phenotypes in a clinical sample of children and adolescents using latent profile analysis (LPA), and to compare these latent profile (LP) groups to the DSM-IV-TR eating disorder categories. Method: Eating disorder symptom data collected from 401 youth (aged 7 through 19…
A note on harmonic quasiconformal mappings

NASA Astrophysics Data System (ADS)

Chen, Xingdi; Fang, Ainong

2008-12-01

In this note we show that a harmonic quasiconformal mapping f=u+iv with respect to the Poincaré metric of the upper half plane onto itself such that v(x,y)=v(y) or u(x,y)=u(x) is a conformal mapping.

Forest and range mapping in the Houston area with ERTS-1

NASA Technical Reports Server (NTRS)

Heath, G. R.; Parker, H. D.

1973-01-01

ERTS-1 data acquired over the Houston area has been analyzed for applications to forest and range mapping. In the field of forestry the Sam Houston National Forest (Texas) was chosen as a test site, (Scene ID 1037-16244). Conventional imagery interpretation as well as computer processing methods were used to make classification maps of timber species, condition and land-use. The results were compared with timber stand maps which were obtained from aircraft imagery and checked in the field. The preliminary investigations show that conventional interpretation techniques indicated an accuracy in classification of 63 percent. The computer-aided interpretations made by a clustering technique gave 70 percent accuracy. Computer-aided and conventional multispectral analysis techniques were applied to range vegetation type mapping in the gulf coast marsh. Two species of salt marsh grasses were mapped.
Single classifier, OvO, OvA and RCC multiclass classification method in handheld based smartphone gait identification

NASA Astrophysics Data System (ADS)

Raziff, Abdul Rafiez Abdul; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Gait recognition is widely used in many applications. In the application of the gait identification especially in people, the number of classes (people) is many which may comprise to more than 20. Due to the large amount of classes, the usage of single classification mapping (direct classification) may not be suitable as most of the existing algorithms are mostly designed for the binary classification. Furthermore, having many classes in a dataset may result in the possibility of having a high degree of overlapped class boundary. This paper discusses the application of multiclass classifier mappings such as one-vs-all (OvA), one-vs-one (OvO) and random correction code (RCC) on handheld based smartphone gait signal for person identification. The results is then compared with a single J48 decision tree for benchmark. From the result, it can be said that using multiclass classification mapping method thus partially improved the overall accuracy especially on OvO and RCC with width factor more than 4. For OvA, the accuracy result is worse than a single J48 due to a high number of classes.
Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification.

PubMed

Rifai Chai; Naik, Ganesh R; Sai Ho Ling; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-07-01

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.
Detection and quantification by deformation imaging of the functional impact of septal compared to free wall preexcitation in the Wolff-Parkinson-White syndrome.

PubMed

De Boeck, Bart W L; Teske, Arco J; Leenders, Geert E; Mohamed Hoesein, Firdaus A A; Loh, Peter; van Driel, Vincent J; Doevendans, Pieter A; Prinzen, Frits W; Cramer, Maarten J

2010-08-15

Pacing experiments in healthy animal hearts have suggested a larger detrimental effect of septal compared to free wall preexcitation. We investigated the intrinsic relation among the site of electrical preexcitation, mechanical dyssynchrony, and dysfunction in human patients. In 33 patients with Wolff-Parkinson-White (WPW) syndrome and 18 controls, regional myocardial deformation was assessed by speckle tracking mapping (ST-Map) to assess the preexcitation site, shortening sequences and dyssynchrony, and the extent of local and global ejecting shortening. The ST-Map data in patients with accessory atrioventricular pathways correctly diagnosed as located in the interventricular septum (IVS) (n = 11) or left ventricular free wall (LFW) (n = 12) were compared to the corresponding control values. A local ejecting shortening of <2 SD of the control values identified hypokinetic segments. The localization of the atrioventricular pathways by ST-Map matched with the invasive electrophysiology findings in 23 of 33 patients and was one segment different in 5 of 33 patients. In both WPW-IVS and WPW-LFW, local ejecting shortening was impaired at the preexcitation site (p <0.01). However, at similar electrical and mechanical dyssynchrony, WPW-IVS had more extensive hypokinesia than did WPW-LFW (3.6 +/- 0.9 vs 1.8 +/- 1.3 segments, p <0.01). Compared to controls, the left ventricular function was significantly reduced only in WPW-IVS (global ejecting shortening 17 +/- 2% vs 19 +/- 2%, p = 0.01; ejection fraction 55 +/- 5% vs 59 +/- 3%, p = 0.02). In conclusion, preexcitation is associated with local hypokinesia, which at comparable preexcitation is more extensive in WPW-IVS than in WPW-LFW and could adversely affect ventricular function. ST-Map might have a future role in detecting and guiding treatment of septal pathways with significant mechanical effects.
Generalized Rainich conditions, generalized stress-energy conditions, and the Hawking-Ellis classification

NASA Astrophysics Data System (ADS)

Martín–Moruno, Prado; Visser, Matt

2017-11-01

The (generalized) Rainich conditions are algebraic conditions which are polynomial in the (mixed-component) stress-energy tensor. As such they are logically distinct from the usual classical energy conditions (NEC, WEC, SEC, DEC), and logically distinct from the usual Hawking-Ellis (Segré-Plebański) classification of stress-energy tensors (type I, type II, type III, type IV). There will of course be significant inter-connections between these classification schemes, which we explore in the current article. Overall, we shall argue that it is best to view the (generalized) Rainich conditions as a refinement of the classical energy conditions and the usual Hawking-Ellis classification.
Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

PubMed

Sabr, Abutaleb; Moeinaddini, Mazaher; Azarnivand, Hossein; Guinot, Benjamin

2016-12-01

In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.
A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.
Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1993-01-01

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.
Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Piiroinen, Rami; Heiskanen, Janne; Mõttus, Matti; Pellikka, Petri

2015-07-01

Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. The objective of this study was to assess the prospects of mapping the common agricultural crops in highly heterogeneous study area in south-eastern Kenya using high spatial and spectral resolution AisaEAGLE imaging spectroscopy data. Minimum noise fraction transformation was used to pack the coherent information in smaller set of bands and the data was classified with support vector machine (SVM) algorithm. A total of 35 plant species were mapped in the field and seven most dominant ones were used as classification targets. Five of the targets were agricultural crops. The overall accuracy (OA) for the classification was 90.8%. To assess the possibility of excluding the remaining 28 plant species from the classification results, 10 different probability thresholds (PT) were tried with SVM. The impact of PT was assessed with validation polygons of all 35 mapped plant species. The results showed that while PT was increased more pixels were excluded from non-target polygons than from the polygons of the seven classification targets. This increased the OA and reduced salt-and-pepper effects in the classification results. Very high spatial resolution imagery and pixel-based classification approach worked well with small targets such as maize while there was mixing of classes on the sides of the tree crowns.
Visual classification of medical data using MLP mapping.

PubMed

Cağatay Güler, E; Sankur, B; Kahya, Y P; Raudys, S

1998-05-01

In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.
Effects of intravenous and topical laryngeal lidocaine on heart rate, mean arterial pressure and cough response to endotracheal intubation in dogs.

PubMed

Thompson, Kate R; Rioja, Eva

2016-07-01

To compare the effects of intravenous (IV) and topical laryngeal lidocaine on heart rate (HR), mean arterial pressure (MAP) and cough response to endotracheal intubation (ETI) in dogs. Prospective, randomized, blinded clinical study. Forty-two client-owned dogs (American Society of Anesthesiologists class I and II status) undergoing elective orthopaedic surgery. Dogs were randomized to three groups. Dogs in group SALIV received 0.1 mL kg(-1) IV saline. Dogs in group LIDIV received 2 mg kg(-1) IV 2% lidocaine. Dogs in group LIDTA received 0.4 mg kg(-1) topically sprayed laryngeal 2% lidocaine. All dogs were premedicated with methadone (0.2 mg kg(-1) IV). After 30 minutes, IV propofol was administered to abolish the lateral palpebral reflex and produce jaw relaxation. The allocated treatment was then administered and, after 30 seconds, further propofol was administered to abolish the medial palpebral reflex and facilitate ETI. HR and MAP were measured at four time-points using cardiac auscultation and automated oscillometry, respectively. The cough response at ETI was recorded. One-way anova and post hoc Tukey adjustment were used to analyse parametric data. The Kruskal-Wallis test was used to analyse non-parametric data. Odds ratios were calculated for the cough response. A p-value of ≤0.05 was considered to indicate statistical significance. In response to ETI, changes in MAP differed significantly between groups. In SALIV, MAP increased (4 ± 6 mmHg), whereas it decreased in LIDIV (6 ± 13 mmHg) (p = 0.013) and LIDTA (7 ± 11 mmHg) (p = 0.003). Dogs in SALIV were almost 10 times more likely to cough than dogs in LIDIV (odds ratio 9.75, 95% confidence interval 0.98-96.60; p = 0.05). In propofol-anaesthetized dogs, IV and topical laryngeal lidocaine attenuated the pressor response to ETI, whereas IV lidocaine reduced the cough response. © 2015 Association of Veterinary Anaesthetists and the American College of Veterinary Anesthesia and Analgesia.
CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data.

PubMed

Minhas, Fayyaz Ul Amir Afsar; Asif, Amina; Arif, Muhammad

2016-12-01

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples. CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper. Copyright © 2016 Elsevier Ltd. All rights reserved.
Validity of the Children's Social Behavior Questionnaire (CSBQ) in Children with Intellectual Disability: Comparing the CSBQ with ADI-R, ADOS, and Clinical DSM-IV-TR Classification

ERIC Educational Resources Information Center

de Bildt, Annelies; Mulder, Erik J.; Hoekstra, Pieter J.; van Lang, Natasja D. J.; Minderaa, Ruud B.; Hartman, Catharina A.

2009-01-01

The Children's Social Behavior Questionnaire (CSBQ) was compared with the Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS), and clinical classification in children with mild and moderate intellectual disability (ID), to investigate its criterion related validity. The contribution of the CSBQ to a…
Performance of the Delirium Rating Scale-Revised-98 Against Different Delirium Diagnostic Criteria in a Population With a High Prevalence of Dementia.

PubMed

Sepulveda, Esteban; Franco, José G; Trzepacz, Paula T; Gaviria, Ana M; Viñuelas, Eva; Palma, José; Ferré, Gisela; Grau, Imma; Vilella, Elisabet

2015-01-01

Delirium diagnosis in elderly is often complicated by underlying dementia. We evaluated performance of the Delirium Rating Scale-Revised-98 (DRS-R98) in patients with high dementia prevalence and also assessed concordance among past and current diagnostic criteria for delirium. Cross-sectional analysis of newly admitted patients to a skilled nursing facility over 6 months, who were rated within 24-48 hours after admission. Interview for Diagnostic and Statistical Manual of Mental Disorders, 3rd edition-R (DSM)-III-R, DSM-IV, DSM-5, and International Classification of Diseases 10th edition delirium ratings, administration of the DRS-R98, and assessment of dementia using the Informant Questionnaire on Cognitive Decline in the Elderly were independently performed by 3 researchers. Discriminant analyses (receiver operating characteristics curves) were used to study DRS-R98 accuracy against different diagnostic criteria. Hanley and McNeil test compared the area under the curve for DRS-R98's discriminant performance for all diagnostic criteria. Dementia was present in 85/125 (68.0%) subjects, and 36/125 (28.8%) met criteria for delirium by at least 1 classification system, whereas only 19/36 (52.8%) did by all. DSM-III-R diagnosed the most as delirious (27.2%), followed by DSM-5 (24.8%), DSM-IV-TR (22.4%), and International Classification of Diseases 10th edition (16%). DRS-R98 had the highest AUC when discriminating DSM-III-R delirium (92.9%), followed by DSM-IV (92.4%), DSM-5 (91%), and International Classification of Diseases 10th edition (90.5%), without statistical differences among them. The best DRS-R98 cutoff score was ≥14.5 for all diagnostic systems except International Classification of Diseases 10th edition (≥15.5). There is a low concordance across diagnostic systems for identification of delirium. The DRS-R98 performs well despite differences across classification systems perhaps because it broadly assesses phenomenology, even in this population with a high prevalence of dementia. Copyright © 2015 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.
23 CFR 470.105 - Urban area boundaries and highway functional classification.

Code of Federal Regulations, 2010 CFR

2010-04-01

... classification. 470.105 Section 470.105 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION... criteria and procedures are provided in the FHWA publication “Highway Functional Classification—Concepts... functional classification shall be mapped and submitted to the Federal Highway Administration (FHWA) for...
A new tool for supervised classification of satellite images available on web servers: Google Maps as a case study

NASA Astrophysics Data System (ADS)

García-Flores, Agustín.; Paz-Gallardo, Abel; Plaza, Antonio; Li, Jun

2016-10-01

This paper describes a new web platform dedicated to the classification of satellite images called Hypergim. The current implementation of this platform enables users to perform classification of satellite images from any part of the world thanks to the worldwide maps provided by Google Maps. To perform this classification, Hypergim uses unsupervised algorithms like Isodata and K-means. Here, we present an extension of the original platform in which we adapt Hypergim in order to use supervised algorithms to improve the classification results. This involves a significant modification of the user interface, providing the user with a way to obtain samples of classes present in the images to use in the training phase of the classification process. Another main goal of this development is to improve the runtime of the image classification process. To achieve this goal, we use a parallel implementation of the Random Forest classification algorithm. This implementation is a modification of the well-known CURFIL software package. The use of this type of algorithms to perform image classification is widespread today thanks to its precision and ease of training. The actual implementation of Random Forest was developed using CUDA platform, which enables us to exploit the potential of several models of NVIDIA graphics processing units using them to execute general purpose computing tasks as image classification algorithms. As well as CUDA, we use other parallel libraries as Intel Boost, taking advantage of the multithreading capabilities of modern CPUs. To ensure the best possible results, the platform is deployed in a cluster of commodity graphics processing units (GPUs), so that multiple users can use the tool in a concurrent way. The experimental results indicate that this new algorithm widely outperform the previous unsupervised algorithms implemented in Hypergim, both in runtime as well as precision of the actual classification of the images.
CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.
A Proposal for a Dimensional Classification System Based on the Shared Features of the DSM-IV Anxiety and Mood Disorders: Implications for Assessment and Treatment

PubMed Central

Brown, Timothy A.; Barlow, David H.

2010-01-01

A wealth of evidence attests to the extensive current and lifetime diagnostic comorbidity of the DSM-IV anxiety and mood disorders. Research has shown that the considerable cross-sectional covariation of DSM-IV emotional disorders is accounted for by common higher-order dimensions such as neuroticism/behavioral inhibition (N/BI) and low positive affect/behavioral activation. Longitudinal studies have indicated that the temporal covariation of these disorders can be explained by changes in N/BI and in some cases, initial levels of N/BI are predictive of the temporal course of emotional disorders. Moreover, the marked phenotypal overlap of the DSM-IV anxiety and mood disorder constructs is a frequent source of diagnostic unreliability (e.g., temporal overlap in the shared features of generalized anxiety disorder and mood disorders, situation specificity of panic attacks in panic disorder and specific phobia). Although dimensional approaches have been considered as a method to address the drawbacks associated with the extant prototypical nosology (e.g., inadequate assessment of individual differences in disorder severity), these proposals do not reconcile key problems in current classification such as modest reliability and high comorbidity. The current paper considers an alternative approach that emphasizes empirically supported common dimensions of emotional disorders over disorder-specific criteria sets. The selection and assessment of these dimensions are discussed along with how these methods could be implemented to promote more reliable and valid diagnosis, prognosis, and treatment planning. For instance, the advantages of this classification system are discussed in context of current transdiagnostic treatment protocols that are efficaciously applied to a variety of disorders by targeting their shared features. PMID:19719339
A new multi-scale geomorphological landscape GIS for the Netherlands

NASA Astrophysics Data System (ADS)

Weerts, Henk; Kosian, Menne; Baas, Henk; Smit, Bjorn

2013-04-01

At present, the Cultural Heritage Agency of the Netherlands is developing a nationwide landscape Geographical Information System (GIS). In this new conceptual approach, the Agency puts together several multi-scale landscape classifications in a GIS. The natural physical landscapes lie at the basis of this GIS, because these landscapes provide the natural boundary conditions for anthropogenic. At the local scale a nationwide digital geomorphological GIS is available in the Netherlands. This map, that was originally mapped at 1:50,000 from the late 1970's to the 1990's, is based on geomorphometrical (observable and measurable in the field), geomorphological and, lithological and geochronological criteria. When used at a national scale, the legend of this comprehensive geomorphological map is very complex which hampers use in e.g. planning practice or predictive archaeology. At the national scale several landscape classifications have been in use in the Netherlands since the early 1950's, typically ranging in the order of 10 -15 landscape units for the entire country. A widely used regional predictive archaeological classification has 13 archaeo-landscapes. All these classifications have been defined "top-down" and their actual content and boundaries have only been broadly defined. Thus, these classifications have little or no meaning at a local scale. We have tried to combine the local scale with the national scale. To do so, we first defined national physical geographical regions based on the new 2010 national geological map 1:500,000. We also made sure there was a reference with the European LANMAP2 classification. We arrived at 20 landscape units at the national scale, based on (1) genesis, (2) large-scale geomorphology, (3) lithology of the shallow sub-surface and (4) age. These criteria that were chosen because the genesis of the landscape largely determines its (scale of) morphology and lithology that in turn determine hydrological conditions. All together, they define the natural boundary conditions for anthropogenic use. All units have been defined, mapped and described based on these criteria. This enables the link with the European LANMAP2 GIS. The unit "Till-plateau sand region" for instance runs deep into Germany and even Poland. At the local scale, the boundaries of the national units can be defined and precisely mapped by linking them to the 1:50,000 geomorphological map polygons. Each national unit consists of a typical assemblage of local geomorphological units. So, the newly developed natural physical landscape map layer can be used from the local to the European scale.
The thirteenth data release of the Sloan Digital Sky Survey: First spectroscopic data from the SDSS-IV survey mapping nearby galaxies at Apache Point Observatory

DOE PAGES

Franco D. Albareti

2017-12-08

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2, MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA, the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. Inmore » addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions of the SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1 data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE. This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. In conclusion, the SDSS website, this http URL, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV.« less

The thirteenth data release of the Sloan Digital Sky Survey: First spectroscopic data from the SDSS-IV survey mapping nearby galaxies at Apache Point Observatory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franco D. Albareti

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2, MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA, the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. Inmore » addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions of the SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1 data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE. This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. In conclusion, the SDSS website, this http URL, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV.« less
Communications and control for electric power systems: Power flow classification for static security assessment

NASA Technical Reports Server (NTRS)

Niebur, D.; Germond, A.

1993-01-01

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.
A self-trained classification technique for producing 30 m percent-water maps from Landsat data

USGS Publications Warehouse

Rover, Jennifer R.; Wylie, Bruce K.; Ji, Lei

2010-01-01

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.
Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

PubMed Central

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
Assessments of SENTINEL-2 Vegetation Red-Edge Spectral Bands for Improving Land Cover Classification

NASA Astrophysics Data System (ADS)

Qiu, S.; He, B.; Yin, C.; Liao, Z.

2017-09-01

The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40 %, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.
THE HOLDRIDGE LIFE ZONES OF THE CONTERMINOUS UNITED STATES IN RELATION TO ECOSYSTEM MAPPING

EPA Science Inventory

Our main goals were to develop a map of the life zones for the conterminous United States, based on the Holdridge Life Zone system as a tool for ecosystem mapping, and to compare the map of Holdridge life zones with other global vegetation classification and mapping efforts.
...
Comparison of rates of reported adverse events associated with i.v. iron products in the United States.

PubMed

Bailie, George R

2012-02-15

An analysis of reported adverse events (AEs) among patients using i.v. iron products, including the newer agent ferumoxytol, is presented. All AE reports to the Food and Drug Administration (FDA) citing iron sucrose, ferric gluconate, high- and low-molecular-weight iron dextran products, or ferumoxytol from October 2009 through June 2010 were evaluated. The rates of various classifications of reported AEs were calculated on a per-unit-sold basis and, for comparison of products supplied in different unit sizes, also in terms of 100-mg dose equivalents (DEq) of iron. A total of 197 reported AEs were identified (a cumulative rate of 14.1 AEs per million units sold). The rates of all AE classifications combined ranged from 5.25 to 746 per million units sold for iron sucrose and ferumoxytol, respectively; using the other method of calculation, the rates ranged from 5.24 per million DEq (iron sucrose) to 147 per million DEq (ferumoxytol). Relative to iron sucrose and sodium ferric gluconate, ferumoxytol was associated with significantly elevated risks of death (odds ratio [OR], 475 and 156, respectively; p < 0.0001), serious nonfatal AEs (OR, 263 and 121, respectively; p < 0.0001), and all evaluated AE classifications combined (OR, 142 and 109, respectively; p < 0.05). Analysis of reports submitted to FDA revealed large differences among i.v. iron products in reported deaths, serious AEs, other major AEs, and other AEs. Iron sucrose and sodium ferric gluconate were associated with much lower rates of AEs per million units sold than iron dextran or ferumoxytol, which were associated with the highest rates of all reported AE classifications.
Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

NASA Astrophysics Data System (ADS)

Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

2016-06-01

This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.
Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach based on geomorphometry

NASA Astrophysics Data System (ADS)

Pedersen, G. B. M.

2016-02-01

A new object-oriented approach is developed to classify glaciovolcanic landforms (Procedure A) and their landform elements boundaries (Procedure B). It utilizes the principle that glaciovolcanic edifices are geomorphometrically distinct from lava shields and plains (Pedersen and Grosse, 2014), and the approach is tested on data from Reykjanes Peninsula, Iceland. The outlined procedures utilize slope and profile curvature attribute maps (20 m/pixel) and the classified results are evaluated quantitatively through error matrix maps (Procedure A) and visual inspection (Procedure B). In procedure A, the highest obtained accuracy is 94.1%, but even simple mapping procedures provide good results (> 90% accuracy). Successful classification of glaciovolcanic landform element boundaries (Procedure B) is also achieved and this technique has the potential to delineate the transition from intraglacial to subaerial volcanic activity in orthographic view. This object-oriented approach based on geomorphometry overcomes issues with vegetation cover, which has been typically problematic for classification schemes utilizing spectral data. Furthermore, it handles complex edifice outlines well and is easily incorporated into a GIS environment, where results can be edited or fused with other mapping results. The approach outlined here is designed to map glaciovolcanic edifices within the Icelandic neovolcanic zone but may also be applied to similar subaerial or submarine volcanic settings, where steep volcanic edifices are surrounded by flat plains.
Classification of LIDAR Data for Generating a High-Precision Roadway Map

NASA Astrophysics Data System (ADS)

Jeong, J.; Lee, I.

2016-06-01

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.
Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator

Treesearch

Raymond L. Czaplewski

2000-01-01

Consider the following example of an accuracy assessment. Landsat data are used to build a thematic map of land cover for a multicounty region. The map classifier (e.g., a supervised classification algorithm) assigns each pixel into one category of land cover. The classification system includes 12 different types of forest and land cover: black spruce, balsam fir,...
Rapid corn and soybean mapping in US Corn Belt and neighboring areas

PubMed Central

Zhong, Liheng; Yu, Le; Li, Xuecao; Hu, Lina; Gong, Peng

2016-01-01

The goal of this study was to promptly map the extent of corn and soybeans early in the growing season. A classification experiment was conducted for the US Corn Belt and neighboring states, which is the most important production area of corn and soybeans in the world. To improve the timeliness of the classification algorithm, training was completely based on reference data and images from other years, circumventing the need to finish reference data collection in the current season. To account for interannual variability in crop development in the cross-year classification scenario, several innovative strategies were used. A random forest classifier was used in all tests, and MODIS surface reflectance products from the years 2008–2014 were used for training and cross-year validation. It is concluded that the fuzzy classification approach is necessary to achieve satisfactory results with R-squared ~0.9 (compared with the USDA Cropland Data Layer). The year of training data is an important factor, and it is recommended to select a year with similar crop phenology as the mapping year. With this phenology-based and cross-year-training method, in 2015 we mapped the cropping proportion of corn and soybeans around mid-August, when the two crops just reached peak growth. PMID:27811989
Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.
Rapid corn and soybean mapping in US Corn Belt and neighboring areas

NASA Astrophysics Data System (ADS)

Zhong, Liheng; Yu, Le; Li, Xuecao; Hu, Lina; Gong, Peng

2016-11-01

The goal of this study was to promptly map the extent of corn and soybeans early in the growing season. A classification experiment was conducted for the US Corn Belt and neighboring states, which is the most important production area of corn and soybeans in the world. To improve the timeliness of the classification algorithm, training was completely based on reference data and images from other years, circumventing the need to finish reference data collection in the current season. To account for interannual variability in crop development in the cross-year classification scenario, several innovative strategies were used. A random forest classifier was used in all tests, and MODIS surface reflectance products from the years 2008-2014 were used for training and cross-year validation. It is concluded that the fuzzy classification approach is necessary to achieve satisfactory results with R-squared ~0.9 (compared with the USDA Cropland Data Layer). The year of training data is an important factor, and it is recommended to select a year with similar crop phenology as the mapping year. With this phenology-based and cross-year-training method, in 2015 we mapped the cropping proportion of corn and soybeans around mid-August, when the two crops just reached peak growth.
Representation of Nursing Terminologies in UMLS

PubMed Central

Kim, Tae Youn; Coenen, Amy; Hardiker, Nicholas; Bartz, Claudia C.

2011-01-01

There are seven nursing terminologies or classifications that are considered a standard to support nursing practice in the U.S. Harmonizing these terminologies will enhance the interoperability of clinical data documented across nursing practice. As a first step to harmonize the nursing terminologies, the purpose of this study was to examine how nursing problems or diagnostic concepts from select terminologies were cross-mapped in Unified Medical Language System (UMLS). A comparison analysis was conducted by examining whether cross-mappings available in UMLS through concept unique identifiers were consistent with cross-mappings conducted by human experts. Of 423 concepts from three terminologies, 411 (97%) were manually cross-mapped by experts to the International Classification for Nursing Practice. The UMLS semantic mapping among the 411 nursing concepts presented 33.6% accuracy (i.e., 138 of 411 concepts) when compared to expert cross-mappings. Further research and collaboration among experts in this field are needed for future enhancement of UMLS. PMID:22195127
Algorithms and methodology used in constructing high-resolution terrain databases

NASA Astrophysics Data System (ADS)

Williams, Bryan L.; Wilkosz, Aaron

1998-07-01

This paper presents a top-level description of methods used to generate high-resolution 3D IR digital terrain databases using soft photogrammetry. The 3D IR database is derived from aerial photography and is made up of digital ground plane elevation map, vegetation height elevation map, material classification map, object data (tanks, buildings, etc.), and temperature radiance map. Steps required to generate some of these elements are outlined. The use of metric photogrammetry is discussed in the context of elevation map development; and methods employed to generate the material classification maps are given. The developed databases are used by the US Army Aviation and Missile Command to evaluate the performance of various missile systems. A discussion is also presented on database certification which consists of validation, verification, and accreditation procedures followed to certify that the developed databases give a true representation of the area of interest, and are fully compatible with the targeted digital simulators.
Assessing three fuel classification systems and their maps using Forest Inventory and Analysis (FIA) surface fuel measurements

Treesearch

Robert E. Keane; Jason M. Herynk; Chris Toney; Shawn P. Urbanski; Duncan C. Lutes; Roger D. Ottmar

2015-01-01

Fuel classifications are integral tools in fire management and planning because they are used as inputs to fire behavior and effects simulation models. Fuel Loading Models (FLMs) and Fuel Characteristic Classification System (FCCSs) fuelbeds are the most popular classifications used throughout wildland fire science and management, but they have yet to be thoroughly...
ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

USGS Publications Warehouse

Rosenfield, George H.; Fitzpatrick-Lins, Katherine

1984-01-01

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.
Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada

NASA Astrophysics Data System (ADS)

Neves, Bárbara M.; Du Preez, Cherisse; Edinger, Evan

2014-01-01

Efforts to locate and map deep-water coral and sponge habitats are essential for the effective management and conservation of these vulnerable marine ecosystems. Here we test the applicability of a simple multibeam sonar classification method developed for fjord environments to map the distribution of shelf-depth substrates and gorgonian coral- and sponge-dominated biotopes. The studied area is a shelf-depth feature Learmonth Bank, northern British Columbia, Canada and the method was applied aiming to map primarily non-reef forming coral and sponge biotopes. Aside from producing high-resolution maps (5 m2 raster grid), biotope-substrate associations were also investigated. A multibeam sonar survey yielded bathymetry, acoustic backscatter strength and slope. From benthic video transects recorded by remotely operated vehicles (ROVs) six primary substrate types and twelve biotope categories were identified, defined by the primary sediment and dominant biological structure, respectively. Substrate and biotope maps were produced using a supervised classification mostly based on the inter-quartile range of the acoustic variables for each substrate type and biotope. Twenty-five percent of the video observations were randomly reserved for testing the classification accuracy. The dominant biotope-defining corals were red tree coral Primnoa pacifica and small styasterids, of which Stylaster parageus was common. Demosponges and hexactinellid sponges were frequently observed but no sponge reefs were observed. The substrate classification readily distinguished fine sediment, Sand and Bedrock from the other substrate types, but had greater difficulty distinguishing Bedrock from Boulders and Cobble. The biotope classification accurately identified Gardens (dense aggregations of sponges and corals) and Primnoa-dominated biotopes (67% accuracy), but most other biotopes had lower accuracies. There was a significant correspondence between Learmonth's biotopes and substrate types, with many biotopes strongly associated with only a single substrate type. This strong correspondence allowed substrate types to function as a surrogate for helping to map biotope distributions. Our results add new information on the distribution of corals and sponges at Learmonth Bank, which can be used to guide management at this location.
Two techniques for mapping and area estimation of small grains in California using Landsat digital data

NASA Technical Reports Server (NTRS)

Sheffner, E. J.; Hlavka, C. A.; Bauer, E. M.

1984-01-01

Two techniques have been developed for the mapping and area estimation of small grains in California from Landsat digital data. The two techniques are Band Ratio Thresholding, a semi-automated version of a manual procedure, and LCLS, a layered classification technique which can be fully automated and is based on established clustering and classification technology. Preliminary evaluation results indicate that the two techniques have potential for providing map products which can be incorporated into existing inventory procedures and automated alternatives to traditional inventory techniques and those which currently employ Landsat imagery.

A new map of standardized terrestrial ecosystems of Africa

USGS Publications Warehouse

Sayre, Roger G.; Comer, Patrick; Hak, Jon; Josse, Carmen; Bow, Jacquie; Warner, Harumi; Larwanou, Mahamane; Kelbessa, Ensermu; Bekele, Tamrat; Kehl, Harald; Amena, Ruba; Andriamasimanana, Rado; Ba, Taibou; Benson, Laurence; Boucher, Timothy; Brown, Matthew; Cress, Jill J.; Dassering, Oueddo; Friesen, Beverly A.; Gachathi, Francis; Houcine, Sebei; Keita, Mahamadou; Khamala, Erick; Marangu, Dan; Mokua, Fredrick; Morou, Boube; Mucina, Ladislav; Mugisha, Samuel; Mwavu, Edward; Rutherford, Michael; Sanou, Patrice; Syampungani, Stephen; Tomor, Bojoi; Vall, Abdallahi Ould Mohamed; Vande Weghe, Jean Pierre; Wangui, Eunice; Waruingi, Lucy

2013-01-01

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.
Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.
Principles of soil mapping of a megalopolis with St. Petersburg as an example

NASA Astrophysics Data System (ADS)

Aparin, B. F.; Sukhacheva, E. Yu.

2014-07-01

For the first time, a soil map of St. Petersburg has been developed on a scale of 1 : 50000 using MicroStation V8i software. The legend to this map contains more than 60 mapping units. The classification of urban soils and information on the soil cover patterns are principally new elements of this legend. New concepts of the urbanized soil space and urbopedocombinations have been suggested for soil mapping of urban territories. The typification of urbopedocombinations in St. Petersburg has been performed on the basis of data on the geometry and composition of the polygons of soils and nonsoil formations. The ratio between the areas of soils and nonsoil formations and their spatial distribution patterns have been used to distinguish between six types of the urbanized soil space. The principles of classification of the soils of urban territories have been specified, and a separate order of pedo-allochthonous soils has been suggested for inclusion into the Classification and Diagnostic System of Russian Soils (2004). Six types of pedo-allochthonous soils have been distinguished on the basis of data on their humus and organic horizons and the character of the underlying mineral substrate.
The History of Soil Mapping and Classification in Europe: The role of the European Commission

NASA Astrophysics Data System (ADS)

Montanarella, Luca

2014-05-01

Early systematic soil mapping in Europe dates back to the early times of soil science in the 19th Century and was developed at National scales mostly for taxation purposes. National soil classification systems emerged out of the various scientific communities active at that time in leading countries like Germany, Austria, France, Belgium, United Kingdom and many others. Different scientific communities were leading in the various countries, in some cases stemming from geological sciences, in others as a branch of agricultural sciences. Soil classification for the purpose of ranking soils for their capacity to be agriculturally productive emerged as the main priority, allowing in some countries for very detailed and accurate soil maps at 1:5,000 scale and larger. Detailed mapping was mainly driven by taxation purposes in the early times but evolved in several countries also as a planning and management tool for farms and local administrations. The need for pan-European soil mapping and classification efforts emerged only after World War II in the early 1950's under the auspices of FAO with the aim to compile a common European soil map as a contribution to the global soil mapping efforts of FAO at that time. These efforts evolved over the next decades, with the support of the European Commission, towards the establishment of a permanent network of National soil survey institutions (the European Soil Bureau Network). With the introduction of digital soil mapping technologies, the new European Soil Information System (EUSIS) was established, incorporating data at multiple scales for the EU member states and bordering countries. In more recent years, the formal establishment of the European Soil Data Centre (ESDAC) hosted by the European Commission, together with a formal legal framework for soil mapping and soil classification provided by the INSPIRE directive and the related standardization and harmonization efforts, has led to the operational development of advanced digital soil mapping techniques supporting the contribution of Europe to a common global soil information system under the coordination of the Global Soil Partnership (GSP) of FAO. Further information: http://eusoils.jrc.ec.europa.eu/ References: Mark G Kibblewhite, Ladislav Miko, Luca Montanarella, Legal frameworks for soil protection: current development and technical information requirements, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, November 2012, Pages 573-577. Luca Montanarella, Ronald Vargas, Global governance of soil resources as a necessary condition for sustainable development, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, November 2012, Pages 559-564.
U.S. Geological Survey ArcMap Sediment Classification tool

USGS Publications Warehouse

O'Malley, John

2007-01-01

The U.S. Geological Survey (USGS) ArcMap Sediment Classification tool is a custom toolbar that extends the Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 9.2 Desktop application to aid in the analysis of seabed sediment classification. The tool uses as input either a point data layer with field attributes containing percentage of gravel, sand, silt, and clay or four raster data layers representing a percentage of sediment (0-100%) for the various sediment grain size analysis: sand, gravel, silt and clay. This tool is designed to analyze the percent of sediment at a given location and classify the sediments according to either the Folk (1954, 1974) or Shepard (1954) as modified by Schlee(1973) classification schemes. The sediment analysis tool is based upon the USGS SEDCLASS program (Poppe, et al. 2004).
Spectrally based mapping of riverbed composition

USGS Publications Warehouse

Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.

2016-01-01

Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader range of fluvial environments is needed to substantiate our initial results, this case study suggests that bed composition in shallow, clear-flowing rivers potentially could be mapped remotely.
Multiple Scale Landscape Pattern Index Interpretation for the Persistent Monitoring of Land-Cover and Land-Use

NASA Astrophysics Data System (ADS)

Spivey, Alvin J.

Mapping land-cover land-use change (LCLUC) over regional and continental scales, and long time scales (years and decades), can be accomplished using thematically identified classification maps of a landscape---a LCLU class map. Observations of a landscape's LCLU class map pattern can indicate the most relevant process, like hydrologic or ecologic function, causing landscape scale environmental change. Quantified as Landscape Pattern Metrics (LPM), emergent landscape patterns act as Landscape Indicators (LI) when physically interpreted. The common mathematical approach to quantifying observed landscape scale pattern is to have LPM measure how connected a class exists within the landscape, through nonlinear local kernel operations of edges and gradients in class maps. Commonly applied kernel-based LPM that consistently reveal causal processes are Dominance, Contagion, and Fractal Dimension. These kernel-based LPM can be difficult to interpret. The emphasis on an image pixel's edge by gradient operations and dependence on an image pixel's existence according to classification accuracy limit the interpretation of LPM. For example, the Dominance and Contagion kernel-based LPM very similarly measure how connected a landscape is. Because of this, their reported edge measurements of connected pattern correlate strongly, making their results ambiguous. Additionally, each of these kernel-based LPM are unscalable when comparing class maps from separate imaging system sensor scenarios that change the image pixel's edge position (i.e. changes in landscape extent, changes in pixel size, changes in orientation, etc), and can only interpret landscape pattern as accurately as the LCLU map classification will allow. This dissertation discusses the reliability of common LPM in light of imaging system effects such as: algorithm classification likelihoods, LCLU classification accuracy due to random image sensor noise, and image scale. A description of an approach to generating well behaved LPM through a Fourier system analysis of the entire class map, or any subset of the class map (e.g. the watershed) is the focus of this work. The Fourier approach provides four improvements for LPM. First, the approach reduces any correlation between metrics by developing them within an independent (i.e. orthogonal) Fourier vector space; a Fourier vector space that includes relevant physically representative parameters ( i.e. between class Euclidean distance). Second, accounting for LCLU classification accuracy the LPM measurement precision and measurement accuracy are reported. Third, the mathematics of this approach makes it possible to compare image data captured at separate pixel resolutions or even from separate landscape scenes. Fourth, Fourier interpreted landscape pattern measurement can be a measure of the entire landscape shape, of individual landscape cover change, or as exchanges between class map subsets by operating on the entire class map, subset of class map, or separate subsets of class map[s] respectively. These LCLUC LPM are examined along the 1991-1992 and 2000-2001 records of National Land Cover Database Landsat data products. Those LPM results are used in a predictive fecal coliform model at the South Carolina watershed level in the context of past (validation study) change. Finally, the proposed LPM ability to be used as ecologically relevant environmental indicators is tested by correlating metrics with other, well known LI that consistently reveal causal processes in the literature.
A new world natural vegetation map for global change studies.

PubMed

Lapola, David M; Oyama, Marcos D; Nobre, Carlos A; Sampaio, Gilvan

2008-06-01

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).
Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.
Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Sun, Guoqing; Kharuk, V. I.; Kovacs, Katalin

1998-01-01

This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and
Multisource multibeam backscatter data: developing a strategy for the production of benthic habitat maps using semi-automated seafloor classification methods

NASA Astrophysics Data System (ADS)

Lacharité, Myriam; Brown, Craig J.; Gazzola, Vicki

2018-06-01

The establishment of multibeam echosounders (MBES) as a mainstream tool in ocean mapping has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The bathymetric and backscatter information generated by MBES enables marine scientists to present highly accurate bathymetric data with a spatial resolution closely matching that of terrestrial mapping, and can generate customized thematic seafloor maps to meet multiple ocean management needs. However, when a variety of MBES systems are used, the creation of objective habitat maps can be hindered by the lack of backscatter calibration, due for example, to system-specific settings, yielding relative rather than absolute values. Here, we describe an approach using object-based image analysis to combine 4 non-overlapping and uncalibrated (backscatter) MBES coverages to form a seamless habitat map on St. Anns Bank (Atlantic Canada), a marine protected area hosting a diversity of benthic habitats. The benthoscape map was produced by analysing each coverage independently with supervised classification (k-nearest neighbor) of image-objects based on a common suite of 7 benthoscapes (determined with 4214 ground-truthing photographs at 61 stations, and characterized with backscatter, bathymetry, and bathymetric position index). Manual re-classification based on uncertainty in membership values to individual classes—especially at the boundaries between coverages—was used to build the final benthoscape map. Given the costs and scarcity of MBES surveys in offshore marine ecosystems—particularly in large ecosystems in need of adequate conservation strategies, such as in Canadian waters—developing approaches to synthesize multiple datasets to meet management needs is warranted.
Comparing Canadian and American normative scores on the Wechsler Adult Intelligence Scale-Fourth Edition.

PubMed

Harrison, Allyson G; Armstrong, Irene T; Harrison, Laura E; Lange, Rael T; Iverson, Grant L

2014-12-01

Psychologists practicing in Canada must decide which set of normative data to use for the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). The purpose of this study was to compare the interpretive effects of applying American versus Canadian normative systems in a sample of 432 Canadian postsecondary-level students who were administered the WAIS-IV as part of an evaluation for a learning disability, attention-deficit hyperactivity disorder, or other mental health problems. Employing the Canadian normative system yielded IQ, Index, and subtest scores that were systematically lower than those obtained using the American norms. Furthermore, the percentage agreement in normative classifications, defined as American and Canadian index scores within five points or within the same classification range, was between 49% and 76%. Substantial differences are present between the American and Canadian WAIS-IV norms. Clinicians should consider carefully the implications regarding which normative system is most appropriate for specific types of evaluations. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Gates to Gregg High Voltage Transmission Line Study. [California

NASA Technical Reports Server (NTRS)

Bergis, V.; Maw, K.; Newland, W.; Sinnott, D.; Thornbury, G.; Easterwood, P.; Bonderud, J.

1982-01-01

The usefulness of LANDSAT data in the planning of transmission line routes was assessed. LANDSAT digital data and image processing techniques, specifically a multi-date supervised classification aproach, were used to develop a land cover map for an agricultural area near Fresno, California. Twenty-six land cover classes were identified, of which twenty classes were agricultural crops. High classification accuracies (greater than 80%) were attained for several classes, including cotton, grain, and vineyards. The primary products generated were 1:24,000, 1:100,000 and 1:250,000 scale maps of the classification and acreage summaries for all land cover classes within four alternate transmission line routes.
Comparison of SAM and OBIA as Tools for Lava Morphology Classification - A Case Study in Krafla, NE Iceland

NASA Astrophysics Data System (ADS)

Aufaristama, Muhammad; Hölbling, Daniel; Höskuldsson, Ármann; Jónsdóttir, Ingibjörg

2017-04-01

The Krafla volcanic system is part of the Icelandic North Volcanic Zone (NVZ). During Holocene, two eruptive events occurred in Krafla, 1724-1729 and 1975-1984. The last eruptive episode (1975-1984), known as the "Krafla Fires", resulted in nine volcanic eruption episodes. The total area covered by the lavas from this eruptive episode is 36 km2 and the volume is about 0.25-0.3 km3. Lava morphology is related to the characteristics of the surface morphology of a lava flow after solidification. The typical morphology of lava can be used as primary basis for the classification of lava flows when rheological properties cannot be directly observed during emplacement, and also for better understanding the behavior of lava flow models. Although mapping of lava flows in the field is relatively accurate such traditional methods are time consuming, especially when the lava covers large areas such as it is the case in Krafla. Semi-automatic mapping methods that make use of satellite remote sensing data allow for an efficient and fast mapping of lava morphology. In this study, two semi-automatic methods for lava morphology classification are presented and compared using Landsat 8 (30 m spatial resolution) and SPOT-5 (10 m spatial resolution) satellite images. For assessing the classification accuracy, the results from semi-automatic mapping were compared to the respective results from visual interpretation. On the one hand, the Spectral Angle Mapper (SAM) classification method was used. With this method an image is classified according to the spectral similarity between the image reflectance spectrums and the reference reflectance spectra. SAM successfully produced detailed lava surface morphology maps. However, the pixel-based approach partly leads to a salt-and-pepper effect. On the other hand, we applied the Random Forest (RF) classification method within an object-based image analysis (OBIA) framework. This statistical classifier uses a randomly selected subset of training samples to produce multiple decision trees. For final classification of pixels or - in the present case - image objects, the average of the class assignments probability predicted by the different decision trees is used. While the resulting OBIA classification of lava morphology types shows a high coincidence with the reference data, the approach is sensitive to the segmentation-derived image objects that constitute the base units for classification. Both semi-automatic methods produce reasonable results in the Krafla lava field, even if the identification of different pahoehoe and aa types of lava appeared to be difficult. The use of satellite remote sensing data shows a high potential for fast and efficient classification of lava morphology, particularly over large and inaccessible areas.
Heterogeneity in Positive Predictive Value of RAS Mutations in Cytologically Indeterminate Thyroid Nodules.

PubMed

Nabhan, Fadi; Porter, Kyle; Lupo, Mark A; Randolph, Gregory W; Patel, Kepal N; Kloos, Richard T

2018-06-01

RAS mutations are common in the available mutational analysis of cytologically indeterminate (Cyto-I) thyroid nodules. However, their reported positive predictive value (PPV) for cancer is widely variable. The reason for this variability is unknown, and it causes clinical management uncertainty. A systematic review was performed, evaluating the PPV for cancer in RAS mutation positive Cyto-I nodules, and variables that might affect residual heterogeneity across the different studies were considered. PubMed was searched through February 22, 2017, including studies that evaluated at least one type of RAS mutation in Cyto-I nodules, including any (or all) of the Bethesda III/IV/V categories or their equivalents and where the histological diagnosis was available. The PPV residual heterogeneity was investigated after accounting for Bethesda classification, blindedness of the histopathologist to the RAS mutational status, Bethesda category-specific cancer prevalence for each study, and which RAS genes and codons were tested. This was studied using five meta-regression models fit to different sets of Bethesda classification categories: Bethesda III, IV, or V (III/IV/V); Bethesda III or IV (III/IV); Bethesda III only; Bethesda IV only; and Bethesda V only. Of 1831 studies, 23 were eligible for data inclusion. Wide ranges of PPV were found at 0-100%, 28-100%, and 0-100% in Bethesda III, IV, and V, respectively. Residual heterogeneity remained moderately high for PPV after accounting for the above moderators for Bethesda III/IV/V (21 studies; I 2 = 59.5%) and Bethesda III/IV (19 studies; I 2 = 66.0%), with significant Cochran's Q-test for residual heterogeneity (p < 0.001). Among individual Bethesda categories, residual heterogeneity was: Bethesda III (eight studies; I 2 = 89.0%), IV (12 studies; I 2 = 53.5%), and V (10 studies; I 2 = 34.4%), with significant Cochran's Q-test for Bethesda III (p < 0.001) and IV (p = 0.04). The PPV of RAS mutations in Bethesda III and IV categories is quite heterogeneous across different studies, creating low confidence in the accuracy of a single estimate of PPV. Clinicians must appreciate this wide variability when managing a RAS-mutated Cyto-I nodule. Future studies should seek to resolve this unexplained variability.
U.S. Fish and Wildlife Service 1979 wetland classification: a review

USGS Publications Warehouse

Cowardin, L.M.; Golet, F.C.

1995-01-01

In 1979 the US Fish and Wildlife Service published and adopted a classification of wetlands and deepwater habitats of the United States. The system was designed for use in a national inventory of wetlands. It was intended to be ecologically based, to furnish the mapping units needed for the inventory, and to provide national consistency in terminology and definition. We review the performance of the classification after 13 years of use. The definition of wetland is based on national lists of hydric soils and plants that occur in wetlands. Our experience suggests that wetland classifications must facilitate mapping and inventory because these data gathering functions are essential to management and preservation of the wetland resource, but the definitions and taxa must have ecological basis. The most serious problem faced in construction of the classification was lack of data for many of the diverse wetland types. Review of the performance of the classification suggests that, for the most part, it was successful in accomplishing its objectives, but that problem areas should be corrected and modification could strengthen its utility. The classification, at least in concept, could be applied outside the United States. Experience gained in use of the classification can furnish guidance as to pitfalls to be avoided in the wetland classification process.
Map showing locations of mines, prospects, and patented mining claims, and classification of mineral deposits in the Silver City 7 1/2-minute Quadrangle, Black Hills, South Dakota

USGS Publications Warehouse

DeWitt, Ed; Buscher, David; Wilson, A.B.; Johnson, Thomas

1988-01-01

This map is one in a set of 26 maps (see index map) at 1:24,000 scale of the Black Hills region of South Dakota and Wyoming om which are shown a geologic classification of mines, a bibliography of mineral deposits, and locations of active and inactive mines, prospects, and patented mining claims. Some of these maps are published as U. S. Geological Survey Miscellaneous Field Studies Maps (MF series) and some as U.S. Geological Survey Open-File Reports (QF series); see index map. An earlier unpublished version of this set of maps was the data base from which plate 4 (scale 1:250,000) of DeWitt and others (1986) was compiled. Subsequent to that publication, the set has been revised and updated, and prospects and patented claims have been added. These revised and more detailed 1:24,000-scale maps should be used for the equivalent areas of plate 4 of DeWitt and others (1986).
Classification of natural formations based on their optical characteristics using small volumes of samples

NASA Astrophysics Data System (ADS)

Abramovich, N. S.; Kovalev, A. A.; Plyuta, V. Y.

1986-02-01

A computer algorithm has been developed to classify the spectral bands of natural scenes on Earth according to their optical characteristics. The algorithm is written in FORTRAN-IV and can be used in spectral data processing programs requiring small data loads. The spectral classifications of some different types of green vegetable canopies are given in order to illustrate the effectiveness of the algorithm.
Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.
Landscape scale mapping of forest inventory data by nearest neighbor classification

Treesearch

Andrew Lister

2009-01-01

One of the goals of the Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is large-area mapping. FIA scientists have tried many methods in the past, including geostatistical methods, linear modeling, nonlinear modeling, and simple choropleth and dot maps. Mapping methods that require individual model-based maps to be...

GlobCorine- A Joint EEA-ESA Project for Operational Land Cover and Land Use Mapping at Pan-European Scale

NASA Astrophysics Data System (ADS)

Bontemps, S.; Defourny, P.; Van Bogaert, E.; Weber, J. L.; Arino, O.

2010-12-01

Regular and global land cover mapping contributes to evaluating the impact of human activities on the environment. Jointly supported by the European Space Agency and the European Environmental Agency, the GlobCorine project builds on the GlobCover findings and aims at making the full use of the MERIS time series for frequent land cover monitoring. The GlobCover automated classification approach has been tuned to the pan-European continent and adjusted towards a classification compatible with the Corine typology. The GlobCorine 2005 land cover map has been achieved, validated and made available to a broad- level stakeholder community from the ESA website. A first version of the GlobCorine 2009 map has also been produced, demonstrating the possibility for an operational production of frequent and updated global land cover maps.
Computer-Assisted Promotion of Recreational Opportunities in Natural Resource Areas: A Demonstration and Case Example

Treesearch

Emilyn Sheffield; Leslie Furr; Charles Nelson

1992-01-01

Filevision IV is a multilayer imaging and data-base management system that combines drawing, filing and extensive report-writing capabilities (Filevision IV, 1988). Filevision IV users access data by attaching graphics to text-oriented data-base records. Tourist attractions, support services, and geo-graphic features can be located on a base map of an area or region....
Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

PubMed

Islam, Md Rabiul; Tanaka, Toshihisa; Molla, Md Khademul Islam

2018-05-08

When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.
MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

PubMed Central

Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation. PMID:26681933
Classification criteria and probability risk maps: limitations and perspectives.

PubMed

Saisana, Michaela; Dubois, Gregoire; Chaloulakou, Archontoula; Spyrellis, Nikolas

2004-03-01

Delineation of polluted zones with respect to regulatory standards, accounting at the same time for the uncertainty of the estimated concentrations, relies on classification criteria that can lead to significantly different pollution risk maps, which, in turn, can depend on the regulatory standard itself. This paper reviews four popular classification criteria related to the violation of a probability threshold or a physical threshold, using annual (1996-2000) nitrogen dioxide concentrations from 40 air monitoring stations in Milan. The relative advantages and practical limitations of each criterion are discussed, and it is shown that some of the criteria are more appropriate for the problem at hand and that the choice of the criterion can be supported by the statistical distribution of the data and/or the regulatory standard. Finally, the polluted area is estimated over the different years and concentration thresholds using the appropriate risk maps as an additional source of uncertainty.
MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

PubMed

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.
Sea ice type maps from Alaska synthetic aperture radar facility imagery: An assessment

NASA Technical Reports Server (NTRS)

Fetterer, Florence M.; Gineris, Denise; Kwok, Ronald

1994-01-01

Synthetic aperture radar (SAR) imagery received at the Alaskan SAR Facility is routinely and automatically classified on the Geophysical Processor System (GPS) to create ice type maps. We evaluated the wintertime performance of the GPS classification algorithm by comparing ice type percentages from supervised classification with percentages from the algorithm. The root mean square (RMS) difference for multiyear ice is about 6%, while the inconsistency in supervised classification is about 3%. The algorithm separates first-year from multiyear ice well, although it sometimes fails to correctly classify new ice and open water owing to the wide distribution of backscatter for these classes. Our results imply a high degree of accuracy and consistency in the growing archive of multiyear and first-year ice distribution maps. These results have implications for heat and mass balance studies which are furthered by the ability to accurately characterize ice type distributions over a large part of the Arctic.
The DSM-5: Classification and criteria changes.

PubMed

Regier, Darrel A; Kuhl, Emily A; Kupfer, David J

2013-06-01

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) marks the first significant revision of the publication since the DSM-IV in 1994. Changes to the DSM were largely informed by advancements in neuroscience, clinical and public health need, and identified problems with the classification system and criteria put forth in the DSM-IV. Much of the decision-making was also driven by a desire to ensure better alignment with the International Classification of Diseases and its upcoming 11th edition (ICD-11). In this paper, we describe select revisions in the DSM-5, with an emphasis on changes projected to have the greatest clinical impact and those that demonstrate efforts to enhance international compatibility, including integration of cultural context with diagnostic criteria and changes that facilitate DSM-ICD harmonization. It is anticipated that this collaborative spirit between the American Psychiatric Association (APA) and the World Health Organization (WHO) will continue as the DSM-5 is updated further, bringing the field of psychiatry even closer to a singular, cohesive nosology. Copyright © 2013 World Psychiatric Association.
Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.
Bioclimatic Classification of Northeast Asia for climate change response

NASA Astrophysics Data System (ADS)

Choi, Y.; Jeon, S. W.; Lim, C. H.

2016-12-01

As climate change has been getting worse, we should monitor the change of biodiversity, and distribution of species to handle the crisis and take advantage of climate change. The development of bioclimatic map which classifies land into homogenous zones by similar environment properties is the first step to establish a strategy. Statistically derived classifications of land provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions. Many countries are trying to make this kind of map and actively utilize it to ecosystem conservation and management. However, the Northeast Asia including North Korea doesn't have detailed environmental information, and has not built environmental classification map. Therefore, this study presents a bioclimatic map of Northeast Asia based on statistical clustering of bioclimate data. Bioclim data ver1.4 which provided by WorldClim were considered for inclusion in a model. Eight of the most relevant climate variables were selected by correlation analysis, based on previous studies. Principal Components Analysis (PCA) was used to explain 86% of the variation into three independent dimensions, which were subsequently clustered using an ISODATA clustering. The bioclimatic zone of Northeast Asia could consist of 29, 35, and 50 zones. This bioclimatic map has a 30' resolution. To assess the accuracy, the correlation coefficient was calculated between the first principal component values of the classification variables and the vegetation index, Gross Primary Production (GPP). It shows about 0.5 Pearson correlation coefficient. This study constructed Northeast Asia bioclimatic map by statistical method with high resolution, but in order to better reflect the realities, the variety of climate variables should be considered. Also, further studies should do more quantitative and qualitative validation in various ways. Then, this could be used more effectively to support decision making on climate change adaptation.
Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

PubMed

Baldeck, Claire A; Asner, Gregory P; Martin, Robin E; Anderson, Christopher B; Knapp, David E; Kellner, James R; Wright, S Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.
Rice crop mapping and change prediction using multi-temporal satellite images in the Mekong Delta, Vietnam

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.

2014-12-01

The rice cropping systems in the Vietnamese Mekong Delta (VMD) has been undergoing major changes to cope with developing agro-economics, increasing population and changing climate. Information on rice cropping practices and changes in cropping systems is critical for policymakers to devise successful strategies to ensure food security and rice grain exports for the country. The primary objective of this research is to map rice cropping systems and predict future dynamics of rice cropping systems using the MODIS time-series data of 2002, 2006, and 2010. First, a phenology-based classification approach was applied for the classification and assessment of rice cropping systems in study region. Second, the Cellular Automata-Markov (CA-Markov) models was used to simulate the rice-cropping system map of VMD for 2010. The comparisons between the classification maps and the ground reference data indicated satisfactory results with overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2010. The simulated map of rice cropping system for 2010 was extrapolated by CA-Markov model based on the trend of rice cropping systems during 2002~2006. The comparison between predicted scenario and classification map for 2010 presents a reasonably closer agreement. In conclusion, the CA-Markov model performs a powerful tool for the dynamic modeling of changes in rice cropping systems, and the results obtained demonstrate that the approach produces satisfactory results in terms of accuracy, quantitative forecast and spatial pattern changes. Meanwhile, the projections of the future changes would provide useful inputs to the agricultural policy for effective management of the rice cropping practices in VMD.
Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

PubMed Central

Baldeck, Claire A.; Asner, Gregory P.; Martin, Robin E.; Anderson, Christopher B.; Knapp, David E.; Kellner, James R.; Wright, S. Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems. PMID:26153693
Influence of pansharpening techniques in obtaining accurate vegetation thematic maps

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martin, Consuelo; Marcello-Ruiz, Javier

2016-10-01

In last decades, there have been a decline in natural resources, becoming important to develop reliable methodologies for their management. The appearance of very high resolution sensors has offered a practical and cost-effective means for a good environmental management. In this context, improvements are needed for obtaining higher quality of the information available in order to get reliable classified images. Thus, pansharpening enhances the spatial resolution of the multispectral band by incorporating information from the panchromatic image. The main goal in the study is to implement pixel and object-based classification techniques applied to the fused imagery using different pansharpening algorithms and the evaluation of thematic maps generated that serve to obtain accurate information for the conservation of natural resources. A vulnerable heterogenic ecosystem from Canary Islands (Spain) was chosen, Teide National Park, and Worldview-2 high resolution imagery was employed. The classes considered of interest were set by the National Park conservation managers. 7 pansharpening techniques (GS, FIHS, HCS, MTF based, Wavelet `à trous' and Weighted Wavelet `à trous' through Fractal Dimension Maps) were chosen in order to improve the data quality with the goal to analyze the vegetation classes. Next, different classification algorithms were applied at pixel-based and object-based approach, moreover, an accuracy assessment of the different thematic maps obtained were performed. The highest classification accuracy was obtained applying Support Vector Machine classifier at object-based approach in the Weighted Wavelet `à trous' through Fractal Dimension Maps fused image. Finally, highlight the difficulty of the classification in Teide ecosystem due to the heterogeneity and the small size of the species. Thus, it is important to obtain accurate thematic maps for further studies in the management and conservation of natural resources.
It is time to bring borderline intellectual functioning back into the main fold of classification systems

PubMed Central

Wieland, Jannelien; Zitman, Frans G.

2016-01-01

Borderline intellectual functioning is an important and frequently unrecognised comorbid condition relevant to the diagnosis and treatment of any and all psychiatric disorders. In the DSM-IV-TR, it is defined by IQ in the 71–84 range. In DSM-5, IQ boundaries are no longer part of the classification, leaving the concept without a clear definition. This modification is one of the least highlighted changes in DSM-5. In this article we describe the history of the classification of borderline intellectual functioning. We provide information about it and on the importance of placing it in the right context and in the right place in future DSM editions and other classification systems such as the International Classification of Diseases. PMID:27512590
A geomorphic approach to 100-year floodplain mapping for the Conterminous United States

NASA Astrophysics Data System (ADS)

Jafarzadegan, Keighobad; Merwade, Venkatesh; Saksena, Siddharth

2018-06-01

Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS.
Static magnetic field blood pressure buffering, baroreflex vs. vascular blood pressure control mechanism.

PubMed

Gmitrov, Juraj

2010-02-01

We compared the effect of static magnetic field (SMF) and verapamil, a potent vascular calcium channel blocking agent, on sudden elevation in blood pressure in conjunction with arterial baroreflex sensitivity (BRS) and microcirculation. Forty-four experiments were performed on conscious rabbits sedated using pentobarbital intravenous (i.v.) infusion (5 mg kg(-1) h(-1)). Mean femoral artery blood pressure (MAP), heart rate, BRS and ear lobe skin microcirculatory blood flow, estimated using microphotoelectric plethysmography (MPPG), were simultaneously measured after a 40 min exposure of the sinocarotid baroreceptors to 350 mT SMF, generated by Nd(2)-Fe(14)-B magnets, or 30 min of verapamil i.v. administration (20 microg kg(-1) min(-1)). BRS was assessed from heart rate and MAP responses to i.v. bolus of nitroprusside and phenylephrine. The decrease in phenylephrine-induced abrupt elevation in MAP (DeltaMAP(AE)) was significantly larger after verapamil than after SMF exposure. DeltaMAP(AE) inversely correlated with verapamil-induced significant increase in DeltaMPPG (r = 0.53, p < 0.000) and with SMF-induced significant increase in DeltaBRS (r = 0.47, p < 0.016). Our results suggest that verapamil-potentiated vascular blood pressure buffering mechanism was more effective than SMF-potentiated baroreflex-mediated blood pressure buffering mechanism, and a potential benefit of both approaches in cardiovascular conditions with abrupt high elevation in blood pressure.
78 FR 68714 - Medical Devices; Ophthalmic Devices; Classification of the Scleral Plug

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-15

... the device materials must be performed; (iv) Performance data must demonstrate acceptable mechanical properties under simulated clinical use conditions including insertion and removal of the device; (v...
A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

NASA Astrophysics Data System (ADS)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to proper wages usage. Thus a more precise and unambiguous boundaries of segments (objects) were received. As a results of the classification 5 classes of land cover (buildings, water, high and low vegetation and others) were extracted. Both pixel-based image analysis and OBIA were conducted with a minimum mapping unit of 10m2. Results were validated on the basis on manual classification and random points (80 per test area), reference data set was manually interpreted using ortophotomaps and expert knowledge of the test site areas.
Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

PubMed

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-02-11

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

Geological sampling data and benthic biota classification: Buzzards Bay and Vineyard Sound, Massachusetts

USGS Publications Warehouse

Ackerman, Seth D.; Pappal, Adrienne L.; Huntley, Emily C.; Blackwood, Dann S.; Schwab, William C.

2015-01-01

Sea-floor sample collection is an important component of a statewide cooperative mapping effort between the U.S. Geological Survey (USGS) and the Massachusetts Office of Coastal Zone Management (CZM). Sediment grab samples, bottom photographs, and video transects were collected within Vineyard Sound and Buzzards Bay in 2010 aboard the research vesselConnecticut. This report contains sample data and related information, including analyses of surficial-sediment grab samples, locations and images of sea-floor photography, survey lines along which sea-floor video was collected, and a classification of benthic biota observed in sea-floor photographs and based on the Coastal and Marine Ecological Classification Standard (CMECS). These sample data and analyses information are used to verify interpretations of geophysical data and are an essential part of geologic maps of the sea floor. These data also provide a valuable inventory of benthic habitat and resources. Geographic information system (GIS) data, maps, and interpretations, produced through the USGS and CZM mapping cooperative, are intended to aid efforts to manage coastal and marine resources and to provide baseline information for research focused on coastal evolution and environmental change.
Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

PubMed Central

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-01-01

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172
Computer-aided classification of forest cover types from small scale aerial photography

NASA Astrophysics Data System (ADS)

Bliss, John C.; Bonnicksen, Thomas M.; Mace, Thomas H.

1980-11-01

The US National Park Service must map forest cover types over extensive areas in order to fulfill its goal of maintaining or reconstructing presettlement vegetation within national parks and monuments. Furthermore, such cover type maps must be updated on a regular basis to document vegetation changes. Computer-aided classification of small scale aerial photography is a promising technique for generating forest cover type maps efficiently and inexpensively. In this study, seven cover types were classified with an overall accuracy of 62 percent from a reproduction of a 1∶120,000 color infrared transparency of a conifer-hardwood forest. The results were encouraging, given the degraded quality of the photograph and the fact that features were not centered, as well as the lack of information on lens vignetting characteristics to make corrections. Suggestions are made for resolving these problems in future research and applications. In addition, it is hypothesized that the overall accuracy is artificially low because the computer-aided classification more accurately portrayed the intermixing of cover types than the hand-drawn maps to which it was compared.
Application of support vector machines for copper potential mapping in Kerman region, Iran

NASA Astrophysics Data System (ADS)

Shabankareh, Mahdi; Hezarkhani, Ardeshir

2017-04-01

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.
Effects of diagnosis on treatment recommendations in chronic insomnia--a report from the APA/NIMH DSM-IV field trial.

PubMed

Buysse, D J; Reynolds, C F; Kupfer, D J; Thorpy, M J; Bixler, E; Kales, A; Manfredi, R; Vgontzas, A; Stepanski, E; Roth, T; Hauri, P; Stapf, D

1997-07-01

The objective of this study was to determine whether sleep specialists and nonspecialists recommend different treatments for different insomnia diagnoses according to two different diagnostic classifications. Two hundred sixteen patients with chronic insomnia at five sites were each interviewed by two clinicians: one sleep specialist and one nonsleep specialist. All interviewers indicated diagnoses using the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV); sleep specialists also indicated diagnoses according to the International Classification for Sleep Disorders (ICSD). Interviewers then indicated how strongly they would recommend each item in a standard list of treatment and diagnostic interventions for each patient. We examined differences in treatment recommendations among the six most common DSM-IV diagnoses assigned by sleep specialists at different sites (n = 192), among the six most common ICSD diagnoses assigned by sleep specialists at different sites (n = 153), and among the six most common DSM-IV diagnoses assigned by nonspecialists at different sites (n = 186). In each analysis, specific treatment and polysomnography recommendations differed significantly for different diagnoses, using either DSM-IV or ICSD criteria. Conversely, different diagnoses were associated with different rank orderings of specific treatment and diagnostic recommendations. Sleep specialist and nonspecialist interviewers each distinguished treatment recommendations among different diagnoses, but in general, nonspecialists more strongly recommended medications and relaxation treatments. Significant site-related differences in treatment recommendations also emerged. Differences in treatment recommendations support the distinction between different DSM-IV and ICSD diagnoses, although they do not provide formal validation. Site-related differences suggest a lack of consensus in how these disorders are conceptualized and treated.
Characterization and delineation of caribou habitat on Unimak Island using remote sensing techniques

NASA Astrophysics Data System (ADS)

Atkinson, Brain M.

The assessment of herbivore habitat quality is traditionally based on quantifying the forages available to the animal across their home range through ground-based techniques. While these methods are highly accurate, they can be time-consuming and highly expensive, especially for herbivores that occupy vast spatial landscapes. The Unimak Island caribou herd has been decreasing in the last decade at rates that have prompted discussion of management intervention. Frequent inclement weather in this region of Alaska has provided for little opportunity to study the caribou forage habitat on Unimak Island. The overall objectives of this study were two-fold 1) to assess the feasibility of using high-resolution color and near-infrared aerial imagery to map the forage distribution of caribou habitat on Unimak Island and 2) to assess the use of a new high-resolution multispectral satellite imagery platform, RapidEye, and use of the "red-edge" spectral band on vegetation classification accuracy. Maximum likelihood classification algorithms were used to create land cover maps in aerial and satellite imagery. Accuracy assessments and transformed divergence values were produced to assess vegetative spectral information and classification accuracy. By using RapidEye and aerial digital imagery in a hierarchical supervised classification technique, we were able to produce a high resolution land cover map of Unimak Island. We obtained overall accuracy rates of 71.4 percent which are comparable to other land cover maps using RapidEye imagery. The "red-edge" spectral band included in the RapidEye imagery provides additional spectral information that allows for a more accurate overall classification, raising overall accuracy 5.2 percent.
Inventory and comparative evaluation of seabed mapping, classification and modeling activities in the Northwest Atlantic, USA to support regional ocean planning

NASA Astrophysics Data System (ADS)

Shumchenia, Emily J.; Guarinello, Marisa L.; Carey, Drew A.; Lipsky, Andrew; Greene, Jennifer; Mayer, Larry; Nixon, Matthew E.; Weber, John

2015-06-01

Efforts are in motion globally to address coastal and marine management needs through spatial planning and concomitant seabed habitat mapping. Contrasting strategies are often evident in these processes among local, regional, national and international scientific approaches and policy needs. In answer to such contrasts among its member states, the United States Northeast Regional Ocean Council formed a Habitat Working Group to conduct a regional inventory and comparative evaluation of seabed characterization, classification, and modeling activities in New England. The goals of this effort were to advance regional understanding of ocean habitats and identify opportunities for collaboration. Working closely with the Habitat Working Group, we organized and led the inventory and comparative analysis with a focus on providing processes and tools that can be used by scientists and managers, updated and adapted for future use, and applied in other ocean management regions throughout the world. Visual schematics were a critical component of the comparative analysis and aided discussion among scientists and managers. Regional consensus was reached on a common habitat classification scheme (U.S. Coastal and Marine Ecological Classification Standard) for regional seabed maps. Results and schematics were presented at a region-wide workshop where further steps were taken to initiate collaboration among projects. The workshop culminated in an agreement on a set of future seabed mapping goals for the region. The work presented here may serve as an example to other ocean planning regions in the U.S., Europe or elsewhere seeking to integrate a variety of seabed characterization, classification and modeling activities.
Mapping gully-affected areas in the region of Taroudannt, Morocco based on Object-Based Image Analysis (OBIA)

NASA Astrophysics Data System (ADS)

d'Oleire-Oltmanns, Sebastian; Marzolff, Irene; Tiede, Dirk; Blaschke, Thomas

2015-04-01

The need for area-wide landform mapping approaches, especially in terms of land degradation, can be ascribed to the fact that within area-wide landform mapping approaches, the (spatial) context of erosional landforms is considered by providing additional information on the physiography neighboring the distinct landform. This study presents an approach for the detection of gully-affected areas by applying object-based image analysis in the region of Taroudannt, Morocco, which is highly affected by gully erosion while simultaneously representing a major region of agro-industry with a high demand of arable land. Various sensors provide readily available high-resolution optical satellite data with a much better temporal resolution than 3D terrain data which lead to the development of an area-wide mapping approach to extract gully-affected areas using only optical satellite imagery. The classification rule-set was developed with a clear focus on virtual spatial independence within the software environment of eCognition Developer. This allows the incorporation of knowledge about the target objects under investigation. Only optical QuickBird-2 satellite data and freely-available OpenStreetMap (OSM) vector data were used as input data. The OSM vector data were incorporated in order to mask out plantations and residential areas. Optical input data are more readily available for a broad range of users compared to terrain data, which is considered to be a major advantage. The methodology additionally incorporates expert knowledge and freely-available vector data in a cyclic object-based image analysis approach. This connects the two fields of geomorphology and remote sensing. The classification results allow conclusions on the current distribution of gullies. The results of the classification were checked against manually delineated reference data incorporating expert knowledge based on several field campaigns in the area, resulting in an overall classification accuracy of 62%. The error of omission accounts for 38% and the error of commission for 16%, respectively. Additionally, a manual assessment was carried out to assess the quality of the applied classification algorithm. The limited error of omission contributes with 23% to the overall error of omission and the limited error of commission contributes with 98% to the overall error of commission. This assessment improves the results and confirms the high quality of the developed approach for area-wide mapping of gully-affected areas in larger regions. In the field of landform mapping, the overall quality of the classification results is often assessed with more than one method to incorporate all aspects adequately.
Classification of mood disorders in DSM-V and DSM-VI.

PubMed

Joyce, Peter R

2008-10-01

For any diagnostic system to be clinically useful, and go beyond description, it must provide an understanding that informs about aetiology and/or outcome. DSM-III and DSM-IV have provided reliability; the challenge for DSM-V and DSM-VI will be to provide validity. For DSM-V this will not be achieved. Believers in DSM-III and DSM-IV have impeded progress towards a valid classification system, so DSM-V needs to retain continuity with its predecessors to retain reliability and enhance research, but position itself to inform a valid diagnostic system by DSM-VI. This review examines the features of a diagnostic system and summarizes what is really known about mood disorders. The review also questions whether what are called mood disorders are primarily disorders of mood. Finally, it provides suggestions for DSM-VI.
Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story.

PubMed

Emami Riedmaier, Arian; Lindley, David J; Hall, Jeffrey A; Castleberry, Steven; Slade, Russell T; Stuart, Patricia; Carr, Robert A; Borchardt, Thomas B; Bow, Daniel A J; Nijsen, Marjoleen

2018-01-01

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition.

PubMed

Chen, WenXue; Lou, HaiYan; Zhang, HongPing; Nie, Xiu; Lan, WenXian; Yang, YongXia; Xiang, Yun; Qi, JianPin; Lei, Hao; Tang, HuiRu; Chen, FenEr; Deng, Feng

2011-07-01

Clinical data have shown that survival rates vary considerably among brain tumor patients, according to the type and grade of the tumor. Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS (1)H NMRS) can provide important information on tumor biology and metabolism. These metabolic fingerprints can then be used for tumor classification and grading, with great potential value for tumor diagnosis. We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies, including two astrocytomas (grade I), 12 astrocytomas (grade II), eight anaplastic astrocytomas (grade III), three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS (1)H NMRS. The results were correlated with pathological features using multivariate data analysis, including principal component analysis (PCA). There were significant differences in the levels of N-acetyl-aspartate (NAA), creatine, myo-inositol, glycine and lactate between tumors of different grades (P<0.05). There were also significant differences in the ratios of NAA/creatine, lactate/creatine, myo-inositol/creatine, glycine/creatine, scyllo-inositol/creatine and alanine/creatine (P<0.05). A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%. HRMAS (1)H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.
Classification of parotidectomies: a proposal of the European Salivary Gland Society.

PubMed

Quer, M; Guntinas-Lichius, O; Marchal, F; Vander Poorten, V; Chevalier, D; León, X; Eisele, D; Dulguerov, P

2016-10-01

The objective of this study is to provide a comprehensive classification system for parotidectomy operations. Data sources include Medline publications, author's experience, and consensus round table at the Third European Salivary Gland Society (ESGS) Meeting. The Medline database was searched with the term "parotidectomy" and "definition". The various definitions of parotidectomy procedures and parotid gland subdivisions extracted. Previous classification systems re-examined and a new classification proposed by a consensus. The ESGS proposes to subdivide the parotid parenchyma in five levels: I (lateral superior), II (lateral inferior), III (deep inferior), IV (deep superior), V (accessory). A new classification is proposed where the type of resection is divided into formal parotidectomy with facial nerve dissection and extracapsular dissection. Parotidectomies are further classified according to the levels removed, as well as the extra-parotid structures ablated. A new classification of parotidectomy procedures is proposed.
Monitoring Geothermal Features in Yellowstone National Park with ATLAS Multispectral Imagery

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Berglund, Judith

2000-01-01

The National Park Service (NPS) must produce an Environmental Impact Statement for each proposed development in the vicinity of known geothermal resource areas (KGRAs) in Yellowstone National Park. In addition, the NPS monitors indicator KGRAs for environmental quality and is still in the process of mapping many geothermal areas. The NPS currently maps geothermal features with field survey techniques. High resolution aerial multispectral remote sensing in the visible, NIR, SWIR, and thermal spectral regions could enable YNP geothermal features to be mapped more quickly and in greater detail In response, Yellowstone Ecosystems Studies, in partnership with NASA's Commercial Remote Sensing Program, is conducting a study on the use of Airborne Terrestrial Applications Sensor (ATLAS) multispectral data for monitoring geothermal features in the Upper Geyser Basin. ATLAS data were acquired at 2.5 meter resolution on August 17, 2000. These data were processed into land cover classifications and relative temperature maps. For sufficiently large features, the ATLAS data can map geothermal areas in terms of geyser pools and hot springs, plus multiple categories of geothermal runoff that are apparently indicative of temperature gradients and microbial matting communities. In addition, the ATLAS maps clearly identify geyserite areas. The thermal bands contributed to classification success and to the computation of relative temperature. With masking techniques, one can assess the influence of geothermal features on the Firehole River. Preliminary results appear to confirm ATLAS data utility for mapping and monitoring geothermal features. Future work will include classification refinement and additional validation.
Comparing automated classification and digitization approaches to detect change in eelgrass bed extent during restoration of a large river delta

USGS Publications Warehouse

Davenport, Anna Elizabeth; Davis, Jerry D.; Woo, Isa; Grossman, Eric; Barham, Jesse B.; Ellings, Christopher S.; Takekawa, John Y.

2017-01-01

Native eelgrass (Zostera marina) is an important contributor to ecosystem services that supplies cover for juvenile fish, supports a variety of invertebrate prey resources for fish and waterbirds, provides substrate for herring roe consumed by numerous fish and birds, helps stabilize sediment, and sequesters organic carbon. Seagrasses are in decline globally, and monitoring changes in their growth and extent is increasingly valuable to determine impacts from large-scale estuarine restoration and inform blue carbon mapping initiatives. Thus, we examined the efficacy of two remote sensing mapping methods with high-resolution (0.5 m pixel size) color near infrared imagery with ground validation to assess change following major tidal marsh restoration. Automated classification of false color aerial imagery and digitized polygons documented a slight decline in eelgrass area directly after restoration followed by an increase two years later. Classification of sparse and low to medium density eelgrass was confounded in areas with algal cover, however large dense patches of eelgrass were well delineated. Automated classification of aerial imagery from unsupervised and supervised methods provided reasonable accuracies of 73% and hand-digitizing polygons from the same imagery yielded similar results. Visual clues for hand digitizing from the high-resolution imagery provided as reliable a map of dense eelgrass extent as automated image classification. We found that automated classification had no advantages over manual digitization particularly because of the limitations of detecting eelgrass with only three bands of imagery and near infrared.
Quantitative classification of a historic northern Wisconsin (U.S.A.) landscape: mapping forests at regional scales

Treesearch

Lisa A. Schulte; David J. Mladenoff; Erik V. Nordheim

2002-01-01

We developed a quantitative and replicable classification system to improve understanding of historical composition and structure within northern Wisconsin's forests. The classification system was based on statistical cluster analysis and two forest metrics, relative dominance (% basal area) and relative importance (mean of relative dominance and relative density...
Comparing the performance of various digital soil mapping approaches to map physical soil properties

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Takács, Katalin; Pásztor, László

2015-04-01

Spatial information on physical soil properties is intensely expected, in order to support environmental related and land use management decisions. One of the most widely used properties to characterize soils physically is particle size distribution (PSD), which determines soil water management and cultivability. According to their size, different particles can be categorized as clay, silt, or sand. The size intervals are defined by national or international textural classification systems. The relative percentage of sand, silt, and clay in the soil constitutes textural classes, which are also specified miscellaneously in various national and/or specialty systems. The most commonly used is the classification system of the United States Department of Agriculture (USDA). Soil texture information is essential input data in meteorological, hydrological and agricultural prediction modelling. Although Hungary has a great deal of legacy soil maps and other relevant soil information, it often occurs, that maps do not exist on a certain characteristic with the required thematic and/or spatial representation. The recent developments in digital soil mapping (DSM), however, provide wide opportunities for the elaboration of object specific soil maps (OSSM) with predefined parameters (resolution, accuracy, reliability etc.). Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil map. This suggests the opportunity of optimization. For the creation of an OSSM one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). We started comprehensive analysis of the effects of the various DSM components on the accuracy of the output maps on pilot areas. The aim of this study is to compare and evaluate different digital soil mapping methods and sets of ancillary variables for producing the most accurate spatial prediction of texture classes in a given area of interest. Both legacy and recently collected data on PSD were used as reference information. The predictor variable data set consisted of digital elevation model and its derivatives, lithology, land use maps as well as various bands and indices of satellite images. Two conceptionally different approaches can be applied in the mapping process. Textural classification can be realized after particle size data were spatially extended by proper geostatistical method. Alternatively, the textural classification is carried out first, followed by the spatial extension through suitable data mining method. According to the first approach, maps of sand, silt and clay percentage have been computed through regression kriging (RK). Since the three maps are compositional (their sum must be 100%), we applied Additive Log-Ratio (alr) transformation, instead of kriging them independently. Finally, the texture class map has been compiled according to the USDA categories from the three maps. Different combinations of reference and training soil data and auxiliary covariables resulted several different maps. On the basis of the other way, the PSD were classified firstly into the USDA categories, then the texture class maps were compiled directly by data mining methods (classification trees and random forests). The various results were compared to each other as well as to the RK maps. The performance of the different methods and data sets has been examined by testing the accuracy of the geostatistically computed and the directly classified results to assess the most predictive and accurate method. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).
Methods of Technological Forecasting,

DTIC Science & Technology

1977-05-01

Trend Extrapolation Progress Curve Analogy Trend Correlation Substitution Analysis or Substitution Growth Curves Envelope Curve Advances in the State of...the Art Technological Mapping Contextual Mapping Matrix Input-Output Analysis Mathematical Models Simulation Models Dynamic Modelling. CHAPTER IV...Generation Interaction between Needs and Possibilities Map of the Technological Future — (‘ross- Impact Matri x Discovery Matrix Morphological Analysis
44 CFR 65.17 - Review of determinations.

Code of Federal Regulations, 2010 CFR

2010-10-01

... determination; and (5) A copy of the effective NFIP map (Flood Hazard Boundary Map (FHBM) or Flood Insurance...) The name of the NFIP community in which the building or manufactured home is located; (ii) The... applies; (iii) The NFIP map panel number and effective date upon which the determination is based; (iv) A...
44 CFR 65.17 - Review of determinations.

Code of Federal Regulations, 2014 CFR

2014-10-01

... determination; and (5) A copy of the effective NFIP map (Flood Hazard Boundary Map (FHBM) or Flood Insurance...) The name of the NFIP community in which the building or manufactured home is located; (ii) The... applies; (iii) The NFIP map panel number and effective date upon which the determination is based; (iv) A...
44 CFR 65.17 - Review of determinations.

Code of Federal Regulations, 2012 CFR

2012-10-01

... determination; and (5) A copy of the effective NFIP map (Flood Hazard Boundary Map (FHBM) or Flood Insurance...) The name of the NFIP community in which the building or manufactured home is located; (ii) The... applies; (iii) The NFIP map panel number and effective date upon which the determination is based; (iv) A...

44 CFR 65.17 - Review of determinations.

Code of Federal Regulations, 2011 CFR

2011-10-01

... determination; and (5) A copy of the effective NFIP map (Flood Hazard Boundary Map (FHBM) or Flood Insurance...) The name of the NFIP community in which the building or manufactured home is located; (ii) The... applies; (iii) The NFIP map panel number and effective date upon which the determination is based; (iv) A...
44 CFR 65.17 - Review of determinations.

Code of Federal Regulations, 2013 CFR

2013-10-01

... determination; and (5) A copy of the effective NFIP map (Flood Hazard Boundary Map (FHBM) or Flood Insurance...) The name of the NFIP community in which the building or manufactured home is located; (ii) The... applies; (iii) The NFIP map panel number and effective date upon which the determination is based; (iv) A...
A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

NASA Astrophysics Data System (ADS)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.
A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.
A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

PubMed Central

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520
Real-time, resource-constrained object classification on a micro-air vehicle

NASA Astrophysics Data System (ADS)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.
Snow mapping and land use studies in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A system was developed for operational snow and land use mapping, based on a supervised classification method using various classification algorithms and representation of the results in maplike form on color film with a photomation system. Land use mapping, under European conditions, was achieved with a stepwise linear discriminant analysis by using additional ratio variables. On fall images, signatures of built-up areas were often not separable from wetlands. Two different methods were tested to correlate the size of settlements and the population with an accuracy for the densely populated Swiss Plateau between +2 or -12%.
Digital elevation data as an aid to land use and land cover classification

USGS Publications Warehouse

Colvocoresses, Alden P.

1981-01-01

In relatively well mapped areas such as the United States and Europe, digital data can be developed from topographic maps or from the stereo aerial photographic movie. For poorer mapped areas (which involved most of the world's land areas), a satellite designed to obtain stereo data offers the best hope for a digital elevation database. Such a satellite, known as Mapsat, has been defined by the U.S. Geological Survey. Utilizing modern solid state technology, there is no reason why such stereo data cannot be acquired simultaneously with the multispectral response, thus simplifying the overall problem of land use and land cover classification.
A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.
Rapid Crop Cover Mapping for the Conterminous United States.

PubMed

Dahal, Devendra; Wylie, Bruce; Howard, Danny

2018-06-05

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.
Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

NASA Technical Reports Server (NTRS)

Rignot, Eric; Williams, Cynthia; Way, Jobea; Viereck, Leslie

1993-01-01

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.
Mapping grass communities based on multi-temporal Landsat TM imagery and environmental variables

NASA Astrophysics Data System (ADS)

Zeng, Yuandi; Liu, Yanfang; Liu, Yaolin; de Leeuw, Jan

2007-06-01

Information on the spatial distribution of grass communities in wetland is increasingly recognized as important for effective wetland management and biological conservation. Remote sensing techniques has been proved to be an effective alternative to intensive and costly ground surveys for mapping grass community. However, the mapping accuracy of grass communities in wetland is still not preferable. The aim of this paper is to develop an effective method to map grass communities in Poyang Lake Natural Reserve. Through statistic analysis, elevation is selected as an environmental variable for its high relationship with the distribution of grass communities; NDVI stacked from images of different months was used to generate Carex community map; the image in October was used to discriminate Miscanthus and Cynodon communities. Classifications were firstly performed with maximum likelihood classifier using single date satellite image with and without elevation; then layered classifications were performed using multi-temporal satellite imagery and elevation with maximum likelihood classifier, decision tree and artificial neural network separately. The results show that environmental variables can improve the mapping accuracy; and the classification with multitemporal imagery and elevation is significantly better than that with single date image and elevation (p=0.001). Besides, maximum likelihood (a=92.71%, k=0.90) and artificial neural network (a=94.79%, k=0.93) perform significantly better than decision tree (a=86.46%, k=0.83).
Page layout analysis and classification for complex scanned documents

NASA Astrophysics Data System (ADS)

Erkilinc, M. Sezer; Jaber, Mustafa; Saber, Eli; Bauer, Peter; Depalov, Dejan

2011-09-01

A framework for region/zone classification in color and gray-scale scanned documents is proposed in this paper. The algorithm includes modules for extracting text, photo, and strong edge/line regions. Firstly, a text detection module which is based on wavelet analysis and Run Length Encoding (RLE) technique is employed. Local and global energy maps in high frequency bands of the wavelet domain are generated and used as initial text maps. Further analysis using RLE yields a final text map. The second module is developed to detect image/photo and pictorial regions in the input document. A block-based classifier using basis vector projections is employed to identify photo candidate regions. Then, a final photo map is obtained by applying probabilistic model based on Markov random field (MRF) based maximum a posteriori (MAP) optimization with iterated conditional mode (ICM). The final module detects lines and strong edges using Hough transform and edge-linkages analysis, respectively. The text, photo, and strong edge/line maps are combined to generate a page layout classification of the scanned target document. Experimental results and objective evaluation show that the proposed technique has a very effective performance on variety of simple and complex scanned document types obtained from MediaTeam Oulu document database. The proposed page layout classifier can be used in systems for efficient document storage, content based document retrieval, optical character recognition, mobile phone imagery, and augmented reality.
Direct visualization of in vitro drug mobilization from Lescol XL tablets using two-dimensional (19)F and (1)H magnetic resonance imaging.

PubMed

Chen, Chen; Gladden, Lynn F; Mantle, Michael D

2014-02-03

This article reports the application of in vitro multinuclear ((19)F and (1)H) two-dimensional magnetic resonance imaging (MRI) to study both dissolution media ingress and drug egress from a commercial Lescol XL extended release tablet in a United States Pharmacopeia Type IV (USP-IV) dissolution cell under pharmacopoeial conditions. Noninvasive spatial maps of tablet swelling and dissolution, as well as the mobilization and distribution of the drug are quantified and visualized. Two-dimensional active pharmaceutical ingredient (API) mobilization and distribution maps were obtained via (19)F MRI. (19)F API maps were coregistered with (1)H T2-relaxation time maps enabling the simultaneous visualization of drug distribution and gel layer dynamics within the swollen tablet. The behavior of the MRI data is also discussed in terms of its relationship to the UV drug release behavior.
A Comparison of the Effects of Intraosseous and Intravenous 5% Albumin on Infusion Time and Hemodynamic Measures in a Swine Model of Hemorrhagic Shock.

PubMed

Muir, Stacy L; Sheppard, Lance B; Maika-Wilson, Anne; Burgert, James M; Garcia-Blanco, Jose; Johnson, Arthur D; Coyner, Jennifer L

2016-08-01

Introduction Obtaining intravenous (IV) access in patients in hemorrhagic shock is often difficult and prolonged. Failed IV attempts delay life-saving treatment. Intraosseous (IO) access may often be obtained faster than IV access. Albumin (5%) is an option for prehospital volume expansion because of the absence of interference with coagulation and platelet function. Hypothesis/Problem There are limited data comparing the performance of IO and IV administered 5% albumin. The aims of this study were to compare the effects of tibial IO (TIO) and IV administration of 500 mL of 5% albumin on infusion time and hemodynamic measurements of heart rate (HR), mean arterial pressure (MAP), cardiac output (CO), and stroke volume (SV) in a swine model of hemorrhagic shock. Sixteen male swine were divided into two groups: TIO and IV. All subjects were anesthetized and a Class III hemorrhage was achieved by exsanguination of 31% of estimated blood volume (EBV) from a femoral artery catheter. Following exsanguination, 500 mL of 5% albumin was administered under pressurized infusion (300 mmHg) by the TIO or IV route and infusion time was recorded. Hemodynamic measurements of HR, MAP, CO, and SV were collected before and after exsanguination and every 20 seconds for 180 seconds during 5% albumin infusion. An independent t-test determined that IV 5% albumin infusion was significantly faster compared to IO (P=.01). Mean infusion time for TIO was seven minutes 35 seconds (SD=two minutes 44 seconds) compared to four minutes 32 seconds (SD=one minute 08 seconds) in the IV group. Multivariate Analysis of Variance was performed on hemodynamic data collected during the 5% albumin infusion. Analyses indicated there were no significant differences between the TIO and IV groups relative to MAP, CO, HR, or SV (P>.05). While significantly longer to infuse 5% albumin by the TIO route, the longer TIO infusion time may be negated as IO devices can be placed more quickly compared to repeated IV attempts. The lack of significant difference between the TIO and IV routes relative to hemodynamic measures indicate the TIO route is a viable route for the infusion of 5% albumin in a swine model of Class III hemorrhage. Muir SL , Sheppard LB , Maika-Wilson A , Burgert JM , Garcia-Blanco J , Johnson AD , Coyner JL . A comparison of the effects of intraosseous and intravenous 5% albumin on infusion time and hemodynamic measures in a swine model of hemorrhagic shock. Prehosp Disaster Med. 2016;31(4):436-442.
Application transfer activity in Missouri

NASA Technical Reports Server (NTRS)

Barr, D. J.

1977-01-01

Land use mapping of Missouri from LANDSAT imagery was investigated. Land resource classification included the inventory of mined land, accomplished with infrared aerial photography, plus topographic, geologic and hydrologic maps.
Cross-Disciplinary Analysis of Lymph Node Classification in Lung Cancer on CT Scanning.

PubMed

El-Sherief, Ahmed H; Lau, Charles T; Obuchowski, Nancy A; Mehta, Atul C; Rice, Thomas W; Blackstone, Eugene H

2017-04-01

Accurate and consistent regional lymph node classification is an important element in the staging and multidisciplinary management of lung cancer. Regional lymph node definition sets-lymph node maps-have been created to standardize regional lymph node classification. In 2009, the International Association for the Study of Lung Cancer (IASLC) introduced a lymph node map to supersede all preexisting lymph node maps. Our aim was to study if and how lung cancer specialists apply the IASLC lymph node map when classifying thoracic lymph nodes encountered on CT scans during lung cancer staging. From April 2013 through July 2013, invitations were distributed to all members of the Fleischner Society, Society of Thoracic Radiology, General Thoracic Surgical Club, and the American Association of Bronchology and Interventional Pulmonology to participate in an anonymous online image-based and text-based 20-question survey regarding lymph node classification for lung cancer staging on CT imaging. Three hundred thirty-seven people responded (approximately 25% participation). Respondents consisted of self-reported thoracic radiologists (n = 158), thoracic surgeons (n = 102), and pulmonologists who perform endobronchial ultrasonography (n = 77). Half of the respondents (50%; 95% CI, 44%-55%) reported using the IASLC lymph node map in daily practice, with no significant differences between subspecialties. A disparity was observed between the IASLC definition sets and their interpretation and application on CT scans, in particular for lymph nodes near the thoracic inlet, anterior to the trachea, anterior to the tracheal bifurcation, near the ligamentum arteriosum, between the bronchus intermedius and esophagus, in the internal mammary space, and adjacent to the heart. Use of older lymph node maps and inconsistencies in interpretation and application of definitions in the IASLC lymph node map may potentially lead to misclassification of stage and suboptimal management of lung cancer in some patients. Published by Elsevier Inc.
A multi-epoch spectroscopic study of the BAL quasar APM 08279+5255. II. Emission- and absorption-line variability time lags

NASA Astrophysics Data System (ADS)

Saturni, F. G.; Trevese, D.; Vagnetti, F.; Perna, M.; Dadina, M.

2016-03-01

Context. The study of high-redshift bright quasars is crucial to gather information about the history of galaxy assembly and evolution. Variability analyses can provide useful data on the physics of quasar processes and their relation with the host galaxy. Aims: In this study, we aim to measure the black hole mass of the bright lensed BAL QSO APM 08279+5255 at z = 3.911 through reverberation mapping, and to update and extend the monitoring of its C IV absorption line variability. Methods: We perform the first reverberation mapping of the Si IV and C IV emission lines for a high-luminosity quasar at high redshift with the use of 138 R-band photometric data and 30 spectra available over 16 years of observations. We also cross-correlate the C IV absorption equivalent width variations with the continuum light curve to estimate the recombination time lags of the various absorbers and infer the physical conditions of the ionised gas. Results: We find a reverberation-mapping time lag of ~900 rest-frame days for both Si IV and C IV emission lines. This is consistent with an extension of the BLR size-to-luminosity relation for active galactic nuclei up to a luminosity of ~1048 erg s-1, and implies a black hole mass of 1010 M⊙. Additionally, we measure a recombination time lag of ~160 days in the rest frame for the C IV narrow absorption system, which implies an electron density of the absorbing gas of ~2.5 × 104 cm-3. Conclusions: The measured black hole mass of APM 08279+5255 indicates that the quasar resides in an under-massive host-galaxy bulge with Mbulge ~ 7.5MBH, and that the lens magnification is lower than ~8. Finally, the inferred electron density of the narrow-line absorber implies a distance of the order of 10 kpc of the absorbing gas from the quasar, placing it within the host galaxy.
In-vehicle signing concepts: An analytical precursor to an in-vehicle information system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spelt, P.F.; Tufano, D.R.; Knee, H.E.

The purpose of the project described in this report is to develop alternative In-Vehicle Signing (IVS) system concepts based on allocation of the functions associated with driving a road vehicle. In the driving milieu, tasks can be assigned to one of three agents, the driver, the vehicle or the infrastructure. Assignment of tasks is based on a philosophy of function allocation which can emphasize any of several philosophical approaches. In this project, function allocations were made according to the current practice in vehicle design and signage as well as a human-centered strategy. Several IVS system concepts are presented based onmore » differing functional allocation outcomes. A design space for IVS systems is described, and a technical analysis of a map-based and sever beacon-based IVS systems are presented. Because of problems associated with both map-based and beacon-based concepts, a hybrid IVS concept was proposed. The hybrid system uses on-board map-based databases to serve those areas in which signage can be anticipated to be relatively static, such as large metropolitan areas where few if any new roads will be built. For areas where sign density is low, and/or where population growth causes changes in traffic flow, beacon-based concepts function best. For this situation, changes need only occur in the central database from which sign information is transmitted. This report presents system concepts which enable progress from the IVS system concept-independent functional requirements to a more specific set of system concepts which facilitate analysis and selection of hardware and software to perform the functions of IVS. As such, this phase of the project represents a major step toward the design and development of a prototype WS system. Once such a system is developed, a program of testing, evaluation, an revision will be undertaken. Ultimately, such a system can become part of the road vehicle of the future.« less
Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar

NASA Astrophysics Data System (ADS)

Fedrigo, Melissa; Newnham, Glenn J.; Coops, Nicholas C.; Culvenor, Darius S.; Bolton, Douglas K.; Nitschke, Craig R.

2018-02-01

Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen's kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the 'ecotone') between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation.

Classification Techniques for Digital Map Compression

DTIC Science & Technology

1989-03-01

classification improved the performance of the K-means classification algorithm resulting in a compression of 8.06:1 with Lempel - Ziv coding. Run-length coding... compression performance are run-length coding [2], [8] and Lempel - Ziv coding 110], [11]. These techniques are chosen because they are most efficient when...investigated. After the classification, some standard file compression methods, such as Lempel - Ziv and run-length encoding were applied to the
A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.
A Case Study in Integrating Multiple E-commerce Standards via Semantic Web Technology

NASA Astrophysics Data System (ADS)

Yu, Yang; Hillman, Donald; Setio, Basuki; Heflin, Jeff

Internet business-to-business transactions present great challenges in merging information from different sources. In this paper we describe a project to integrate four representative commercial classification systems with the Federal Cataloging System (FCS). The FCS is used by the US Defense Logistics Agency to name, describe and classify all items under inventory control by the DoD. Our approach uses the ECCMA Open Technical Dictionary (eOTD) as a common vocabulary to accommodate all different classifications. We create a semantic bridging ontology between each classification and the eOTD to describe their logical relationships in OWL DL. The essential idea is that since each classification has formal definitions in a common vocabulary, we can use subsumption to automatically integrate them, thus mitigating the need for pairwise mappings. Furthermore our system provides an interactive interface to let users choose and browse the results and more importantly it can translate catalogs that commit to these classifications using compiled mapping results.
Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

PubMed Central

Kobryn, Halina T.; Wouters, Kristin; Beckley, Lynnath E.; Heege, Thomas

2013-01-01

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km2 of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km2). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921
Using remote sensing in support of environmental management: A framework for selecting products, algorithms and methods.

PubMed

de Klerk, Helen M; Gilbertson, Jason; Lück-Vogel, Melanie; Kemp, Jaco; Munch, Zahn

2016-11-01

Traditionally, to map environmental features using remote sensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single 'best performer' from which the final map is made. We use a combination of an omission/commission plot to evaluate various results and compile a probability map based on consistently strong performing models across a range of standard accuracy measures. We suggest that this easy-to-use approach can be applied in any study using remote sensing to map natural features for management action. We demonstrate this approach using optical remote sensing products of different spatial and spectral resolution to map the endemic and threatened flora of quartz patches in the Knersvlakte, South Africa. Quartz patches can be mapped using either SPOT 5 (used due to its relatively fine spatial resolution) or Landsat8 imagery (used because it is freely accessible and has higher spectral resolution). Of the variety of classification algorithms available, we tested maximum likelihood and support vector machine, and applied these to raw spectral data, the first three PCA summaries of the data, and the standard normalised difference vegetation index. We found that there is no 'one size fits all' solution to the choice of a 'best fit' model (i.e. combination of classification algorithm or data sets), which is in agreement with the literature that classifier performance will vary with data properties. We feel this lends support to our suggestion that rather than the identification of a 'single best' model and a map based on this result alone, a probability map based on the range of consistently top performing models provides a rigorous solution to environmental mapping. Copyright © 2016 Elsevier Ltd. All rights reserved.
National Seabed Mapping Programmes Collaborate to Advance Marine Geomorphological Mapping in Adjoining European Seas

NASA Astrophysics Data System (ADS)

Monteys, X.; Guinan, J.; Green, S.; Gafeira, J.; Dove, D.; Baeten, N. J.; Thorsnes, T.

2017-12-01

Marine geomorphological mapping is an effective means of characterising and understanding the seabed and its features with direct relevance to; offshore infrastructure placement, benthic habitat mapping, conservation & policy, marine spatial planning, fisheries management and pure research. Advancements in acoustic survey techniques and data processing methods resulting in the availability of high-resolution marine datasets e.g. multibeam echosounder bathymetry and shallow seismic mean that geological interpretations can be greatly improved by combining with geomorphological maps. Since December 2015, representatives from the national seabed mapping programmes of Norway (MAREANO), Ireland (INFOMAR) and the United Kingdom (MAREMAP) have collaborated and established the MIM geomorphology working group) with the common aim of advancing best practice for geological mapping in their adjoining sea areas in north-west Europe. A recently developed two-part classification system for Seabed Geomorphology (`Morphology' and Geomorphology') has been established as a result of an initiative led by the British Geological Survey (BGS) with contributions from the MIM group (Dove et al. 2016). To support the scheme, existing BGS GIS tools (SIGMA) have been adapted to apply this two-part classification system and here we present on the tools effectiveness in mapping geomorphological features, along with progress in harmonising the classification and feature nomenclature. Recognising that manual mapping of seabed features can be time-consuming and subjective, semi-automated approaches for mapping seabed features and improving mapping efficiency is being developed using Arc-GIS based tools. These methods recognise, spatially delineate and morphologically describe seabed features such as pockmarks (Gafeira et al., 2012) and cold-water coral mounds. Such tools utilise multibeam echosounder data or any other bathymetric dataset (e.g. 3D seismic, Geldof et al., 2014) that can produce a depth digital model. The tools have the capability to capture an extensive list of morphological attributes. The MIM geomorphology working group's strategy to develop methods for more efficient marine geomorphological mapping is presented with data examples and case studies showing the latest results.
HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Tilton, James C.; Tan, Bin; Brown De Colstoun, Eric C.

2017-01-01

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.
Spectral signature selection for mapping unvegetated soils

NASA Technical Reports Server (NTRS)

May, G. A.; Petersen, G. W.

1975-01-01

Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.
Object-Based Classification and Change Detection of Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Park, J. G.; Harada, I.; Kwak, Y.

2016-06-01

Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.
The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.
Deconvolution single shot multibox detector for supermarket commodity detection and classification

NASA Astrophysics Data System (ADS)

Li, Dejian; Li, Jian; Nie, Binling; Sun, Shouqian

2017-07-01

This paper proposes an image detection model to detect and classify supermarkets shelves' commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.
Differential expression of basement membrane type IV collagen α2 and α6 chains as a prognostic factor in patients with extrahepatic bile duct carcinoma.

PubMed

Hirashima, Kotaro; Iyama, Ken-Ichi; Baba, Yoshifumi; Honda, Yumi; Sado, Yoshikazu; Ninomiya, Yoshifumi; Watanabe, Masayuki; Takamori, Hiroshi; Beppu, Toru; Baba, Hideo

2013-03-01

The destruction of the basement membrane (BM) is the first step in cancer invasion and metastasis. Type IV collagen is a major component of the BM, and is composed of six genetically distinct α(IV) chains; α1(IV) to α6(IV). The loss of α5(IV) and α6(IV) chains from the epithelial BM at the early stage of cancer invasion has been reported in several types of cancers. However, the expression of α5(IV) and α6(IV) chains in extrahepatic bile duct carcinoma (EBDC) remains unclear. We examined the expression of α(IV) chains by immunohistochemistry using 71 resected EBDC specimens. Prognostic significance of α(IV) chains was examined by Cox regression and Kaplan-Meier analyses. In the invasive cancer, the expression of α6(IV) chain in the BM was lost partially or completely preceded by the loss of α2(IV) chain. The loss of α6(IV) chain in the BM of the invasive cancer was related to the tumor classification, TNM stages, and the expression of α2(IV) chain. The patients with α2(IV)-negative and α6(IV)-negative chains had significantly poorer prognosis than those with α2(IV)-positive and α6(IV)-positive/negative chains (P = 0.04). The loss of α2(IV) and α6(IV) chains might be a useful prognostic factor in patients with EBDC. Copyright © 2012 Wiley Periodicals, Inc.
Status and distribution of mangrove forests of the world using earth observation satellite data

USGS Publications Warehouse

Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N.

2011-01-01

Aim Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, compiled using disparate geospatial data sources and national statistics, need to be improved. Here, we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive.Methods We interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth-sun distance by converting the digital number values to the top-of-the-atmosphere reflectance. Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map 'true mangroves'.Results The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5?? N and 5?? S latitude.Main conclusions We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created. We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies. ?? 2010 Blackwell Publishing Ltd.
Status and distribution of mangrove forests of the world using earth observation satellite data

USGS Publications Warehouse

Giri, Chandra; Ochieng, E.; Tieszen, Larry L.; Zhu, Zhi-Liang; Singh, Ashbindu; Loveland, Thomas R.; Masek, Jeffery G.; Duke, Norm

2011-01-01

Aim Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, compiled using disparate geospatial data sources and national statistics, need to be improved. Here, we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive. Methods We interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth–sun distance by converting the digital number values to the top-of-the-atmosphere reflectance. Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map ‘true mangroves’. Results The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude. Main conclusions We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created. We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies.
Adaptive video-based vehicle classification technique for monitoring traffic : [executive summary].

DOT National Transportation Integrated Search

2015-08-01

Federal Highway Administration (FHWA) recommends axle-based classification standards to map : passenger vehicles, single unit trucks, and multi-unit trucks, at Automatic Traffic Recorder (ATR) stations : statewide. Many state Departments of Transport...
First steps towards a novel European forest fuel classification systems and a European forest fuel map

NASA Astrophysics Data System (ADS)

Sebastián-López, Ana; Urbieta, Itziar R.; de La Fuente Blanco, David; García Mateo, Rubén.; Moreno Rodríguez, José Manuel; Eftichidis, George; Varela, Vassiliki; Cesari, Véronique; Mário Ribeiro, Luís.; Viegas, Domingos Xavier; Lanorte, Antonio; Lasaponara, Rosa; Camia, Andrea; San Miguel, Jesús

2010-05-01

Forest fires burn at the local scale, but their massive occurrence causes effects which have global dimensions. Furthermore climate change projections associate global warming to a significant increase in forest fire activity. Warmer and drier conditions are expected to increase the frequency, duration and intensity of fires, and greater amounts of fuel associated with forest areas in decline may cause more frequent and larger fires. These facts create the need for establishing strategies for harmonizing fire danger rating, fire risk assessment, and fire prevention policies at a supranational level. Albeit forest fires are a permanent threat for European ecosystems, particularly in the south, there is no commonly accepted fuel classification scheme adopted for operational use by the Member States of the EU. The European Commission (EC) DG Environment and JRC have launched a set of studies following a resolution of the European Parliament on the further development and enhancement of the European Forest Fire Information System (EFFIS), the EC focal point for information on forest fires in Europe. One of the studies that are being funded is the FUELMAP project. The objective of FUELMAP is to develop a novel fuel classification system and a new European fuel map that will be based on a comprehensive classification of fuel complexes representing the various vegetation types across EU27, plus Switzerland, Croatia and Turkey. The overall work plan is grounded on a throughout knowledge of European forest landscapes and the key features of fuel situations occurring in natural areas. The method makes extended use of existing databases available in the Member States and European Institutions. Specifically, our proposed classification combines relevant information on ecoregions, land cover and uses, potential and actual vegetation, and stand structure. GIS techniques are used in order to define the geographic extent of the classification units and for identifying the main driving factors that determine the spatial distribution of the resulting fuel complexes. Furthermore, relevant parameters influencing fire potential and effects such as fuel load, live/dead ratio, and fuels' size classes' distribution are considered. National- and local-scale datasets (vegetation maps, forest inventory plots, fuel maps...) will be also studied and compared. Local ground- truth data will be used to assess the accuracy of the classification and will contribute, along with literature values and experts' opinion, to characterize the fuels' physical properties. The resulting classification aims to support the characterization of the fire potential, serve as input in fire emissions models, and be used to assess the expected impact of fire in the European landscapes. The work plan includes the development of a GIS software tool to automatically update the fuel map from modified (up-to-date) input data layers. The fuel map of Europe is mainly intended to support the implementation of the EFFIS modules that can be enhanced by the use of improved information on forest fuel properties and spatial distribution, though it is also envisaged that the results of the project might be useful for other relevant applications at different spatial scales. To this purpose, the classification will be designed with a hierarchical and flexible structure for describing heterogeneous landscapes. The work is on-going and this presentation shows the first results towards the envisaged European fuel map.
A Comparison of DSM-IV-TR and DSM-5 Diagnostic Classifications in the Clinical Diagnosis of Autistic Spectrum Disorder.

PubMed

Yaylaci, Ferhat; Miral, Suha

2017-01-01

Aim of this study was to compare children diagnosed with Pervasive Developmental Disorder (PDD) according to DSM-IV-TR and DSM-5 diagnostic systems. One hundred fifty children aged between 3 and 15 years diagnosed with PDD by DSM-IV-TR were included. PDD symptoms were reviewed through psychiatric assessment based on DSM-IV-TR and DSM-5 criteria. Clinical severity was determined using Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC). A statistically significant decrease (19.3 %) was detected in the diagnostic ratio with DSM-5. Age and symptom severity differed significantly between those who were and were not diagnosed with PDD using DSM-5. B4 criteria in DSM-5 was most common criterion. Results indicate that individuals diagnosed with PDD by DSM-IV-TR criteria may not be diagnosed using DSM-5 criteria.
A method of classification for multisource data in remote sensing based on interval-valued probabilities

NASA Technical Reports Server (NTRS)

Kim, Hakil; Swain, Philip H.

1990-01-01

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.
Ecoregions and ecodistricts: Ecological regionalizations for the Netherlands' environmental policy

NASA Astrophysics Data System (ADS)

Klijn, Frans; de Waal, Rein W.; Oude Voshaar, Jan H.

1995-11-01

For communicating data on the state of the environment to policy makers, various integrative frameworks are used, including regional integration. For this kind of integration we have developed two related ecological regionalizations, ecoregions and ecodistricts, which are two levels in a series of classifications for hierarchically nested ecosystems at different spatial scale levels. We explain the compilation of the maps from existing geographical data, demonstrating the relatively holistic, a priori integrated approach. The resulting maps are submitted to discriminant analysis to test the consistancy of the use of mapping characteristics, using data on individual abiotic ecosystem components from a national database on a 1-km2 grid. This reveals that the spatial patterns of soil, groundwater, and geomorphology correspond with the ecoregion and ecodistrict maps. Differences between the original maps and maps formed by automatically reclassifying 1-km2 cells with these discriminant components are found to be few. These differences are discussed against the background of the principal dilemma between deductive, a priori integrated, and inductive, a posteriori, classification.
Mapping of Coral Reef Environment in the Arabian Gulf Using Multispectral Remote Sensing

NASA Astrophysics Data System (ADS)

Ben-Romdhane, H.; Marpu, P. R.; Ghedira, H.; Ouarda, T. B. M. J.

2016-06-01

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

Land cover

USGS Publications Warehouse

Jorgenson, Janet C.; Joria, Peter C.; Douglas, David C.; Douglas, David C.; Reynolds, Patricia E.; Rhode, E.B.

2002-01-01

Documenting the distribution of land-cover types on the Arctic National Wildlife Refuge coastal plain is the foundation for impact assessment and mitigation of potential oil exploration and development. Vegetation maps facilitate wildlife studies by allowing biologists to quantify the availability of important wildlife habitats, investigate the relationships between animal locations and the distribution or juxtaposition of habitat types, and assess or extrapolate habitat characteristics across regional areas.To meet the needs of refuge managers and biologists, satellite imagery was chosen as the most cost-effective method for mapping the large, remote landscape of the 1002 Area.Objectives of our study were the following: 1) evaluate a vegetation classification scheme for use in mapping. 2) determine optimal methods for producing a satellite-based vegetation map that adequately met the needs of the wildlife research and management objectives; 3) produce a digital vegetation map for the Arctic Refuge coastal plain using Lands at-Thematic Mapper(TM) satellite imagery, existing geobotanical classifications, ground data, and aerial photographs, and 4) perform an accuracy assessment of the map.
The Shock Index revisited – a fast guide to transfusion requirement? A retrospective analysis on 21,853 patients derived from the TraumaRegister DGU®

PubMed Central

2013-01-01

Introduction Isolated vital signs (for example, heart rate or systolic blood pressure) have been shown unreliable in the assessment of hypovolemic shock. In contrast, the Shock Index (SI), defined by the ratio of heart rate to systolic blood pressure, has been advocated to better risk-stratify patients for increased transfusion requirements and early mortality. Recently, our group has developed a novel and clinical reliable classification of hypovolemic shock based upon four classes of worsening base deficit (BD). The objective of this study was to correlate this classification to corresponding strata of SI for the rapid assessment of trauma patients in the absence of laboratory parameters. Methods Between 2002 and 2011, data for 21,853 adult trauma patients were retrieved from the TraumaRegister DGU® database and divided into four strata of worsening SI at emergency department arrival (group I, SI <0.6; group II, SI ≥0.6 to <1.0; group III, SI ≥1.0 to <1.4; and group IV, SI ≥1.4) and were assessed for demographics, injury characteristics, transfusion requirements, fluid resuscitation and outcomes. The four strata of worsening SI were compared with our recently suggested BD-based classification of hypovolemic shock. Results Worsening of SI was associated with increasing injury severity scores from 19.3 (± 12) in group I to 37.3 (± 16.8) in group IV, while mortality increased from 10.9% to 39.8%. Increments in SI paralleled increasing fluid resuscitation, vasopressor use and decreasing hemoglobin, platelet counts and Quick’s values. The number of blood units transfused increased from 1.0 (± 4.8) in group I to 21.4 (± 26.2) in group IV patients. Of patients, 31% in group III and 57% in group IV required ≥10 blood units until ICU admission. The four strata of SI discriminated transfusion requirements and massive transfusion rates equally with our recently introduced BD-based classification of hypovolemic shock. Conclusion SI upon emergency department arrival may be considered a clinical indicator of hypovolemic shock in respect to transfusion requirements, hemostatic resuscitation and mortality. The four SI groups have been shown to equal our recently suggested BD-based classification. In daily clinical practice, SI may be used to assess the presence of hypovolemic shock if point-of-care testing technology is not available. PMID:23938104
Land use mapping from CBERS-2 images with open source tools by applying different classification algorithms

NASA Astrophysics Data System (ADS)

Sanhouse-García, Antonio J.; Rangel-Peraza, Jesús Gabriel; Bustos-Terrones, Yaneth; García-Ferrer, Alfonso; Mesas-Carrascosa, Francisco J.

2016-02-01

Land cover classification is often based on different characteristics between their classes, but with great homogeneity within each one of them. This cover is obtained through field work or by mean of processing satellite images. Field work involves high costs; therefore, digital image processing techniques have become an important alternative to perform this task. However, in some developing countries and particularly in Casacoima municipality in Venezuela, there is a lack of geographic information systems due to the lack of updated information and high costs in software license acquisition. This research proposes a low cost methodology to develop thematic mapping of local land use and types of coverage in areas with scarce resources. Thematic mapping was developed from CBERS-2 images and spatial information available on the network using open source tools. The supervised classification method per pixel and per region was applied using different classification algorithms and comparing them among themselves. Classification method per pixel was based on Maxver algorithms (maximum likelihood) and Euclidean distance (minimum distance), while per region classification was based on the Bhattacharya algorithm. Satisfactory results were obtained from per region classification, where overall reliability of 83.93% and kappa index of 0.81% were observed. Maxver algorithm showed a reliability value of 73.36% and kappa index 0.69%, while Euclidean distance obtained values of 67.17% and 0.61% for reliability and kappa index, respectively. It was demonstrated that the proposed methodology was very useful in cartographic processing and updating, which in turn serve as a support to develop management plans and land management. Hence, open source tools showed to be an economically viable alternative not only for forestry organizations, but for the general public, allowing them to develop projects in economically depressed and/or environmentally threatened areas.
Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.
Going Deeper With Contextual CNN for Hyperspectral Image Classification.

PubMed

Lee, Hyungtae; Kwon, Heesung

2017-10-01

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.
General classification handbook for floodplain vegetation in large river systems

USGS Publications Warehouse

Dieck, Jennifer J.; Ruhser, Janis; Hoy, Erin E.; Robinson, Larry R.

2015-01-01

This handbook describes the General Wetland Vegetation Classification System developed as part of the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration (UMRR) Program, Long Term Resource Monitoring (LTRM) element. The UMRR is a cooperative effort between the U.S. Army Corps of Engineers, U.S. Geological Survey, U.S. Fish and Wildlife Service, and the states of Illinois, Iowa, Minnesota, Missouri, and Wisconsin. The classification system consists of 31 general map classes and has been used to create systemic vegetation data layers throughout the diverse Upper Mississippi River System (UMRS), which includes the commercially navigable reaches of the Mississippi River from Minneapolis, Minnesota, in the north to Cairo, Illinois, in the south, the Illinois River, and navigable portions of the Kaskaskia, Black, St. Croix, and Minnesota Rivers. In addition, this handbook describes the evolution of the General Wetland Vegetation Classification System, discusses the process of creating a vegetation data layer, and describes each of the 31 map classes in detail. The handbook also acts as a pictorial guide to each of the map classes as they may appear in the field, as well as on color-infrared imagery. This version is an update to the original handbook published in 2004.
Geodiversity and biodiversity assessment of the Słupsk Bank, Baltic Sea

NASA Astrophysics Data System (ADS)

Najwer, Alicja; Zelewska, Izabela; Zwoliński, Zbigniew

2017-04-01

Recognizing the most diversified parts of the territory turns out to be very crucial for management and planning of natural protected areas. There is an increasing number of studies concerning assessing geodiversity and biodiversity of the land areas. However, there is noticeable lack of such publications for submerged zones. The study area consists of 100km2 Słupsk sandy shoal sporadically covered with boulder layers, located in the southern part of the Baltic Sea. It is characterised by landscapes of a significant nature value protected by Natura 2000 and is as well designated as an open sea by Helsinki Commission Baltic Sea Protected Area (HELCOM BSPA). The main aim of the presentation is an attempt to integrate geodiversity and biodiversity assessments of the submerged area using GIS platform. The basis for the diversity assessment is the proper selection of features of the marine environment, its reclassification and integration by the map algebra analysis. The map of geodiversity is based on three factor maps: a relief energy map (classification based on bathymetric model, a landform fragmentation/geomorphological map (expert classification using BPI - Bathymetric Position Index), and a lithological map (classification of the average size of grain fraction). The map of biodiversity is based on the following factor maps: a map of biomass distribution of Ceraminum Diaphanum, a map of biomass distribution of Coccotylus Truncatus, a map of biomass distribution of Polysiphonia Fucoides, a map of biomass distribution of Mytilus Edulis Trossulus, a map of distribution of macroalgae, and finally a map of distribution of macrozoobenthos. It was decided to use four classes of diversity (from low through medium and high, up to very high). The designation of the lowest class was abandoned because it characterizes areas with high anthropopressure. Maps of geodiversity and biodiversity may prove to be helpful in determining the directions for management of the most valuable parts of the areas from the nature point of view, as well as delimitation of the geodiveristy/biodiversity hotspots for purpose of the strict nature protection. This study is the first attempt to use methods of diversity assessment for marine environment.
Color Shaded-Relief and Surface-Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

USGS Publications Warehouse

Mars, John L.; Garrity, Christopher P.; Houseknecht, David W.; Amoroso, Lee; Meares, Donald C.

2007-01-01

Introduction The northeastern part of the National Petroleum Reserve in Alaska (NPRA) has become an area of active petroleum exploration during the past five years. Recent leasing and exploration drilling in the NPRA requires the U.S. Bureau of Land Management (BLM) to manage and monitor a variety of surface activities that include seismic surveying, exploration drilling, oil-field development drilling, construction of oil-production facilities, and construction of pipelines and access roads. BLM evaluates a variety of permit applications, environmental impact studies, and other documents that require rapid compilation and analysis of data pertaining to surface and subsurface geology, hydrology, and biology. In addition, BLM must monitor these activities and assess their impacts on the natural environment. Timely and accurate completion of these land-management tasks requires elevation, hydrologic, geologic, petroleum-activity, and cadastral data, all integrated in digital formats at a higher resolution than is currently available in nondigital (paper) formats. To support these land-management tasks, a series of maps was generated from remotely sensed data in an area of high petroleum-industry activity (fig. 1). The maps cover an area from approximately latitude 70?00' N. to 70?30' N. and from longitude 151?00' W. to 153?10' W. The area includes the Alpine oil field in the east, the Husky Inigok exploration well (site of a landing strip) in the west, many of the exploration wells drilled in NPRA since 2000, and the route of a proposed pipeline to carry oil from discovery wells in NPRA to the Alpine oil field. This map area is referred to as the 'Fish Creek area' after a creek that flows through the region. The map series includes (1) a color shaded-relief map based on 5-m-resolution data (sheet 1), (2) a surface-classification map based on 30-m-resolution data (sheet 2), and (3) a 5-m-resolution shaded relief-surface classification map that combines the shaded-relief and surface-classification data (sheet 3). Remote sensing datasets that were used to compile the maps include Landsat 7 Enhanced Thematic Mapper+ (ETM+), and interferometric synthetic aperture radar (IFSAR) data. In addition, a 1:250,000-scale geologic map of the Harrison Bay quadrangle, Alaska (Carter and Galloway, 1985, 2005) was used in conjunction with ETM+ and IFSAR data.
New DSM-V neurocognitive disorders criteria and their impact on diagnostic classifications of mild cognitive impairment and dementia in a memory clinic setting.

PubMed

Tay, Laura; Lim, Wee Shiong; Chan, Mark; Ali, Noorhazlina; Mahanum, Shariffah; Chew, Pamela; Lim, June; Chong, Mei Sian

2015-08-01

To examine diagnostic agreement between Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) Neurocognitive Disorders (NCDs) criteria and DSM, Fourth Edition (DSM-IV) criteria for dementia and International Working Group (IWG) criteria for mild cognitive impairment (MCI) and DSM-V's impact on diagnostic classifications of NCDs. The authors further examined clinical factors for discrepancy in diagnostic classifications between the different operational definitions. Using a cross-sectional study in tertiary memory clinic, the authors studied consecutive new patients aged 55 years or older who presented with cognitive symptoms. Dementia severity was scored based on the Clinical Dementia Rating scale (CDR). All patients completed neuropsychological evaluation. Agreement in diagnostic classifications between DSM-IV/IWG and DSM-V was examined using the kappa test and AC1 statistic, with multinomial logistic regression for factors contributing to MCI reclassification as major NCDs as opposed to diagnostically concordant MCI and dementia groups. Of 234 patients studied, 166 patients achieved concordant diagnostic classifications, with overall kappa of 0.41. Eighty-six patients (36.7%) were diagnosed with MCI and 131 (56.0%) with DSM-IV-defined dementia. With DSM-V, 40 patients (17.1%) were classified as mild NCDs and 183 (78.2%) as major NCDs, representing a 39.7% increase in frequency of dementia diagnoses. CDR sum-of-boxes score contributed independently to differentiation of MCI patients reclassified as mild versus major NCDs (OR: 0.01; 95% CI: 0-0.09). CDR sum-of-boxes score (OR: 5.18; 95% CI: 2.04-13.15), performance in amnestic (OR: 0.14; 95% CI: 0.06-0.34) and language (Boston naming: OR: 0.52; 95% CI: 0.29-0.94) tests, were independent determinants of diagnostically concordant dementia diagnosis. The authors observed moderate agreement between the different operational definitions and a 40% increase in dementia diagnoses with operationalization of the DSM-V criteria. Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.
Mappability of drug-like space: towards a polypharmacologically competent map of drug-relevant compounds.

PubMed

Sidorov, Pavel; Gaspar, Helena; Marcou, Gilles; Varnek, Alexandre; Horvath, Dragos

2015-12-01

Intuitive, visual rendering--mapping--of high-dimensional chemical spaces (CS), is an important topic in chemoinformatics. Such maps were so far dedicated to specific compound collections--either limited series of known activities, or large, even exhaustive enumerations of molecules, but without associated property data. Typically, they were challenged to answer some classification problem with respect to those same molecules, admired for their aesthetical virtues and then forgotten--because they were set-specific constructs. This work wishes to address the question whether a general, compound set-independent map can be generated, and the claim of "universality" quantitatively justified, with respect to all the structure-activity information available so far--or, more realistically, an exploitable but significant fraction thereof. The "universal" CS map is expected to project molecules from the initial CS into a lower-dimensional space that is neighborhood behavior-compliant with respect to a large panel of ligand properties. Such map should be able to discriminate actives from inactives, or even support quantitative neighborhood-based, parameter-free property prediction (regression) models, for a wide panel of targets and target families. It should be polypharmacologically competent, without requiring any target-specific parameter fitting. This work describes an evolutionary growth procedure of such maps, based on generative topographic mapping, followed by the validation of their polypharmacological competence. Validation was achieved with respect to a maximum of exploitable structure-activity information, covering all of Homo sapiens proteins of the ChEMBL database, antiparasitic and antiviral data, etc. Five evolved maps satisfactorily solved hundreds of activity-based ligand classification challenges for targets, and even in vivo properties independent from training data. They also stood chemogenomics-related challenges, as cumulated responsibility vectors obtained by mapping of target-specific ligand collections were shown to represent validated target descriptors, complying with currently accepted target classification in biology. Therefore, they represent, in our opinion, a robust and well documented answer to the key question "What is a good CS map?"
High-Altitude Electromagnetic Pulse (HEMP) Testing

DTIC Science & Technology

2011-11-10

Security Classification Guide ( SCG ). b. The HEMP simulation facility shall have a measured map of the peak amplitude waveform of the...Quadripartite Standardization Agreement s, sec second SCG security classification guide SN serial number SOP Standard Operating Procedure
Visual attention based bag-of-words model for image classification

NASA Astrophysics Data System (ADS)

Wang, Qiwei; Wan, Shouhong; Yue, Lihua; Wang, Che

2014-04-01

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.
Mapping structural landmarks, ligand binding sites and missense mutations to the collagen IV heterotrimers predicts major functional domains, novel interactions and variation in phenotypes in inherited diseases affecting basement membranes

PubMed Central

Des Parkin, J.; San Antonio, James D.; Pedchenko, Vadim; Hudson, Billy; Jensen, Shane T.; Savige, Judy

2016-01-01

Collagen IV is the major protein found in basement membranes. It comprises 3 heterotrimers (α1α1α2, α3α4α5, and α5α5α6) that form distinct networks, and are responsible for membrane strength and integrity. We constructed linear maps of the collagen IV heterotrimers (‘interactomes’) that indicated major structural landmarks, known and predicted ligand-binding sites, and missense mutations, in order to identify functional and disease-associated domains, potential interactions between ligands, and genotype-phenotype relationships. The maps documented more than 30 known ligand-binding sites as well as motifs for integrins, heparin, von Willebrand factor (VWF), decorin and bone morphogenetic protein (BMP). They predicted functional domains for angiogenesis and haemostasis, and disease domains for autoimmunity, tumor growth and inhibition, infection and glycation. Cooperative ligand interactions were indicated by binding site proximity, for example, between integrins, matrix metalloproteinases and heparin. The maps indicated that mutations affecting major ligand-binding sites, for example for Von Hippel Lindau (VHL) protein in the α1 chain or integrins in the α5 chain, resulted in distinctive phenotypes (Hereditary Angiopathy, Nephropathy, Aneurysms and muscle Cramps (HANAC) syndrome, and early onset Alport syndrome respectively). These maps further our understanding of basement membrane biology and disease, and suggest novel membrane interactions, functions, and therapeutic targets. PMID:21280145
Lung-MAP: AZD4547 as Second-Line Therapy in Treating FGFR Positive Patients With Recurrent Stage IV Squamous Cell Lung Cancer

ClinicalTrials.gov

2017-12-13

FGFR1 Gene Amplification; FGFR1 Gene Mutation; FGFR2 Gene Amplification; FGFR2 Gene Mutation; FGFR3 Gene Amplification; FGFR3 Gene Mutation; Recurrent Squamous Cell Lung Carcinoma; Stage IV Squamous Cell Lung Carcinoma AJCC v7
Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

NASA Astrophysics Data System (ADS)

Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

2015-12-01

Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.
[Characteristic study on village landscape patterns in Sichuan Basin hilly region based on high resolution IKONOS remote sensing].

PubMed

Li, Shoucheng; Liu, Wenquan; Cheng, Xu; Ellis, Erle C

2005-10-01

To realize the landscape programming of agro-ecosystem management, landscape-stratification can provide us the best understanding of landscape ecosystem at very detailed scales. For this purpose, the village landscapes in densely populated Jintang and Jianyang Counties of Sichuan Basin hilly region were mapped from high resolution (1 m) IKONOS satellite imagery by using a standardized 4 level ecological landscape classification and mapping system in a regionally-representative sample of five 500 x 500 m2 landscape quadrats (sample plots). Based on these maps, the spatial patterns were analyzed by landscape indicators, which demonstrated a large variety of landscape types or ecotopes across the village landscape of this region, with diversity indexes ranging from 1.08 to 2.26 at different levels of the landscape classification system. The richness indices ranged from 42.2% to 58.6 %, except that for the landcover at 85 %. About 12.5 % of the ecotopes were distributed in the same way in each landscape sample, and the remaining 87.5% were distributed differently. The landscape fragmentation indices varied from 2.93 to 4.27 across sample plots, and from 2.86 to 5.63 across classification levels. The population density and the road and hamlet areas had strong linear correlations with some landscape indicators, and especially, the correlation coefficients of hamlet areas with fractal indexes and fragmental dimensions were 0.957* and 0.991**, respectively. The differences in most landscape pattern indices across sample plots and landscape classes were statistically significant, indicating that cross-scale mapping and classification of village landscapes could provide more detailed information on landscape patterns than those from a single level of classification.
As-built design specification for segment map (Sgmap) program

NASA Technical Reports Server (NTRS)

Tompkins, M. A. (Principal Investigator)

1981-01-01

The segment map program (SGMAP), which is part of the CLASFYT package, is described in detail. This program is designed to output symbolic maps or numerical dumps from LANDSAT cluster/classification files or aircraft ground truth/processed ground truth files which are in 'universal' format.
Mapping General Anesthetic Sites in Heteromeric γ-Aminobutyric Acid Type A Receptors Reveals a Potential For Targeting Receptor Subtypes.

PubMed

Forman, Stuart A; Miller, Keith W

2016-11-01

IV general anesthetics, including propofol, etomidate, alphaxalone, and barbiturates, produce important actions by enhancing γ-aminobutyric acid type A (GABAA) receptor activation. In this article, we review scientific studies that have located and mapped IV anesthetic sites using photoaffinity labeling and substituted cysteine modification protection. These anesthetics bind in transmembrane pockets between subunits of typical synaptic GABAA receptors, and drugs that display stereoselectivity also show remarkably selective interactions with distinct interfacial sites. These results suggest strategies for developing new drugs that selectively modulate distinct GABAA receptor subtypes.
Meta-Analysis of Human Factors Engineering Studies Comparing Individual Differences, Practice Effects and Equipment Design Variations.

DTIC Science & Technology

1985-02-21

Approvoid foT public 90Ieleol, 2* . tJni7nited " - . - o . - ’--. * . -... . 1 UNCLASSIFIED S, E CURITY CLASSIFICATION OF THIS PAGE-" REPORT DOCUMENTATION...ACCESSION NO. 11. TITLE (Include Security Classification) . Veta -Analysis of Human Factors Engineering Studies Comparing Individual Differences, Practice...Background C Opportunity D Significance E History III. PHASE I FINAL REPORT A Literature Review B Formal Analysis C Results D Implications for Phase II IV
A Chemical Monitoring Program of the Explosion Products in Underwater Explosion Tests

DTIC Science & Technology

1975-04-04

CLASSIFICATION QF THIS PAGE- (When Date Entered) UNCLASSIFIED tL,URJTY CLASSIFICATION OF THIS PAGE(Then Data Entered) 20.and determination of various explosion...to institute a chemical monitoring program of the explosion products in underwater explosion tests, to determine monitoring parameters, and to...27 3.2.3 Samplers 28 3.2.4 Storage of Sediment Samples 32 IV. DETERMINATION OF EXPLOSION PRODUCTS 32 4.1 DESIGN OF MEASUREMENT SYSTEM 32 4.1.1

Interrelationship between Autism Diagnostic Observation Schedule-Generic (ADOS-G), Autism Diagnostic Interview-Revised (ADI-R), and the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) Classification in Children and Adolescents with Mental Retardation

ERIC Educational Resources Information Center

de Bildt, Annelies; Sytema, Sjoerd; Ketelaars, Cees; Kraijer, Dirk; Mulder, Erik; Volkmar, Fred; Minderaa, Ruud

2004-01-01

The interrelationship between the Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule-Generic (ADOS-G) and clinical classification was studied in 184 children and adolescents with Mental Retardation (MR). The agreement between the ADI-R and ADOS-G was fair, with a substantial difference between younger and older…
Trainer Engineering Report (Final) for MILES. Volume 2. Revision

DTIC Science & Technology

1981-04-22

formerly a separate document, Data Item AOOX. iii/iv 1A , SECURITY CLASSIFICATION OF THIS PAGE (Uhen Deaa Enterecd) ... __ . ....... REPORT DOCUMENTATION...NAVTRAEQUIPCEN, Orlando, FL 32813 3 14. MON’TORING AGENCY NAME & ADDRESS(II dilletent from CoftrollIn OGlue*) IS. SECURITY CLASS. (of thie twoot...OBSOLETE UNCLASSIFIED S/N 0102蓞-6601 I SECURITY CL.ASSIFICATION OF THIS iPAGE fUlses Data EaieteE i • CONTENTS .I * INTRODUCTION 1-1 1.1 1980 MILES 1-1
Large Scale Crop Classification in Ukraine using Multi-temporal Landsat-8 Images with Missing Data

NASA Astrophysics Data System (ADS)

Kussul, N.; Skakun, S.; Shelestov, A.; Lavreniuk, M. S.

2014-12-01

At present, there are no globally available Earth observation (EO) derived products on crop maps. This issue is being addressed within the Sentinel-2 for Agriculture initiative where a number of test sites (including from JECAM) participate to provide coherent protocols and best practices for various global agriculture systems, and subsequently crop maps from Sentinel-2. One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this abstract, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using non-missing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. For this, an ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for large scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine [1-2]. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy and kappa coefficient. The obtained classification map is also validated through estimated crop and forest areas and comparison to official statistics. 1. A.Yu. Shelestov et al., "Geospatial information system for agricultural monitoring," Cybernetics Syst. Anal., vol. 49, no. 1, pp. 124-132, 2013. 2. J. Gallego et al., "Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations," J. Autom. Inform. Scie., vol. 44, no. 5, pp. 67-80, 2012.
20 CFR 627.440 - Classification of costs.

Code of Federal Regulations, 2011 CFR

2011-04-01

... to: (A) Salaries, fringe benefits, equipment, supplies, space, staff training, transportation, and... participants; (iii) Equipment and materials used in providing training to participants; (iv) Classroom space... work experience, vocational exploration, limited internships, and entry employment. (2) Direct training...
20 CFR 627.440 - Classification of costs.

Code of Federal Regulations, 2012 CFR

2012-04-01

... to: (A) Salaries, fringe benefits, equipment, supplies, space, staff training, transportation, and... participants; (iii) Equipment and materials used in providing training to participants; (iv) Classroom space... work experience, vocational exploration, limited internships, and entry employment. (2) Direct training...
20 CFR 627.440 - Classification of costs.

Code of Federal Regulations, 2010 CFR

2010-04-01

... to: (A) Salaries, fringe benefits, equipment, supplies, space, staff training, transportation, and... participants; (iii) Equipment and materials used in providing training to participants; (iv) Classroom space... work experience, vocational exploration, limited internships, and entry employment. (2) Direct training...
Risk-Informed Mean Recurrence Intervals for Updated Wind Maps in ASCE 7-16.

PubMed

McAllister, Therese P; Wang, Naiyu; Ellingwood, Bruce R

2018-05-01

ASCE 7 is moving toward adopting load requirements that are consistent with risk-informed design goals characteristic of performance-based engineering (PBE). ASCE 7-10 provided wind maps that correspond to return periods of 300, 700, and 1,700 years for Risk Categories I, II, and combined III/IV, respectively. The risk targets for Risk Categories III and IV buildings and other structures (designated as essential facilities) are different in PBE. The reliability analyses reported in this paper were conducted using updated wind load data to (1) confirm that the return periods already in ASCE 7-10 were also appropriate for risk-informed PBE, and (2) to determine a new risk-based return period for Risk Category IV. The use of data for wind directionality factor, K d , which has become available from recent wind tunnel tests, revealed that reliabilities associated with wind load combinations for Risk Category II structures are, in fact, consistent with the reliabilities associated with the ASCE 7 gravity load combinations. This paper shows that the new wind maps in ASCE 7-16, which are based on return periods of 300, 700, 1,700, and 3,000 years for Risk Categories I, II, III, and IV, respectively), achieve the reliability targets in Section 1.3.1.3 of ASCE 7-16 for nonhurricane wind loads.
Who is MADD? Mixed anxiety depressive disorder in the general population.

PubMed

Spijker, Jan; Batelaan, Neeltje; de Graaf, Ron; Cuijpers, Pim

2010-02-01

Diagnostic criteria for (subthreshold) mixed anxiety depression (MADD) were proposed in DSM-IV. Yet the usefulness of this classification is questioned. We therefore assessed the prevalence of MADD, and investigated whether MADD adds to separate classifications of pure subthreshold depression and anxiety. Data of the Netherlands Mental Health and Incidence Study were used. The 12-month prevalence of MADD was 0.6%. Between the three subthreshold categories few differences were found with regard to socio-demographic variables, care utilisation and functioning. Course in MADD seems more favourable and MADD is not a stable diagnosis over time. The MADD criteria used in the present study differed slightly from the proposed criteria in DSM-IV and sample sizes were small. Given these results, MADD is not a relevant diagnosis in terms of prevalence and consequences when classified according to the currently proposed criteria. 2009 Elsevier B.V. All rights reserved.
Defining process design space for monoclonal antibody cell culture.

PubMed

Abu-Absi, Susan Fugett; Yang, LiYing; Thompson, Patrick; Jiang, Canping; Kandula, Sunitha; Schilling, Bernhard; Shukla, Abhinav A

2010-08-15

The concept of design space has been taking root as a foundation of in-process control strategies for biopharmaceutical manufacturing processes. During mapping of the process design space, the multidimensional combination of operational variables is studied to quantify the impact on process performance in terms of productivity and product quality. An efficient methodology to map the design space for a monoclonal antibody cell culture process is described. A failure modes and effects analysis (FMEA) was used as the basis for the process characterization exercise. This was followed by an integrated study of the inoculum stage of the process which includes progressive shake flask and seed bioreactor steps. The operating conditions for the seed bioreactor were studied in an integrated fashion with the production bioreactor using a two stage design of experiments (DOE) methodology to enable optimization of operating conditions. A two level Resolution IV design was followed by a central composite design (CCD). These experiments enabled identification of the edge of failure and classification of the operational parameters as non-key, key or critical. In addition, the models generated from the data provide further insight into balancing productivity of the cell culture process with product quality considerations. Finally, process and product-related impurity clearance was evaluated by studies linking the upstream process with downstream purification. Production bioreactor parameters that directly influence antibody charge variants and glycosylation in CHO systems were identified.
Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

NASA Technical Reports Server (NTRS)

Huckle, H. F. (Principal Investigator)

1980-01-01

The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.
Low Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

NASA Technical Reports Server (NTRS)

Spruce, Joe

2001-01-01

Yellowstone National Park (YNP) contains a diversity of land cover. YNP managers need site-specific land cover maps, which may be produced more effectively using high-resolution hyperspectral imagery. ISODATA clustering techniques have aided operational multispectral image classification and may benefit certain hyperspectral data applications if optimally applied. In response, a study was performed for an area in northeast YNP using 11 select bands of low-altitude AVIRIS data calibrated to ground reflectance. These data were subjected to ISODATA clustering and Maximum Likelihood Classification techniques to produce a moderately detailed land cover map. The latter has good apparent overall agreement with field surveys and aerial photo interpretation.
The mapping of marsh vegetation using aircraft multispectral scanner data. [in Louisiana

NASA Technical Reports Server (NTRS)

Butera, M. K.

1975-01-01

A test was conducted to determine if salinity regimes in coastal marshland could be mapped and monitored by the identification and classification of marsh vegetative species from aircraft multispectral scanner data. The data was acquired at 6.1 km (20,000 ft.) on October 2, 1974, over a test area in the coastal marshland of southern Louisiana including fresh, intermediate, brackish, and saline zones. The data was classified by vegetational species using a supervised, spectral pattern recognition procedure. Accuracies of training sites ranged from 67% to 96%. Marsh zones based on free soil water salinity were determined from the species classification to demonstrate a practical use for mapping marsh vegetation.
Use of a remote computer terminal during field checking of Landsat digital maps

USGS Publications Warehouse

Robinove, Charles J.; Hutchinson, C.F.

1978-01-01

Field checking of small-scale land classification maps made digitally from Landsat data is facilitated by use of a remote portable teletypewriter terminal linked by teleplume to the IDIMS (Interactive Digital Image Manipulation System) at the EDC (EROS Data Center), Sioux Falls, S. Dak. When field checking of maps 20 miles northeast of Baker, Calif., during the day showed that changes in classification were needed, the terminal was used at night to combine image statistical files, remap portions of images, and produce new alphanumeric maps for field checking during the next day. The alphanumeric maps can be used without serious difficulty in location in the field even though the scale is distorted, and statistical files created during the field check can be used for full image classification and map output at the EDC. This process makes field checking faster than normal, provides interaction with the statistical data while in the field, and reduces to a minimum the number of trips needed to work interactively with the IDIMS at the EDC, thus saving significant amounts of time and money. The only significant problem is using telephone lines which at times create spurious characters in the printout or prevent the line feed (paper advance) signal from reaching the terminal, thus overprinting lines which should be sequential. We recommend that maps for field checking be made with more spectral classes than are expected because in the field it is much easier to group classes than to reclassify or separate classes when only the remote terminal is available for display.
A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.
Automated artery-venous classification of retinal blood vessels based on structural mapping method

NASA Astrophysics Data System (ADS)

Joshi, Vinayak S.; Garvin, Mona K.; Reinhardt, Joseph M.; Abramoff, Michael D.

2012-03-01

Retinal blood vessels show morphologic modifications in response to various retinopathies. However, the specific responses exhibited by arteries and veins may provide a precise diagnostic information, i.e., a diabetic retinopathy may be detected more accurately with the venous dilatation instead of average vessel dilatation. In order to analyze the vessel type specific morphologic modifications, the classification of a vessel network into arteries and veins is required. We previously described a method for identification and separation of retinal vessel trees; i.e. structural mapping. Therefore, we propose the artery-venous classification based on structural mapping and identification of color properties prominent to the vessel types. The mean and standard deviation of each of green channel intensity and hue channel intensity are analyzed in a region of interest around each centerline pixel of a vessel. Using the vector of color properties extracted from each centerline pixel, it is classified into one of the two clusters (artery and vein), obtained by the fuzzy-C-means clustering. According to the proportion of clustered centerline pixels in a particular vessel, and utilizing the artery-venous crossing property of retinal vessels, each vessel is assigned a label of an artery or a vein. The classification results are compared with the manually annotated ground truth (gold standard). We applied the proposed method to a dataset of 15 retinal color fundus images resulting in an accuracy of 88.28% correctly classified vessel pixels. The automated classification results match well with the gold standard suggesting its potential in artery-venous classification and the respective morphology analysis.
Multistrategy Self-Organizing Map Learning for Classification Problems

PubMed Central

Hasan, S.; Shamsuddin, S. M.

2011-01-01

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test. PMID:21876686
Mapping Successional Stages in a Wet Tropical Forest Using Landsat ETM+ and Forest Inventory Data

NASA Technical Reports Server (NTRS)

Goncalves, Fabio G.; Yatskov, Mikhail; dos Santos, Joao Roberto; Treuhaft, Robert N.; Law, Beverly E.

2010-01-01

In this study, we test whether an existing classification technique based on the integration of Landsat ETM+ and forest inventory data enables detailed characterization of successional stages in a wet tropical forest site. The specific objectives were: (1) to map forest age classes across the La Selva Biological Station in Costa Rica; and (2) to quantify uncertainties in the proposed approach in relation to field data and existing vegetation maps. Although significant relationships between vegetation height entropy (a surrogate for forest age) and ETM+ data were detected, the classification scheme tested in this study was not suitable for characterizing spatial variation in age at La Selva, as evidenced by the error matrix and the low Kappa coefficient (12.9%). Factors affecting the performance of the classification at this particular study site include the smooth transition in vegetation structure between intermediate and advanced successional stages, and the low sensitivity of NDVI to variations in vertical structure at high biomass levels.
Classification for congenital anomalies of the hand: the IFSSH classification and the JSSH modification.

PubMed

De Smet, L

2002-01-01

The purpose of a classification for clinical problems which, except for a few specialized centers, occur only sporadically is to provide a system where these cases can be stored. This should allow all involved investigators to speak the same language; so-doing syndromes can be delinated, frequencies of occurence established and results of--different--treatments compared. A classification system should be simple to use, reliable and uniformly accepted. It should allow space for adaptations and/or extensions. The IFSSH proposed a 7 categories classification based on the proposed classification of Swanson et al. in 1976. This classification, was based on, which was thought in the seventies, etiopathogenic pathways. These 7 groups are: I. Failure of formation; transverse (A), or longitudinal (B) II. Failure of differentiation III. Polydactyly IV. Overgrowth V. Undergrowth VI. Amniotic band syndrome VII. Generalized skeletal syndromes. The extended classification proposed by IFSSH was used to classify 1013 hand differences in 925 hands of 650 patients. We found associated anomalies in 26.7%. The classification was straightforward in 86%, difficult in 6.6% and not possible in 7.8%. Group II was the most numerous group including 513 anomalies. We propose to include in this group the Madelung deformity, the Kirner deformity and congenital trigger fingers and trigger thumbs. In group I the radial and ulnar deficiencies, limited to the hand without forearm deficlencies should be Included. Triphalangeal thumbs are a problem, we suggest it to be listed in group III and consider it as a duplication in length. It is not always possible to evaluate the (transverse) absence of the fingers or hand. Longitudinal deficiencies (group IIB), symbrachydactyly (group V), and amniotic bands (group IV) occasionally develop a phenotype similar to the genuine transverse deficiency (group IA). Recently, the Japanese Society for Surgery of the Hand (JSSH) (16) proposed an extension/modification of the IFSSH classification. Based on newer knowledge on teratology, symbrachydactyly in all stages were transfered to group I. Two new groups were introduced. A group "failure of finger ray induction" including typical cleft hand (IC), central polydactyly (III) and (bony) syndactyly (II)--was included. Also a group of "unclassifiable" cases was added. This Japanese proposed classification is a real improvement and most clinicians and surgeons tend to use it in the future.
Landsat-faciliated vegetation classification of the Kenai National Wildlife Refuge and adjacent areas, Alaska

USGS Publications Warehouse

Talbot, S. S.; Shasby, M.B.; Bailey, T.N.

1985-01-01

A Landsat-based vegetation map was prepared for Kenai National Wildlife Refuge and adjacent lands, 2 million and 2.5 million acres respectively. The refuge lies within the middle boreal sub zone of south central Alaska. Seven major classes and sixteen subclasses were recognized: forest (closed needleleaf, needleleaf woodland, mixed); deciduous scrub (lowland and montane, subalpine); dwarf scrub (dwarf shrub tundra, lichen tundra, dwarf shrub and lichen tundra, dwarf shrub peatland, string bog/wetlands); herbaceous (graminoid meadows and marshes); scarcely vegetated areas ; water (clear, moderately turbid, highly turbid); and glaciers. The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photo interpretation, and digital Landsat data. Major steps in the Landsat analysis involved: preprocessing (geometric connection), spectral class labeling of sample areas, derivation of statistical parameters for spectral classes, preliminary classification of the entree study area using a maximum-likelihood algorithm, and final classification through ancillary information such as digital elevation data. The vegetation map (scale 1:250,000) was a pioneering effort since there were no intermediate-sclae maps of the area. Representative of distinctive regional patterns, the map was suitable for use in comprehensive conservation planning and wildlife management.
Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

USGS Publications Warehouse

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

NASA Astrophysics Data System (ADS)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.
Italian consensus conference on guidelines for conservative treatment on lower limb muscle injuries in athlete

PubMed Central

Bisciotti, Gian Nicola; Volpi, Piero; Amato, Maurizio; Alberti, Giampietro; Allegra, Francesco; Aprato, Alessandro; Artina, Matteo; Auci, Alessio; Bait, Corrado; Bastieri, Gian Matteo; Balzarini, Luca; Belli, Andrea; Bellini, Gianandrea; Bettinsoli, Pierfrancesco; Bisciotti, Alessandro; Bisciotti, Andrea; Bona, Stefano; Brambilla, Lorenzo; Bresciani, Marco; Buffoli, Michele; Calanna, Filippo; Canata, Gian Luigi; Cardinali, Davide; Carimati, Giulia; Cassaghi, Gabriella; Cautero, Enrico; Cena, Emanuele; Corradini, Barbara; D'Agostino, Cristina; De Donato, Massimo; Delle Rose, Giacomo; Di Marzo, Francesco; Di Pietto, Francesco; Enrica, Drapchind; Eirale, Cristiano; Febbrari, Luigi; Ferrua, Paolo; Foglia, Andrea; Galbiati, Alberto; Gheza, Alberto; Giammattei, Carlo; Masia, Francesco; Melegati, Gianluca; Moretti, Biagio; Moretti, Lorenzo; Niccolai, Roberto; Orgiani, Antonio; Orizio, Claudio; Pantalone, Andrea; Parra, Federica; Patroni, Paolo; Pereira Ruiz, Maria Teresa; Perri, Marzio; Petrillo, Stefano; Pulici, Luca; Quaglia, Alessandro; Ricciotti, Luca; Rosa, Francesco; Sasso, Nicola; Sprenger, Claudio; Tarantola, Chiara; Tenconi, Fabio Gianpaolo; Tosi, Fabio; Trainini, Michele; Tucciarone, Agostino; Yekdah, Ali; Vuckovic, Zarko; Zini, Raul; Chamari, Karim

2018-01-01

Provide the state of the art concerning (1) biology and aetiology, (2) classification, (3) clinical assessment and (4) conservative treatment of lower limb muscle injuries (MI) in athletes. Seventy international experts with different medical backgrounds participated in the consensus conference. They discussed and approved a consensus composed of four sections which are presented in these documents. This paper represents a synthesis of the consensus conference, the following four sections are discussed: (i) The biology and aetiology of MIs. A definition of MI was formulated and some key points concerning physiology and pathogenesis of MIs were discussed. (ii) The MI classification. A classification of MIs was proposed. (iii) The MI clinical assessment, in which were discussed anamnesis, inspection and clinical examination and are provided the relative guidelines. (iv) The MI conservative treatment, in which are provided the guidelines for conservative treatment based on the severity of the lesion. Furthermore, instrumental therapy and pharmacological treatment were discussed. Knowledge of the aetiology and biology of MIs is an essential prerequisite in order to plan and conduct a rehabilitation plan. Another important aspect is the use of a rational MI classification on prognostic values. We propose a classification based on radiological investigations performed by ultrasonography and MRI strongly linked to prognostic factors. Furthermore, the consensus conference results will able to provide fundamental guidelines for diagnostic and rehabilitation practice, also considering instrumental therapy and pharmacological treatment of MI. Expert opinion, level IV. PMID:29862040
Comparison of diagnostic classification systems for delirium with new research criteria that incorporate the three core domains.

PubMed

Trzepacz, Paula T; Meagher, David J; Franco, José G

2016-05-01

Diagnostic classification systems do not incorporate phenomenological research findings about the three core symptom domains of delirium (Attentional/Cognitive, Circadian, Higher Level Thinking). We evaluated classification performances of novel Trzepacz, Meagher, and Franco research diagnostic criteria (TMF) that incorporate those domains and ICD-10, DSM-III-R, DSM-IV, and DSM-5. Primary data analysis of 641 patients with mixed neuropsychiatric profiles. Delirium (n=429) and nondelirium (n=212) reference standard groups were identified using cluster analysis of symptoms assessed using the Delirium Rating Scale-Revised-98. Accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), and likelihood ratios (LR+, LR-) are reported. TMF criteria had high sensitivity and specificity (87.4% and 89.2%), more balanced than DSM-III-R (100% and 31.6%), DSM-IV (97.7% and 74.1%), DSM-5 (97.7% and 72.6%), and ICD-10 (66.2% and 100%). PPV of DSM-III-R, DSM-IV, and DSM-5 were <90.0%, while PPV for ICD-10 and TMF were >90%. ICD-10 had the lowest NPV (59.4%). TMF had the highest LR+ (8.06) and DSM-III-R the lowest LR- (0.0). Overall, values for DSM-IV and DSM-5 were similar, whereas for ICD-10 and DSM-III-R were inverse of each other. In the pre-existing cognitive impairment/dementia subsample (n=128), TMF retained its highest LR+ though specificity (58.3%) became less well balanced with sensitivity (87.9%), which still exceeded that of DSM. TMF research diagnostic criteria performed well, with more balanced sensitivity and specificity and the highest likelihood ratio for delirium identification. Reflecting the three core domains of delirium, TMF criteria may have advantages in biological research where delineation of this syndrome is important. Copyright © 2016. Published by Elsevier Inc.
A Prototype Model for Automating Nursing Diagnosis, Nurse Care Planning and Patient Classification.

DTIC Science & Technology

1986-03-01

Each diagnosis has an assessment level. Assessment levels are defining characteristics observed by the nurse or subjectively stated by the patient... characteristics of this order line. Select IV Order (Figure 4.l.1.le] is the first screen of a series of three. Select IV Order has up to 10 selections...For I F Upatient orders. Input Files Used: IVC.Scr and Procfile.Prg * Output Files Used: None Calling Routine: IUB.Prg * Routine Called: None
AN EXPERIMENTAL ASSESSMENT OF MINIMUM MAPPING UNIT SIZE

EPA Science Inventory

Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU). The choice of a MMU that is appropriate for the projected use of a classification is important. The objective of this experiment was to determine the optimal MMU of a L...
Ocean Thermal Feature Recognition, Discrimination and Tracking Using Infrared Satellite Imagery

DTIC Science & Technology

1991-06-01

rejected if the temperature in the mapped area exceeds classification criteria ............................... 17 viii 2.6 Ideal feature space mapping from...in seconds, and 1P is the side dimension of the pixel in meters. Figure 2.6: Ideal feature space mapping from pattern tile - search tile comparison. 20
Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

NASA Astrophysics Data System (ADS)

Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

2016-06-01

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.
The Holdridge life zones of the conterminous United States in relation to ecosystem mapping

Treesearch

A.E. Lugo; S. L. Brown; R. Dodson; T. S Smith; H. H. Shugart

1999-01-01

Aim Our main goals were to develop a map of the life zones for the conterminous United States, based on the Holdridge Life Zone system, as a tool for ecosystem mapping, and to compare the map of Holdridge life zones with other global vegetation classification and mapping efforts. Location The area of interest is the forty-eight contiguous states of the United States....
Bush Encroachment Mapping for Africa - Multi-Scale Analysis with Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Graw, V. A. M.; Oldenburg, C.; Dubovyk, O.

2015-12-01

Bush encroachment describes a global problem which is especially facing the savanna ecosystem in Africa. Livestock is directly affected by decreasing grasslands and inedible invasive species which defines the process of bush encroachment. For many small scale farmers in developing countries livestock represents a type of insurance in times of crop failure or drought. Among that bush encroachment is also a problem for crop production. Studies on the mapping of bush encroachment so far focus on small scales using high-resolution data and rarely provide information beyond the national level. Therefore a process chain was developed using a multi-scale approach to detect bush encroachment for whole Africa. The bush encroachment map is calibrated with ground truth data provided by experts in Southern, Eastern and Western Africa. By up-scaling location specific information on different levels of remote sensing imagery - 30m with Landsat images and 250m with MODIS data - a map is created showing potential and actual areas of bush encroachment on the African continent and thereby provides an innovative approach to map bush encroachment on the regional scale. A classification approach links location data based on GPS information from experts to the respective pixel in the remote sensing imagery. Supervised classification is used while actual bush encroachment information represents the training samples for the up-scaling. The classification technique is based on Random Forests and regression trees, a machine learning classification approach. Working on multiple scales and with the help of field data an innovative approach can be presented showing areas affected by bush encroachment on the African continent. This information can help to prevent further grassland decrease and identify those regions where land management strategies are of high importance to sustain livestock keeping and thereby also secure livelihoods in rural areas.
Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

PubMed Central

Yoon, Soon Ho; Jung, Julip; Hong, Helen; Park, Eun Ah; Lee, Chang Hyun; Lee, Youkyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun

2014-01-01

Objective To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Results Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. Conclusion Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation. PMID:24843245
Computer-aided classification of visual ventilation patterns in patients with chronic obstructive pulmonary disease at two-phase xenon-enhanced CT.

PubMed

Yoon, Soon Ho; Goo, Jin Mo; Jung, Julip; Hong, Helen; Park, Eun Ah; Lee, Chang Hyun; Lee, Youkyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun

2014-01-01

To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.
Coastal habitat mapping in the Aegean Sea using high resolution orthophoto maps

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Papakonstantinou, Apostolos; Doukari, Michaela; Stamatis, Panagiotis; Makri, Despina; Katsanevakis, Stelios

2017-09-01

The significance of coastal habitat mapping lies in the need to prevent from anthropogenic interventions and other factors. Until 2015, Landsat-8 (30m) imagery were used as medium spatial resolution satellite imagery. So far, Sentinel-2 satellite imagery is very useful for more detailed regional scale mapping. However, the use of high resolution orthophoto maps, which are determined from UAV data, is expected to improve the mapping accuracy. This is due to small spatial resolution of the orthophoto maps (30 cm). This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. Additionally, the produced orthophoto maps analyzed through an object-based image analysis (OBIA) and nearest-neighbor classification for mapping the coastal habitats. Classification classes included the main general habitat types, i.e. seagrass, soft bottom, and hard bottom The developed methodology applied at the Koumbara beach (Ios Island - Greece). Results showed that UAS's data revealed the sub-bottom complexity in large shallow areas since they provide such information in the spatial resolution that permits the mapping of seagrass meadows with extreme detail. The produced habitat vectors are ideal as reference data for studies with satellite data of lower spatial resolution.
Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

2018-01-01

Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.
Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.
Non-parametric analysis of LANDSAT maps using neural nets and parallel computers

NASA Technical Reports Server (NTRS)

Salu, Yehuda; Tilton, James

1991-01-01

Nearest neighbor approaches and a new neural network, the Binary Diamond, are used for the classification of images of ground pixels obtained by LANDSAT satellite. The performances are evaluated by comparing classifications of a scene in the vicinity of Washington DC. The problem of optimal selection of categories is addressed as a step in the classification process.
A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa

Treesearch

Elizabeth A. Freeman; Gretchen G. Moisen

2008-01-01

Modelling techniques used in binary classification problems often result in a predicted probability surface, which is then translated into a presence - absence classification map. However, this translation requires a (possibly subjective) choice of threshold above which the variable of interest is predicted to be present. The selection of this threshold value can have...
An accuracy assessment of forest disturbance mapping in the western Great Lakes

Treesearch

P.L. Zimmerman; I.W. Housman; C.H. Perry; R.A. Chastain; J.B. Webb; M.V. Finco

2013-01-01

The increasing availability of satellite imagery has spurred the production of thematic land cover maps based on satellite data. These maps are more valuable to the scientific community and land managers when the accuracy of their classifications has been assessed. Here, we assessed the accuracy of a map of forest disturbance in the watersheds of Lake Superior and Lake...
Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.
Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.
Comprehensive classification test of scapular dyskinesis: A reliability study.

PubMed

Huang, Tsun-Shun; Huang, Han-Yi; Wang, Tyng-Guey; Tsai, Yung-Shen; Lin, Jiu-Jenq

2015-06-01

Assessment of scapular dyskinesis (SD) is of clinical interest, as SD is believed to be related to shoulder pathology. However, no clinical assessment with sufficient reliability to identify SD and provide treatment strategies is available. The purpose of this study was to investigate the reliability of the comprehensive SD classification method. Cross-sectional reliability study. Sixty subjects with unilateral shoulder pain were evaluated by two independent physiotherapists with a visual-based palpation method. SD was classified as single abnormal scapular pattern [inferior angle (pattern I), medial border (pattern II), superior border of scapula prominence or abnormal scapulohumeral rhythm (pattern III)], a mixture of the above abnormal scapular patterns, or normal pattern (pattern IV). The assessment of SD was evaluated as subjects performed bilateral arm raising/lowering movements with a weighted load in the scapular plane. Percentage of agreement and kappa coefficients were calculated to determine reliability. Agreement between the 2 independent physiotherapists was 83% (50/60, 6 subjects as pattern III and 44 subjects as pattern IV) in the raising phase and 68% (41/60, 5 subjects as pattern I, 12 subjects as pattern II, 12 subjects as pattern IV, 12 subjects as mixed patterns I and II) in the lowering phase. The kappa coefficients were 0.49-0.64. We concluded that the visual-based palpation classification method for SD had moderate to substantial inter-rater reliability. The appearance of different types of SD was more pronounced in the lowering phase than in the raising phase of arm movements. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prognostic Relevance of Histomolecular Classification of Diffuse Adult High-Grade Gliomas with Necrosis.

PubMed

Figarella-Branger, Dominique; Mokhtari, Karima; Colin, Carole; Uro-Coste, Emmanuelle; Jouvet, Anne; Dehais, Caroline; Carpentier, Catherine; Villa, Chiara; Maurage, Claude-Alain; Eimer, Sandrine; Polivka, Marc; Vignaud, Jean-Michel; Laquerriere, Annie; Sevestre, Henri; Lechapt-Zalcman, Emmanuelle; Quintin-Roué, Isabelle; Aubriot-Lorton, Marie-Hélène; Diebold, Marie-Danièle; Viennet, Gabriel; Adam, Clovis; Loussouarn, Delphine; Michalak, Sophie; Rigau, Valérie; Heitzmann, Anne; Vandenbos, Fanny; Forest, Fabien; Chiforeanu, Danchristian; Tortel, Marie-Claire; Labrousse, François; Chenard, Marie-Pierre; Nguyen, Anh Tuan; Varlet, Pascale; Kemeny, Jean Louis; Levillain, Pierre-Marie; Cazals-Hatem, Dominique; Richard, Pomone; Delattre, Jean-Yves

2015-07-01

Diffuse adult high-grade gliomas (HGGs) with necrosis encompass anaplastic oligodendrogliomas (AOs) with necrosis (grade III), glioblastomas (GBM, grade IV) and glioblastomas with an oligodendroglial component (GBMO, grade IV). Here, we aimed to search for prognostic relevance of histological classification and molecular alterations of these tumors. About 210 patients were included (63 AO, 56 GBM and 91 GBMO). GBMO group was split into "anaplastic oligoastrocytoma (AOA) with necrosis grade IV/GBMO," restricted to tumors showing intermingled astrocytic and oligodendroglial component, and "GBM/GBMO" based on tumors presenting oligodendroglial foci and features of GBM. Genomic arrays, IDH1 R132H expression analyses and IDH direct sequencing were performed. 1p/19q co-deletion characterized AO, whereas no IDH1 R132H expression and intact 1p/19q characterized both GBM and GBM/GBMO. AOA with necrosis/GBMO mainly demonstrated IDH1 R132H expression and intact 1p/19q. Other IDH1 or IDH2 mutations were extremely rare. Both histological and molecular classifications were predictive of progression free survival (PFS) and overall survival (OS) (P < 10(-4) ). Diffuse adult HGGs with necrosis can be split into three histomolecular groups of prognostic relevance: 1p/19q co-deleted AO, IDH1 R132H-GBM and 1p/19q intact IDH1 R132H+ gliomas that might be classified as IDH1 R132H+ GBM. Because of histomolecular heterogeneity, we suggest to remove the name GBMO. © 2014 International Society of Neuropathology.
Combat Stories Map: A Historical Repository and After Action Tool for Capturing, Storing, and Analyzing Georeferenced Individual Combat Narratives

DTIC Science & Technology

2016-06-01

of technology and near-global Internet accessibility, a web -based program incorporating interactive maps to record personal combat experiences does...not exist. The Combat Stories Map addresses this deficiency. The Combat Stories Map is a web -based Geographic Information System specifically designed...iv THIS PAGE INTENTIONALLY LEFT BLANK v ABSTRACT Despite the proliferation of technology and near-global Internet accessibility, a web
Mappability of drug-like space: towards a polypharmacologically competent map of drug-relevant compounds

NASA Astrophysics Data System (ADS)

Sidorov, Pavel; Gaspar, Helena; Marcou, Gilles; Varnek, Alexandre; Horvath, Dragos

2015-12-01

Intuitive, visual rendering—mapping—of high-dimensional chemical spaces (CS), is an important topic in chemoinformatics. Such maps were so far dedicated to specific compound collections—either limited series of known activities, or large, even exhaustive enumerations of molecules, but without associated property data. Typically, they were challenged to answer some classification problem with respect to those same molecules, admired for their aesthetical virtues and then forgotten—because they were set-specific constructs. This work wishes to address the question whether a general, compound set-independent map can be generated, and the claim of "universality" quantitatively justified, with respect to all the structure-activity information available so far—or, more realistically, an exploitable but significant fraction thereof. The "universal" CS map is expected to project molecules from the initial CS into a lower-dimensional space that is neighborhood behavior-compliant with respect to a large panel of ligand properties. Such map should be able to discriminate actives from inactives, or even support quantitative neighborhood-based, parameter-free property prediction (regression) models, for a wide panel of targets and target families. It should be polypharmacologically competent, without requiring any target-specific parameter fitting. This work describes an evolutionary growth procedure of such maps, based on generative topographic mapping, followed by the validation of their polypharmacological competence. Validation was achieved with respect to a maximum of exploitable structure-activity information, covering all of Homo sapiens proteins of the ChEMBL database, antiparasitic and antiviral data, etc. Five evolved maps satisfactorily solved hundreds of activity-based ligand classification challenges for targets, and even in vivo properties independent from training data. They also stood chemogenomics-related challenges, as cumulated responsibility vectors obtained by mapping of target-specific ligand collections were shown to represent validated target descriptors, complying with currently accepted target classification in biology. Therefore, they represent, in our opinion, a robust and well documented answer to the key question "What is a good CS map?"
Constructing linkage maps in the genomics era with MapDisto 2.0.

PubMed

Heffelfinger, Christopher; Fragoso, Christopher A; Lorieux, Mathias

2017-07-15

Genotyping by sequencing (GBS) generates datasets that are challenging to handle by current genetic mapping software with graphical interface. Geneticists need new user-friendly computer programs that can analyze GBS data on desktop computers. This requires improvements in computation efficiency, both in terms of speed and use of random-access memory (RAM). MapDisto v.2.0 is a user-friendly computer program for construction of genetic linkage maps. It includes several new major features: (i) handling of very large genotyping datasets like the ones generated by GBS; (ii) direct importation and conversion of Variant Call Format (VCF) files; (iii) detection of linkage, i.e. construction of linkage groups in case of segregation distortion; (iv) data imputation on VCF files using a new approach, called LB-Impute. Features i to iv operate through inclusion of new Java modules that are used transparently by MapDisto; (v) QTL detection via a new R/qtl graphical interface. The program is available free of charge at mapdisto.free.fr. mapdisto@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises

NASA Astrophysics Data System (ADS)

Jokar Arsanjani, Jamal; Vaz, Eric

2015-03-01

Until recently, land surveys and digital interpretation of remotely sensed imagery have been used to generate land use inventories. These techniques however, are often cumbersome and costly, allocating large amounts of technical and temporal costs. The technological advances of web 2.0 have brought a wide array of technological achievements, stimulating the participatory role in collaborative and crowd sourced mapping products. This has been fostered by GPS-enabled devices, and accessible tools that enable visual interpretation of high resolution satellite images/air photos provided in collaborative mapping projects. Such technologies offer an integrative approach to geography by means of promoting public participation and allowing accurate assessment and classification of land use as well as geographical features. OpenStreetMap (OSM) has supported the evolution of such techniques, contributing to the existence of a large inventory of spatial land use information. This paper explores the introduction of this novel participatory phenomenon for land use classification in Europe's metropolitan regions. We adopt a positivistic approach to assess comparatively the accuracy of these contributions of OSM for land use classifications in seven large European metropolitan regions. Thematic accuracy and degree of completeness of OSM data was compared to available Global Monitoring for Environment and Security Urban Atlas (GMESUA) datasets for the chosen metropolises. We further extend our findings of land use within a novel framework for geography, justifying that volunteered geographic information (VGI) sources are of great benefit for land use mapping depending on location and degree of VGI dynamism and offer a great alternative to traditional mapping techniques for metropolitan regions throughout Europe. Evaluation of several land use types at the local level suggests that a number of OSM classes (such as anthropogenic land use, agricultural and some natural environment classes) are viable alternatives for land use classification. These classes are highly accurate and can be integrated into planning decisions for stakeholders and policymakers.
Producing a satellite-derived map and modelling Spartina alterniflora expansion for Willapa Bay in Washington State

NASA Astrophysics Data System (ADS)

Berlin, Cynthia Jane

1998-12-01

This research addresses the identification of the areal extent of the intertidal wetlands of Willapa Bay, Washington, and the evaluation of the potential for exotic Spartina alterniflora (smooth cordgrass) expansion in the bay using a spatial geographic approach. It is hoped that the results will address not only the management needs of the study area but provide a research design that may be applied to studies of other coastal wetlands. Four satellite images, three Landsat Multi-Spectral (MSS) and one Thematic Mapper (TM), are used to derive a map showing areas of water, low, middle and high intertidal, and upland. Two multi-date remote sensing mapping techniques are assessed: a supervised classification using density-slicing and an unsupervised classification using an ISODATA algorithm. Statistical comparisons are made between the resultant derived maps and the U.S.G.S. topographic maps for the Willapa Bay area. The potential for Spartina expansion in the bay is assessed using a sigmoidal (logistic) growth model and a spatial modelling procedure for four possible growth scenarios: without management controls (Business-as-Usual), with moderate management controls (e.g. harvesting to eliminate seed setting), under a hypothetical increase in the growth rate that may reflect favorable environmental changes, and under a hypothetical decrease in the growth rate that may reflect aggressive management controls. Comparisons for the statistics of the two mapping techniques suggest that although the unsupervised classification method performed satisfactorily, the supervised classification (density-slicing) method provided more satisfactory results. Results from the modelling of potential Spartina expansion suggest that Spartina expansion will proceed rapidly for the Business-as-Usual and hypothetical increase in the growth rate scenario, and at a slower rate for the elimination of seed setting and hypothetical decrease in the growth rate scenarios, until all potential habitat is filled.
Digital overlaying of the Universal Transverse Mercator Grid with LANDSAT-data derived products

NASA Technical Reports Server (NTRS)

Pendleton, T. W.

1976-01-01

Software has been written in FORTRAN IV for a Varian 73 computer which reformats LANDSAT-data-derived surface classifications and pictorial representations into a digital array which corresponds to the Universal Transverse Mercator Grid.
SOM Classification of Martian TES Data

NASA Technical Reports Server (NTRS)

Hogan, R. C.; Roush, T. L.

2002-01-01

A classification scheme based on unsupervised self-organizing maps (SOM) is described. Results from its application to the ASU mineral spectral database are presented. Applications to the Martian Thermal Emission Spectrometer data are discussed. Additional information is contained in the original extended abstract.
An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.
Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.
High resolution mapping and classification of oyster habitats in nearshore Louisiana using sidescan sonar

USGS Publications Warehouse

Allen, Y.C.; Wilson, C.A.; Roberts, H.H.; Supan, J.

2005-01-01

Sidescan sonar holds great promise as a tool to quantitatively depict the distribution and extent of benthic habitats in Louisiana's turbid estuaries. In this study, we describe an effective protocol for acoustic sampling in this environment. We also compared three methods of classification in detail: mean-based thresholding, supervised, and unsupervised techniques to classify sidescan imagery into categories of mud and shell. Classification results were compared to ground truth results using quadrat and dredge sampling. Supervised classification gave the best overall result (kappa = 75%) when compared to quadrat results. Classification accuracy was less robust when compared to all dredge samples (kappa = 21-56%), but increased greatly (90-100%) when only dredge samples taken from acoustically homogeneous areas were considered. Sidescan sonar when combined with ground truth sampling at an appropriate scale can be effectively used to establish an accurate substrate base map for both research applications and shellfish management. The sidescan imagery presented here also provides, for the first time, a detailed presentation of oyster habitat patchiness and scale in a productive oyster growing area.
ERTS-1 data applications to Minnesota forest land use classification

NASA Technical Reports Server (NTRS)

Sizer, J. E. (Principal Investigator); Eller, R. G.; Meyer, M. P.; Ulliman, J. J.

1973-01-01

The author has identified the following significant results. Color-combined ERTS-1 MSS spectral slices were analyzed to determine the maximum (repeatable) level of meaningful forest resource classification data visually attainable by skilled forest photointerpreters for the following purposes: (1) periodic updating of the Minnesota Land Management Information System (MLMIS) statewide computerized land use data bank, and (2) to provide first-stage forest resources survey data for large area forest land management planning. Controlled tests were made of two forest classification schemes by experienced professional foresters with special photointerpretation training and experience. The test results indicate it is possible to discriminate the MLMIS forest class from the MLMIS nonforest classes, but that it is not possible, under average circumstances, to further stratify the forest classification into species components with any degree of reliability with ERTS-1 imagery. An ongoing test of the resulting classification scheme involves the interpretation, and mapping, of the south half of Itasca County, Minnesota, with ERTS-1 imagery. This map is undergoing field checking by on the ground field cooperators, whose evaluation will be completed in the fall of 1973.
A review of somatoform disorders in DSM-IV and somatic symptom disorders in proposed DSM-V.

PubMed

Ghanizadeh, Ahmad; Firoozabadi, Ali

2012-12-01

Psychiatric care providers should be trained to use current changes in the somatoform disorders criteria. New diagnostic criteria for Somatic Symptom disorders in the proposed DSM-V is discussed and compared with its older counterpart in DSM-IV. A new category called Somatic Syndrome Disorders is suggested. It includes new subcategories such as "Complex Somatic Symptom Disorder" (CSSD) and "Simple Somatic Symptom Disorder" (SSSD). Some of the subcategories of DSM-IV derived disorders are included in CSSD. While there are some changes in diagnostic criteria, there are concerns and limitations about the new classification needed to be more discussed before implementation. Functional somatic disturbance, the counterpart of converion disorder in DSM-IV, can be highly dependet on the developmental level of children. However, the role of developmental level needs to be considered.
Semantics-informed cartography: the case of Piemonte Geological Map

NASA Astrophysics Data System (ADS)

Piana, Fabrizio; Lombardo, Vincenzo; Mimmo, Dario; Giardino, Marco; Fubelli, Giandomenico

2016-04-01

In modern digital geological maps, namely those supported by a large geo-database and devoted to dynamical, interactive representation on WMS-WebGIS services, there is the need to provide, in an explicit form, the geological assumptions used for the design and compilation of the database of the Map, and to get a definition and/or adoption of semantic representation and taxonomies, in order to achieve a formal and interoperable representation of the geologic knowledge. These approaches are fundamental for the integration and harmonisation of geological information and services across cultural (e.g. different scientific disciplines) and/or physical barriers (e.g. administrative boundaries). Initiatives such as GeoScience Markup Language (last version is GeoSciML 4.0, 2015, http://www.geosciml.org) and the INSPIRE "Data Specification on Geology" http://inspire.jrc.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_GE_v3.0rc3.pdf (an operative simplification of GeoSciML, last version is 3.0 rc3, 2013), as well as the recent terminological shepherding of the Geoscience Terminology Working Group (GTWG) have been promoting information exchange of the geologic knowledge. Grounded on these standard vocabularies, schemas and data models, we provide a shared semantic classification of geological data referring to the study case of the synthetic digital geological map of the Piemonte region (NW Italy), named "GEOPiemonteMap", developed by the CNR Institute of Geosciences and Earth Resources, Torino (CNR IGG TO) and hosted as a dynamical interactive map on the geoportal of ARPA Piemonte Environmental Agency. The Piemonte Geological Map is grounded on a regional-scale geo-database consisting of some hundreds of GeologicUnits whose thousands instances (Mapped Features, polygons geometry) widely occur in Piemonte region, and each one is bounded by GeologicStructures (Mapped Features, line geometry). GeologicUnits and GeologicStructures have been spatially correlated through the whole region and described using the GeoSciML vocabularies. A hierarchical schema is provided for the Piemonte Geological Map that gives the parental relations between several orders of GeologicUnits referring to mostly recurring geological objects and main GeologicEvents, in a logical framework compliant with GeoSciML and INSPIRE data models. The classification criteria and the Hierarchy Schema used to define the GEOPiemonteMap Legend, as well as the intended meanings of the geological concepts used to achieve the overall classification schema, are explicitly described in several WikiGeo pages (implemented by "MediaWiki" open source software, https://www.mediawiki.org/wiki/MediaWiki). Moreover, a further step toward a formal classification of the contents (both data and interpretation) of the GEOPiemonteMap was triggered, by setting up an ontological framework, named "OntoGeonous", in order to achieve a thorough semantic characterization of the Map.
Accurate crop classification using hierarchical genetic fuzzy rule-based systems

NASA Astrophysics Data System (ADS)

Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

2014-10-01

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.
Insights into bird wing evolution and digit specification from polarizing region fate maps.

PubMed

Towers, Matthew; Signolet, Jason; Sherman, Adrian; Sang, Helen; Tickle, Cheryll

2011-08-09

The proposal that birds descended from theropod dinosaurs with digits 2, 3 and 4 was recently given support by short-term fate maps, suggesting that the chick wing polarizing region-a group that Sonic hedgehog-expressing cells-gives rise to digit 4. Here we show using long-term fate maps that Green fluorescent protein-expressing chick wing polarizing region grafts contribute only to soft tissues along the posterior margin of digit 4, supporting fossil data that birds descended from theropods that had digits 1, 2 and 3. In contrast, digit IV of the chick leg with four digits (I-IV) arises from the polarizing region. To determine how digit identity is specified over time, we inhibited Sonic hedgehog signalling. Fate maps show that polarizing region and adjacent cells are specified in parallel through a series of anterior to posterior digit fates-a process of digit specification that we suggest is involved in patterning all vertebrate limbs with more than three digits.
A diagnostic algorithm to optimize data collection and interpretation of Ripple Maps in atrial tachycardias.

PubMed

Koa-Wing, Michael; Nakagawa, Hiroshi; Luther, Vishal; Jamil-Copley, Shahnaz; Linton, Nick; Sandler, Belinda; Qureshi, Norman; Peters, Nicholas S; Davies, D Wyn; Francis, Darrel P; Jackman, Warren; Kanagaratnam, Prapa

2015-11-15

Ripple Mapping (RM) is designed to overcome the limitations of existing isochronal 3D mapping systems by representing the intracardiac electrogram as a dynamic bar on a surface bipolar voltage map that changes in height according to the electrogram voltage-time relationship, relative to a fiduciary point. We tested the hypothesis that standard approaches to atrial tachycardia CARTO™ activation maps were inadequate for RM creation and interpretation. From the results, we aimed to develop an algorithm to optimize RMs for future prospective testing on a clinical RM platform. CARTO-XP™ activation maps from atrial tachycardia ablations were reviewed by two blinded assessors on an off-line RM workstation. Ripple Maps were graded according to a diagnostic confidence scale (Grade I - high confidence with clear pattern of activation through to Grade IV - non-diagnostic). The RM-based diagnoses were corroborated against the clinical diagnoses. 43 RMs from 14 patients were classified as Grade I (5 [11.5%]); Grade II (17 [39.5%]); Grade III (9 [21%]) and Grade IV (12 [28%]). Causes of low gradings/errors included the following: insufficient chamber point density; window-of-interest<100% of cycle length (CL); <95% tachycardia CL mapped; variability of CL and/or unstable fiducial reference marker; and suboptimal bar height and scar settings. A data collection and map interpretation algorithm has been developed to optimize Ripple Maps in atrial tachycardias. This algorithm requires prospective testing on a real-time clinical platform. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
LAMMR world data base documentation support and demonstrations

NASA Technical Reports Server (NTRS)

Chin, R.; Beaudet, P.

1980-01-01

The primary purpose of the World Surface Map is to provide the LAMMR subsystem with world surface type classifications that are used to set up LAMMR LEVEL II process control. This data base will be accessed solely by the LAMMR subsystem. The SCATT and ALT subsystems will access the data base indirectly through the T sub b (Brightness Temperature) Data Bank, where the surface types were updated from a priori to current classification, and where the surface types were organized on an orbital subtrack basis. The single most important factor in the design of the World Surface Maps is the ease of access to the information while the complexity of generating these maps is of lesser importance because their generation is a one-time, off-line process. The World Surface Map provides storage of information with a resolution of 7 km necessary to set flags concerning the earth's features with a different set of maps for each month of the year.
Rapid crop cover mapping for the conterminous United States

USGS Publications Warehouse

Dahal, Devendra; Wylie, Bruce K.; Howard, Daniel

2018-01-01

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.
Comparative utility of LANDSAT-1 and Skylab data for coastal wetland mapping and ecological studies

NASA Technical Reports Server (NTRS)

Anderson, R.; Alsid, L.; Carter, V.

1975-01-01

Skylab 190-A photography and LANDSAT-1 analog data have been analyzed to determine coastal wetland mapping potential as a near term substitute for aircraft data and as a long term monitoring tool. The level of detail and accuracy of each was compared. Skylab data provides more accurate classification of wetland types, better delineation of freshwater marshes and more detailed analysis of drainage patterns. LANDSAT-1 analog data is useful for general classification, boundary definition and monitoring of human impact in wetlands.

LANDSAT data for coastal zone management. [New Jersey

NASA Technical Reports Server (NTRS)

Mckenzie, S.

1981-01-01

The lack of adequate, current data on land and water surface conditions in New Jersey led to the search for better data collections and analysis techniques. Four-channel MSS data of Cape May County and access to the OSER computer interpretation system were provided by NASA. The spectral resolution of the data was tested and a surface cover map was produced by going through the steps of supervised classification. Topics covered include classification; change detection and improvement of spectral and spatial resolution; merging LANDSAT and map data; and potential applications for New Jersey.
Semi-automated surface mapping via unsupervised classification

NASA Astrophysics Data System (ADS)

D'Amore, M.; Le Scaon, R.; Helbert, J.; Maturilli, A.

2017-09-01

Due to the increasing volume of the returned data from space mission, the human search for correlation and identification of interesting features becomes more and more unfeasible. Statistical extraction of features via machine learning methods will increase the scientific output of remote sensing missions and aid the discovery of yet unknown feature hidden in dataset. Those methods exploit algorithm trained on features from multiple instrument, returning classification maps that explore intra-dataset correlation, allowing for the discovery of unknown features. We present two applications, one for Mercury and one for Vesta.
Classification of wetlands vegetation using small scale color infrared imagery

NASA Technical Reports Server (NTRS)

Williamson, F. S. L.

1975-01-01

A classification system for Chesapeake Bay wetlands was derived from the correlation of film density classes and actual vegetation classes. The data processing programs used were developed by the Laboratory for the Applications of Remote Sensing. These programs were tested for their value in classifying natural vegetation, using digitized data from small scale aerial photography. Existing imagery and the vegetation map of Farm Creek Marsh were used to determine the optimal number of classes, and to aid in determining if the computer maps were a believable product.
Appendix A: Ecoprovinces of the Central North American Cordillera and adjacent plains

Treesearch

Dennis A. Demarchi

1994-01-01

The fundamental difference between the map presented here and other regional ecosystem classifications is that this map's ecological units are based on climatic processes rather than vegetation communities (map appears at the end of this appendix). Macroclimatic processes are the physical and thermodynamic interaction between climatic controls, or the relatively...
Concordance between gambling disorder diagnoses in the DSM-IV and DSM-5; Results from the National Epidemiological Survey of Alcohol and Related Disorders

PubMed Central

Petry, Nancy M.; Blanco, Carlos; Jin, Chelsea; Grant, Bridget F.

2015-01-01

The fifth edition of the Diagnostic and Statistic Manual for Mental Disorders (DSM-5) eliminates the committing illegal acts criterion and reduces the threshold for a diagnosis of gambling disorder to four of nine criteria. This study compared the DSM-5 “4 of 9” classification system to the “5 of 10” DSM-IV system, as well as other permutations (i.e., just lowing the threshold to four criteria or just eliminating the illegal acts criterion) in 43,093 respondents to the National Epidemiological Survey of Alcohol and Related Conditions. Subgroups were analyzed to ascertain if changes will impact differentially diagnoses based on gender, age or race/ethnicity. In the full sample and each subpopulation, prevalence rates were higher when the DSM-5 classification system was employed relative to the DSM-IV system, but the hit rate between the two systems ranged from 99.80% to 99.96%. Across all gender, age and racial/ethnic subgroups, specificity was greater than 99% when the DSM-5 system was employed relative to the DSM-IV system, and sensitivity was 100%. Results from this study suggest that eliminating the illegal acts criterion has little impact on diagnosis of gambling disorder, but lowering the threshold for diagnosis does increase the base rate in the general population and each subgroup, even though overall rates remain low and sensitivity and specificity are high. PMID:24588275
Predicted seafloor facies of Central Santa Monica Bay, California

USGS Publications Warehouse

Dartnell, Peter; Gardner, James V.

2004-01-01

Summary -- Mapping surficial seafloor facies (sand, silt, muddy sand, rock, etc.) should be the first step in marine geological studies and is crucial when modeling sediment processes, pollution transport, deciphering tectonics, and defining benthic habitats. This report outlines an empirical technique that predicts the distribution of seafloor facies for a large area offshore Los Angeles, CA using high-resolution bathymetry and co-registered, calibrated backscatter from multibeam echosounders (MBES) correlated to ground-truth sediment samples. The technique uses a series of procedures that involve supervised classification and a hierarchical decision tree classification that are now available in advanced image-analysis software packages. Derivative variance images of both bathymetry and acoustic backscatter are calculated from the MBES data and then used in a hierarchical decision-tree framework to classify the MBES data into areas of rock, gravelly muddy sand, muddy sand, and mud. A quantitative accuracy assessment on the classification results is performed using ground-truth sediment samples. The predicted facies map is also ground-truthed using seafloor photographs and high-resolution sub-bottom seismic-reflection profiles. This Open-File Report contains the predicted seafloor facies map as a georeferenced TIFF image along with the multibeam bathymetry and acoustic backscatter data used in the study as well as an explanation of the empirical classification process.
Mapping urban land cover from space: Some observations for future progress

NASA Technical Reports Server (NTRS)

Gaydos, L.

1982-01-01

The multilevel classification system adopted by the USGS for operational mapping of land use and land cover at levels 1 and 2 is discussed and the successes and failures of mapping land cover from LANDSAT digital data are reviewed. Techniques used for image interpretation and their relationships to sensor parameters are examined. The requirements for mapping levels 2 and 3 classes are considered.
California desert resource inventory using multispectral classification of digitally mosaicked Landsat frames

NASA Technical Reports Server (NTRS)

Bryant, N. A.; Mcleod, R. G.; Zobrist, A. L.; Johnson, H. B.

1979-01-01

Procedures for adjustment of brightness values between frames and the digital mosaicking of Landsat frames to standard map projections are developed for providing a continuous data base for multispectral thematic classification. A combination of local terrain variations in the Californian deserts and a global sampling strategy based on transects provided the framework for accurate classification throughout the entire geographic region.
Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

NASA Technical Reports Server (NTRS)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.
Classification and Accuracy Assessment for Coarse Resolution Mapping within the Great Lakes Basin, USA

EPA Science Inventory

This study applied a phenology-based land-cover classification approach across the Laurentian Great Lakes Basin (GLB) using time-series data consisting of 23 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) composite images (250 ...
5 CFR 900.706 - Employment practices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... changes in compensation; (iv) Job assignments, job classifications, organizational structures, position descriptions, lines of progression, and seniority lists; (v) Leaves of absence, sick leave, or any other leave..., conferences, and other related activities, and selection for leaves of absence to pursue training; (viii...
5 CFR 900.706 - Employment practices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... changes in compensation; (iv) Job assignments, job classifications, organizational structures, position descriptions, lines of progression, and seniority lists; (v) Leaves of absence, sick leave, or any other leave..., conferences, and other related activities, and selection for leaves of absence to pursue training; (viii...
5 CFR 900.706 - Employment practices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... changes in compensation; (iv) Job assignments, job classifications, organizational structures, position descriptions, lines of progression, and seniority lists; (v) Leaves of absence, sick leave, or any other leave..., conferences, and other related activities, and selection for leaves of absence to pursue training; (viii...
Proteomic analysis of cellular soluble proteins from human bronchial smooth muscle cells by combining nondenaturing micro 2DE and quantitative LC-MS/MS. 2. Similarity search between protein maps for the analysis of protein complexes.

PubMed

Jin, Ya; Yuan, Qi; Zhang, Jun; Manabe, Takashi; Tan, Wen

2015-09-01

Human bronchial smooth muscle cell soluble proteins were analyzed by a combined method of nondenaturing micro 2DE, grid gel-cutting, and quantitative LC-MS/MS and a native protein map was prepared for each of the identified 4323 proteins [1]. A method to evaluate the degree of similarity between the protein maps was developed since we expected the proteins comprising a protein complex would be separated together under nondenaturing conditions. The following procedure was employed using Excel macros; (i) maps that have three or more squares with protein quantity data were selected (2328 maps), (ii) within each map, the quantity values of the squares were normalized setting the highest value to be 1.0, (iii) in comparing a map with another map, the smaller normalized quantity in two corresponding squares was taken and summed throughout the map to give an "overlap score," (iv) each map was compared against all the 2328 maps and the largest overlap score, obtained when a map was compared with itself, was set to be 1.0 thus providing 2328 "overlap factors," (v) step (iv) was repeated for all maps providing 2328 × 2328 matrix of overlap factors. From the matrix, protein pairs that showed overlap factors above 0.65 from both protein sides were selected (431 protein pairs). Each protein pair was searched in a database (UniProtKB) on complex formation and 301 protein pairs, which comprise 35 protein complexes, were found to be documented. These results demonstrated that native protein maps and their similarity search would enable simultaneous analysis of multiple protein complexes in cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Land use survey and mapping and water resources investigation in Korea

NASA Technical Reports Server (NTRS)

Choi, J. H.; Kim, W. I.; Son, D. S. (Principal Investigator)

1978-01-01

The author has identified the following significant results. Land use imagery is applicable to land use classification for small scale land use mapping less than 1:250,000. Land use mapping by satellite is more efficient and more cost-effective than land use mapping from conventional medium altitude aerial photographs. Six categories of level 1 land use classification are recognizable from MSS imagery. A hydrogeomorphological study of the Han River basin indicates that band 7 is useful for recognizing the soil and the weathering part of bed rock. The morphological change of the main river is accurately recognized and the drainage system in the area observed is easily classified because of the more or less simple rock type. Although the direct hydrological characteristics are not obtained from the MSS imagery, the indirect information such as the permeability of the soil and the vegetation cover, is helpful in interpreting the hydrological aspects.
Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

NASA Astrophysics Data System (ADS)

Höhle, J.

2014-09-01

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.
A Tool for Creating Regionally Calibrated High-Resolution Land Cover Data Sets for the West African Sahel: Using Machine Learning to Scale Up Hand-Classified Maps in a Data-Sparse Environment

NASA Astrophysics Data System (ADS)

Van Gordon, M.; Van Gordon, S.; Min, A.; Sullivan, J.; Weiner, Z.; Tappan, G. G.

2017-12-01

Using support vector machine (SVM) learning and high-accuracy hand-classified maps, we have developed a publicly available land cover classification tool for the West African Sahel. Our classifier produces high-resolution and regionally calibrated land cover maps for the Sahel, representing a significant contribution to the data available for this region. Global land cover products are unreliable for the Sahel, and accurate land cover data for the region are sparse. To address this gap, the U.S. Geological Survey and the Regional Center for Agriculture, Hydrology and Meteorology (AGRHYMET) in Niger produced high-quality land cover maps for the region via hand-classification of Landsat images. This method produces highly accurate maps, but the time and labor required constrain the spatial and temporal resolution of the data products. By using these hand-classified maps alongside SVM techniques, we successfully increase the resolution of the land cover maps by 1-2 orders of magnitude, from 2km-decadal resolution to 30m-annual resolution. These high-resolution regionally calibrated land cover datasets, along with the classifier we developed to produce them, lay the foundation for major advances in studies of land surface processes in the region. These datasets will provide more accurate inputs for food security modeling, hydrologic modeling, analyses of land cover change and climate change adaptation efforts. The land cover classification tool we have developed will be publicly available for use in creating additional West Africa land cover datasets with future remote sensing data and can be adapted for use in other parts of the world.
Do we need a new classification of parotid gland surgery?

PubMed

Wierzbicka, Małgorzata; Piwowarczyk, Krzysztof; Nogala, Hanna; Błaszczyńska, Marzena; Kosiedrowski, Michał; Mazurek, Cezary

2016-06-30

In February 2016 the European Salivary Gland Society (ESGS) presented and recommended classification of parotidectomies based on the anatomical I-V level division of parotid gland. The main goal of this paper is to present the new classification, and to answer the question if it is more precise compared to classic one. 607 patients (315 man, 292 women) operated on for parotid tumours in a tertiary referral centre, Department of Otolaryngology, Head and Neck Surgery, Medical University of Poznań (502 benign and 105 malignant tumours). Parotid surgery descriptions provided by retrospective analysis of all operating protocols covering the years 2006-2015 were "translated" into the new classification proposed by the ESGS. Analysis of operating protocols and fitting them into the new classification proposed by the ESGS show some discrepancies, in both benign and malignant tumours. Based on the re-evaluation of 607 cases, in 94 procedures for benign tumors the only information available was that "surgery was performed within the superficial lobe". Thus, the new classification forces the surgeon to be much more precise than previously. In 3 cases the whole superficial lobe was removed, together with the upper part of the deep lobe. Because the classification lacked parotidectomy I-II-IV, it indicated that the new classification was insufficient in the aforementioned three cases. In 6 cases of ECD more than one parotid gland tumour was removed. Among malignant tumours, total parotidectomy was the predominant procedure. In 3/13 cases of expanded parotidectomy the temporomandibular joint (TMJ) was additionally removed and it seems that the acronym TMJ should be included among the additional resected structures. It is also necessary to supplement the description of the treatment with casuistically resected anatomical structures for oncological purposes (RT planning) and follow-up imaging. Currently, since 2015 in Poland there has been the National Cancer Registry of benign salivary gland tumours (https://guzyslinianek.pcss.pl). New surgical anatomy and classification based on it will be very helpful in unequivocal, albeit brief and not laborious, reporting of procedures. To summarize, the classification is: easy to use, precise, and forced the surgeon to make a detailed description saving time at the same time. Although it is broad and accurate, it did not cover all clinically rare cases, multiple foci and it does not contain key information about the rupture of the tumour's capsule, so it is necessary to complement the type of surgery by this annotations. The simple, clear and comprehensive classification is especially valuable for centres that lead registration. Thus, we are personally grateful for this new classification, which facilitates multicentre communication.
MIADS2 ... an alphanumeric map information assembly and display system for a large computer

Treesearch

Elliot L. Amidon

1966-01-01

A major improvement and extension of the Map Information Assembly and Display System (MIADS) developed in 1964 is described. Basic principles remain unchanged, but the computer programs have been expanded and rewritten for a large computer, in Fortran IV and MAP languages. The code system is extended from 99 integers to about 2,200 alphanumeric 2-character codes. Hand-...
Classification of Nortes in the Gulf of Mexico derived from wave energy maps

NASA Astrophysics Data System (ADS)

Appendini, C. M.; Hernández-Lasheras, J.

2016-02-01

Extreme wave climate in the Gulf of Mexico is determined by tropical cyclones and winds from the Central American Cold Surges, locally referred to as Nortes. While hurricanes can have catastrophic effects, extreme waves and storm surge from Nortes occur several times a year, and thus have greater impacts on human activities along the Mexican coast of the Gulf of Mexico. Despite the constant impacts from Nortes, there is no available classification that relates their characteristics (e.g. pressure gradients, wind speed), to the associated coastal impacts. This work presents a first approximation to characterize and classify Nortes, which is based on the assumption that the derived wave energy synthetizes information (i.e. wind intensity, direction and duration) of individual Norte events as they pass through the Gulf of Mexico. First, we developed an index to identify Nortes based on surface pressure differences of two locations. To validate the methodology we compared the events identified with other studies and available Nortes logs. Afterwards, we detected Nortes from the 1986/1987, 2008/2009 and 2009/2010 seasons and used their corresponding wind fields to derive the wave energy maps using a numerical wave model. We used the energy maps to classify the events into groups using manual (visual) and automatic classifications (principal component analysis and k-means). The manual classification identified 3 types of Nortes and the automatic classification identified 5, although 3 of them had a high degree of similarity. The principal component analysis indicated that all events have similar characteristics, as few components are necessary to explain almost all of the variance. The classification from the k-means indicated that 81% of analyzed Nortes affect the southeastern Gulf of Mexico, while a smaller percentage affects the northern Gulf of Mexico and even less affect the western Caribbean.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.