Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data

PubMed Central

Vaglio Laurin, Gaia; Chan, Jonathan Cheung-Wai; Chen, Qi; Lindsell, Jeremy A.; Coomes, David A.; Guerriero, Leila; Frate, Fabio Del; Miglietta, Franco; Valentini, Riccardo

2014-01-01

Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m2 in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m2 resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R2 = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales. PMID:24937407

Crop water-stress assessment using an airborne thermal scanner

NASA Technical Reports Server (NTRS)

Millard, J. P.; Jackson, R. D.; Reginato, R. J.; Idso, S. B.; Goettelman, R. C.

1978-01-01

An airborne thermal scanner was used to measure the temperature of a wheat crop canopy in Phoenix, Arizona. The results indicate that canopy temperatures acquired about an hour and a half past solar noon were well correlated with presunrise plant water tension, a parameter directly related to plant growth and development. Pseudo-colored thermal images reading directly in stress degree days, a unit indicative of crop irrigation needs and yield potential, were produced. The aircraft data showed significant within-field canopy temperature variability, indicating the superiority of the synoptic view provided by aircraft over localized ground measurements. The standard deviation between airborne and ground-acquired canopy temperatures was 2 C or less.

Linking rainforest ecophysiology and microclimate through fusion of airborne LiDAR and hyperspectral imagery

Treesearch

Eben N. Broadbent; Angélica M. Almeyda Zambrano; Gregory P. Asner; Christopher B. Field; Brad E. Rosenheim; Ty Kennedy-Bowdoin; David E. Knapp; David Burke; Christian Giardina; Susan Cordell

2014-01-01

We develop and validate a high-resolution three-dimensional model of light and air temperature for a tropical forest interior in Hawaii along an elevation gradient varying greatly in structure but maintaining a consistent species composition. Our microclimate models integrate high-resolution airborne waveform light detection and ranging data (LiDAR) and hyperspectral...

NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

NASA Technical Reports Server (NTRS)

Cook, Bruce D.; Corp, Lawrence A.; Nelson, Ross F.; Middleton, Elizabeth M.; Morton, Douglas C.; McCorkel, Joel T.; Masek, Jeffrey G.; Ranson, Kenneth J.; Ly, Vuong; Montesano, Paul M.

2013-01-01

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (approximately 1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.

Multipurpose Hyperspectral Imaging System

NASA Technical Reports Server (NTRS)

Mao, Chengye; Smith, David; Lanoue, Mark A.; Poole, Gavin H.; Heitschmidt, Jerry; Martinez, Luis; Windham, William A.; Lawrence, Kurt C.; Park, Bosoon

2005-01-01

A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral imaging with or without relative movement of the imaging system, and it can be used to scan a target of any size as long as the target can be imaged at the focal plane; for example, automated inspection of food items and identification of single-celled organisms. The spectral resolution of this system is greater than that of prior terrestrial multispectral imaging systems. Moreover, unlike prior high-spectral resolution airborne and spaceborne hyperspectral imaging systems, this system does not rely on relative movement of the target and the imaging system to sweep an imaging line across a scene. This compact system (see figure) consists of a front objective mounted at a translation stage with a motorized actuator, and a line-slit imaging spectrograph mounted within a rotary assembly with a rear adaptor to a charged-coupled-device (CCD) camera. Push-broom scanning is carried out by the motorized actuator which can be controlled either manually by an operator or automatically by a computer to drive the line-slit across an image at a focal plane of the front objective. To reduce the cost, the system has been designed to integrate as many as possible off-the-shelf components including the CCD camera and spectrograph. The system has achieved high spectral and spatial resolutions by using a high-quality CCD camera, spectrograph, and front objective lens. Fixtures for attachment of the system to a microscope (U.S. Patent 6,495,818 B1) make it possible to acquire multispectral images of single cells and other microscopic objects.

Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

DOE PAGES

Habib, Ayman; Han, Youkyung; Xiong, Weifeng; ...

2016-09-24

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging ismore » based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a

Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

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

Habib, Ayman; Han, Youkyung; Xiong, Weifeng

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging ismore » based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a

Airborne Hyperspectral Imagery for the Detection of Agricultural Crop Stress

NASA Technical Reports Server (NTRS)

Cassady, Philip E.; Perry, Eileen M.; Gardner, Margaret E.; Roberts, Dar A.

2001-01-01

Multispectral digital imagery from aircraft or satellite is presently being used to derive basic assessments of crop health for growers and others involved in the agricultural industry. Research indicates that narrow band stress indices derived from hyperspectral imagery should have improved sensitivity to provide more specific information on the type and cause of crop stress, Under funding from the NASA Earth Observation Commercial Applications Program (EOCAP) we are identifying and evaluating scientific and commercial applications of hyperspectral imagery for the remote characterization of agricultural crop stress. During the summer of 1999 a field experiment was conducted with varying nitrogen treatments on a production corn-field in eastern Nebraska. The AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) hyperspectral imager was flown at two critical dates during crop development, at two different altitudes, providing images with approximately 18m pixels and 3m pixels. Simultaneous supporting soil and crop characterization included spectral reflectance measurements above the canopy, biomass characterization, soil sampling, and aerial photography. In this paper we describe the experiment and results, and examine the following three issues relative to the utility of hyperspectral imagery for scientific study and commercial crop stress products: (1) Accuracy of reflectance derived stress indices relative to conventional measures of stress. We compare reflectance-derived indices (both field radiometer and AVIRIS) with applied nitrogen and with leaf level measurement of nitrogen availability and chlorophyll concentrations over the experimental plots (4 replications of 5 different nitrogen levels); (2) Ability of the hyperspectral sensors to detect sub-pixel areas under crop stress. We applied the stress indices to both the 3m and 18m AVIRIS imagery for the entire production corn field using several sub-pixel areas within the field to compare the relative

Evaluation of eelgrass beds mapping using a high-resolution airborne multispectral scanner

USGS Publications Warehouse

Su, H.; Karna, D.; Fraim, E.; Fitzgerald, M.; Dominguez, R.; Myers, J.S.; Coffland, B.; Handley, L.R.; Mace, T.

2006-01-01

Eelgrass (Zostera marina) can provide vital ecological functions in stabilizing sediments, influencing current dynamics, and contributing significant amounts of biomass to numerous food webs in coastal ecosystems. Mapping eelgrass beds is important for coastal water and nearshore estuarine monitoring, management, and planning. This study demonstrated the possible use of high spatial (approximately 5 m) and temporal (maximum low tide) resolution airborne multispectral scanner on mapping eelgrass beds in Northern Puget Sound, Washington. A combination of supervised and unsupervised classification approaches were performed on the multispectral scanner imagery. A normalized difference vegetation index (NDVI) derived from the red and near-infrared bands and ancillary spatial information, were used to extract and mask eelgrass beds and other submerged aquatic vegetation (SAV) in the study area. We evaluated the resulting thematic map (geocoded, classified image) against a conventional aerial photograph interpretation using 260 point locations randomly stratified over five defined classes from the thematic map. We achieved an overall accuracy of 92 percent with 0.92 Kappa Coefficient in the study area. This study demonstrates that the airborne multispectral scanner can be useful for mapping eelgrass beds in a local or regional scale, especially in regions for which optical remote sensing from space is constrained by climatic and tidal conditions. ?? 2006 American Society for Photogrammetry and Remote Sensing.

Airborne infrared-hyperspectral mapping for detection of gaseous and solid targets

NASA Astrophysics Data System (ADS)

Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Farley, V.; Lagueux, P.; Marcotte, F.; Chamberland, M.

2010-04-01

Airborne hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. The Telops Hyper-Cam is a rugged and compact infrared hyperspectral imager based on the Fourier-transform technology. It has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. The technology offers fine spectral resolution (up to 0.25 cm-1) and high accuracy radiometric calibration (better than 1 degree Celsius). Furthermore, the spectral resolution, spatial resolution, swath width, integration time and sensitivity are all flexible parameters that can be selected and optimized to best address the specific objectives of each mission. The system performance and a few measurements have been presented in previous publications. This paper focuses on analyzing additional measurements in which detection of fertilizer and Freon gas has been demonstrated.

Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery

USDA-ARS?s Scientific Manuscript database

Citrus greening or Huanglongbing (HLB) is a devastating disease spread in many citrus groves since first found in 2005 in Florida. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were taken to detect citrus greening infected trees in 2007 and 2010. Ground truthi...

Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data.

PubMed

Dalponte, Michele; Coomes, David A

2016-10-01

Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales.We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density.We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks ( r 2  = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect.An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.

Alteration mineral mapping and metallogenic prediction using CASI/SASI airborne hyperspectral data in Mingshujing area of Gansu Province, NW China

NASA Astrophysics Data System (ADS)

Sun, Yu; Zhao, Yingjun; Qin, Kai; Tian, Feng

2016-04-01

Hyperspectral remote sensing is a frontier of remote sensing. Due to its advantage of integrated image with spectrum, it can realize objects identification, superior to objects classification of multispectral remote sensing. Taken the Mingshujing area in Gansu Province of China as an example, this study extracted the alteration minerals and thus to do metallogenic prediction using CASI/SASI airborne hyperspectral data. The Mingshujing area, located in Liuyuan region of Gansu Province, is dominated by middle Variscan granites and Indosinian granites, with well developed EW- and NE-trending faults. In July 2012, our project team obtained the CASI/SASI hyperspectral data of Liuyuan region by aerial flight. The CASI hyperspectral data have 32 bands and the SASI hyperspectral data have 88 bands, with spectral resolution of 15nm for both. The hyperspectral raw data were first preprocessed, including radiometric correction and geometric correction. We then conducted atmospheric correction using empirical line method based on synchronously measured ground spectra to obtain hyperspectral reflectance data. Spectral dimension of hyperspectral data was reduced by the minimum noise fraction transformation method, and then purity pixels were selected. After these steps, image endmember spectra were obtained. We used the endmember spectrum election method based on expert knowledge to analyze the image endmember spectra. Then, the mixture tuned matched filter (MTMF) mapping method was used to extract mineral information, including limonite, Al-rich sericite, Al-poor sericite and chlorite. Finally, the distribution of minerals in the Mingshujing area was mapped. According to the distribution of limonite and Al-rich sericite mapped by CASI/SASI hyperspectral data, we delineated five gold prospecting areas, and further conducted field verification in these areas. It is shown that there are significant gold mineralized anomalies in surface in the Baixianishan and Xitan prospecting

Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data.

PubMed

Miura, Tomoaki; Huete, Alfredo R

2009-01-01

In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The "reflectance mode (RM)" method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The "linear-interpolation (LI)" method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and post-flight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The "continuous panel (CP)" method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accurate reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 ± 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies. An important

Great Lakes Hyperspectral Water Quality Instrument Suite for Airborne Monitoring of Algal Blooms

NASA Technical Reports Server (NTRS)

Lekki, John; Leshkevich, George; Nguyen, Quang-Viet; Flatico, Joseph; Prokop, Norman; Kojima, Jun; Anderson, Robert; Demers, James; Krasowski, Michael

2007-01-01

NASA Glenn Research Center and NOAA Great Lakes Environmental Research Lab are collaborating to utilize an airborne hyperspectral imaging sensor suite to monitor Harmful Algal Blooms (HABs) in the western basin of Lake Erie. The HABs are very dynamic events as they form, spread and then disappear within a 4 to 8 week time period in late summer. They are a concern for human health, fish and wildlife because they can contain blue green toxic algae. Because of this toxicity there is a need for the blooms to be continually monitored. This situation is well suited for aircraft based monitoring because the blooms are a very dynamic event and they can spread over a large area. High resolution satellite data is not suitable by itself because it will not give the temporal resolution due to the infrequent overpasses of the quickly changing blooms. A custom designed hyperspectral imager and a point spectrometer mounted on aT 34 aircraft have been used to obtain data on an algal bloom that formed in the western basin of Lake Erie during September 2006. The sensor suite and operations will be described and preliminary hyperspectral data of this event will be presented

A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions

NASA Astrophysics Data System (ADS)

Ibrahim, Elsy; Kim, Wonkook; Crawford, Melba; Monbaliu, Jaak

2017-02-01

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.

An oil film information retrieval method overcoming the influence of sun glitter, based on AISA+ airborne hyper-spectral image

NASA Astrophysics Data System (ADS)

Zhan, Yuanzeng; Mao, Tianming; Gong, Fang; Wang, Difeng; Chen, Jianyu

2010-10-01

As an effective survey tool for oil spill detection, the airborne hyper-spectral sensor affords the potentiality for retrieving the quantitative information of oil slick which is useful for the cleanup of spilled oil. But many airborne hyper-spectral images are affected by sun glitter which distorts radiance values and spectral ratios used for oil slick detection. In 2005, there's an oil spill event leaking at oil drilling platform in The South China Sea, and an AISA+ airborne hyper-spectral image recorded this event will be selected for studying in this paper, which is affected by sun glitter terribly. Through a spectrum analysis of the oil and water samples, two features -- "spectral rotation" and "a pair of fixed points" can be found in spectral curves between crude oil film and water. Base on these features, an oil film information retrieval method which can overcome the influence of sun glitter is presented. Firstly, the radiance of the image is converted to normal apparent reflectance (NormAR). Then, based on the features of "spectral rotation" (used for distinguishing oil film and water) and "a pair of fixed points" (used for overcoming the effect of sun glitter), NormAR894/NormAR516 is selected as an indicator of oil film. Finally, by using a threshold combined with the technologies of image filter and mathematic morphology, the distribution and relative thickness of oil film are retrieved.

Airborne Hyperspectral Survey of Afghanistan 2007: Flight Line Planning and HyMap Data Collection

USGS Publications Warehouse

Kokaly, Raymond F.; King, Trude V.V.; Livo, K. Eric

2008-01-01

Hyperspectral remote sensing data were acquired over Afghanistan with the HyMap imaging spectrometer (Cocks and others, 1998) operating on the WB-57 high altitude NASA research aircraft (http://jsc-aircraft-ops.jsc.nasa.gov/wb57/index.html). These data were acquired during the interval of August 22, 2007 to October 2, 2007, as part of the United States Geological Survey (USGS) project 'Oil and Gas Resources Assessment of the Katawaz and Helmand Basins'. A total of 218 flight lines of hyperspectral remote sensing data were collected over the country. This report describes the planning of the airborne survey and the flight lines that were flown. Included with this report are digital files of the nadir tracks of the flight lines, including a map of the labeled flight lines and corresponding vector shape files for geographic information systems (GIS).

A methodology for luminance map retrieval using airborne hyperspectral and photogrammetric data

NASA Astrophysics Data System (ADS)

Pipia, Luca; Alamús, Ramon; Tardà, Anna; Pérez, Fernando; Palà, Vicenç; Corbera, Jordi

2014-10-01

This paper puts forward a methodology developed at the Institut Cartogràfic i Geològic de Catalunya (ICGC) to quantify upwelling light flux using hyperspectral and photogrammetric airborne data. The work was carried out in the frame of a demonstrative study requested by the municipality of Sant Cugat del Vallès, in the vicinity of Barcelona (Spain), and aimed to envisage a new approach to assess artificial lighting policies and actions as alternative to field campaigns. Hyperspectral and high resolution multispectral/panchromatic data were acquired simultaneously over urban areas. In order to avoid moon light contributions, data were acquired during the first days of new moon phase. Hyperspectral data were radiometrically calibrated. Then, National Center for Environmental Prediction (NCEP) atmospheric profiles were employed to estimate the actual Column Water Vapor (CWV) to be passed to ModTran5.0 for the atmospheric transmissivity τ calculation. At-the-ground radiance was finally integrated using the photopic sensitivity curve to generate a luminance map (cdm-2) of the flown area by mosaicking the different flight tracks. In an attempt to improve the spatial resolution and enhance the dynamic range of the luminance map, a sensor-fusion strategy was finally looked into. DMC Photogrammetric data acquired simultaneously to hyperspectral information were converted into at-the-ground radiance and upscaled to CASI spatial resolution. High-resolution (HR) luminance maps with enhanced dynamic range were finally generated by linearly fitting up-scaled DMC mosaics to the CASI-based luminance information. In the end, a preliminary assessment of the methodology is carried out using non-simultaneous in-situ measurements.

Upgraded airborne scanner for commercial remote sensing

NASA Astrophysics Data System (ADS)

Chang, Sheng-Huei; Rubin, Tod D.

1994-06-01

Traditional commercial remote sensing has focused on the geologic market, with primary focus on mineral identification and mapping in the visible through short-wave infrared spectral regions (0.4 to 2.4 microns). Commercial remote sensing users now demand airborne scanning capabilities spanning the entire wavelength range from ultraviolet through thermal infrared (0.3 to 12 microns). This spectral range enables detection, identification, and mapping of objects and liquids on the earth's surface and gases in the air. Applications requiring this range of wavelengths include detection and mapping of oil spills, soil and water contamination, stressed vegetation, and renewable and non-renewable natural resources, and also change detection, natural hazard mitigation, emergency response, agricultural management, and urban planning. GER has designed and built a configurable scanner that acquires high resolution images in 63 selected wave bands in this broad wavelength range.

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing.

PubMed

William, David J; Rybicki, Nancy B; Lombana, Alfonso V; O'Brien, Tim M; Gomez, Richard B

2003-01-01

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

Estimating Leaf Water Potential of Giant Sequoia Trees from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Francis, E. J.; Asner, G. P.

2015-12-01

Recent drought-induced forest dieback events have motivated research on the mechanisms of tree survival and mortality during drought. Leaf water potential, a measure of the force exerted by the evaporation of water from the leaf surface, is an indicator of plant water stress and can help predict tree mortality in response to drought. Scientists have traditionally measured water potentials on a tree-by-tree basis, but have not been able to produce maps of tree water potential at the scale of a whole forest, leaving forest managers unaware of forest drought stress patterns and their ecosystem-level consequences. Imaging spectroscopy, a technique for remote measurement of chemical properties, has been used to successfully estimate leaf water potentials in wheat and maize crops and pinyon-pine and juniper trees, but these estimates have never been scaled to the canopy level. We used hyperspectral reflectance data collected by the Carnegie Airborne Observatory (CAO) to map leaf water potentials of giant sequoia trees (Sequoiadendron giganteum) in an 800-hectare grove in Sequoia National Park. During the current severe drought in California, we measured predawn and midday leaf water potentials of 48 giant sequoia trees, using the pressure bomb method on treetop foliage samples collected with tree-climbing techniques. The CAO collected hyperspectral reflectance data at 1-meter resolution from the same grove within 1-2 weeks of the tree-level measurements. A partial least squares regression was used to correlate reflectance data extracted from the 48 focal trees with their water potentials, producing a model that predicts water potential of giant sequoia trees. Results show that giant sequoia trees can be mapped in the imagery with a classification accuracy of 0.94, and we predicted the water potential of the mapped trees to assess 1) similarities and differences between a leaf water potential map and a canopy water content map produced from airborne hyperspectral data, 2

PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING

EPA Science Inventory

The use of airborne hyperspectral remote sensing imagery for automated mapping of submersed aquatic vegetation in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery, together with in-situ spectral refl...

Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots

EPA Science Inventory

Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. ...

Hyperspectral Remote Sensing of Atmospheric Profiles from Satellites and Aircraft

NASA Technical Reports Server (NTRS)

Smith, W. L.; Zhou, D. K.; Harrison, F. W.; Revercomb, H. E.; Larar, A. M.; Huang, H. L.; Huang, B.

2001-01-01

A future hyperspectral resolution remote imaging and sounding system, called the GIFTS (Geostationary Imaging Fourier Transform Spectrometer), is described. An airborne system, which produces the type of hyperspectral resolution sounding data to be achieved with the GIFTS, has been flown on high altitude aircraft. Results from simulations and from the airborne measurements are presented to demonstrate the revolutionary remote sounding capabilities to be realized with future satellite hyperspectral remote imaging/sounding systems.

Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak-pine forest

Treesearch

Nicholas S. Skowronski; Kenneth L. Clark; Michael Gallagher; Richard A. Birdsey; John L. Hom

2014-01-01

We estimated aboveground tree biomass and change in aboveground tree biomass using repeated airborne laser scanner (ALS) acquisitions and temporally coincident ground observations of forest biomass, for a relatively undisturbed period (2004-2007; ∇07-04), a contrasting period of disturbance (2007-2009; ∇09-07...

Health condition assessment for vegetation exposed to heavy metal pollution through airborne hyperspectral data.

PubMed

Banerjee, Bikram Pratap; Raval, Simit; Zhai, Hao; Cullen, Patrick Joseph

2017-11-03

Recent advancements in hyperspectral remote sensing technology now provide improved diagnostic capabilities to assess vegetation health conditions. This paper uses a set of 13 vegetation health indices related to chlorophyll, xanthophyll, blue/green/red ratio and structure from airborne hyperspectral reflectance data collected around a derelict mining area in Yerranderie, New South Wales, Australia. The studied area has ten historic mine shafts with a legacy of heavy metals and acidic contamination in a pristine ecosystem now recognised as Great Blue Mountain World Heritage Area. The forest is predominantly comprised of different species of Eucalyptus trees. In addition to the airborne survey, ground-based spectra of the tree leaves were collected along the two accessible heavy metal contaminated pathways. The stream networks in the area were classified and the geospatial patterns of vegetation health were analysed along the Tonalli River, a major water tributary flowing through the National Park. Despite the inflow of contaminated water from the near-mine streams, the measured vegetation health indices along Tonalli River were found to remain unchanged. The responses of the vegetation health indices between the near-mine and away-mine streams were found similar. Based on the along-stream and inter-stream analysis of the spectral indices of vegetation health, no significant impact of the heavy metal pollution could be noticed. The results indicate the possibility of the vegetation having developed immunity towards the high levels of heavy metal pollution over a century of exposure.

Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

NASA Astrophysics Data System (ADS)

Luo, Shezhou; Wang, Cheng; Xi, Xiaohuan; Pan, Feifei; Qian, Mingjie; Peng, Dailiang; Nie, Sheng; Qin, Haiming; Lin, Yi

2017-06-01

Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation.

Airborne Hyperspectral Sensing of Monitoring Harmful Algal Blooms in the Great Lakes Region: System Calibration and Validation

NASA Technical Reports Server (NTRS)

Lekki, John; Anderson, Robert; Avouris, Dulcinea; Becker, RIchard; Churnside, James; Cline, Michael; Demers, James; Leshkevich, George; Liou, Larry; Luvall, Jeffrey;

Improved Scanners for Microscopic Hyperspectral Imaging

NASA Technical Reports Server (NTRS)

Mao, Chengye

2009-01-01

Improved scanners to be incorporated into hyperspectral microscope-based imaging systems have been invented. Heretofore, in microscopic imaging, including spectral imaging, it has been customary to either move the specimen relative to the optical assembly that includes the microscope or else move the entire assembly relative to the specimen. It becomes extremely difficult to control such scanning when submicron translation increments are required, because the high magnification of the microscope enlarges all movements in the specimen image on the focal plane. To overcome this difficulty, in a system based on this invention, no attempt would be made to move either the specimen or the optical assembly. Instead, an objective lens would be moved within the assembly so as to cause translation of the image at the focal plane: the effect would be equivalent to scanning in the focal plane. The upper part of the figure depicts a generic proposed microscope-based hyperspectral imaging system incorporating the invention. The optical assembly of this system would include an objective lens (normally, a microscope objective lens) and a charge-coupled-device (CCD) camera. The objective lens would be mounted on a servomotor-driven translation stage, which would be capable of moving the lens in precisely controlled increments, relative to the camera, parallel to the focal-plane scan axis. The output of the CCD camera would be digitized and fed to a frame grabber in a computer. The computer would store the frame-grabber output for subsequent viewing and/or processing of images. The computer would contain a position-control interface board, through which it would control the servomotor. There are several versions of the invention. An essential feature common to all versions is that the stationary optical subassembly containing the camera would also contain a spatial window, at the focal plane of the objective lens, that would pass only a selected portion of the image. In one version

Characterization of methane emissions in Los Angeles with airborne hyperspectral imaging

NASA Astrophysics Data System (ADS)

Saad, K.; Tratt, D. M.; Buckland, K. N.; Roehl, C. M.; Wennberg, P. O.; Wunch, D.

2017-12-01

As urban areas develop regulations to limit atmospheric methane (CH4), accurate quantification of anthropogenic emissions will be critical for program development and evaluation. However, relating emissions derived from process-level metadata to those determined from assimilating atmospheric observations of CH4 concentrations into models is particularly difficult. Non-methane hydrocarbons (NMHCs) can help differentiate between thermogenic and biogenic CH4 emissions, as they are primarily co-emitted with the former; however, these trace gases are subject to the same limitations as CH4. Remotely-sensed hyperspectral imaging bridges these approaches by measuring emissions plumes directly with spatial coverage on the order of 10 km2 min-1. We identify the sources of and evaluate emissions plumes measured by airborne infrared hyperspectral imagers flown over the Los Angeles (LA) metropolitan area, which encompasses various CH4 sources, including petroleum and natural gas wells and facilities. We quantify total CH4 and NMHC emissions, as well as their relative column densities, at the point-source level to create fingerprints of source types. We aggregate these analyses to estimate the range of variability in chemical composition across source types. These CH4 and NMHC emissions factors are additionally compared to their tropospheric column abundances measured by the Total Carbon Column Observing Network (TCCON) Pasadena Fourier transform infrared spectrometer, which provides a footprint for the LA basin.

Airborne Hyperspectral Remote Sensing

DTIC Science & Technology

1999-09-30

REFERENCES Davis, C. O., M. Kappus , J. Bowles, J. Fisher, J. Antoniades, and M. Carney, “Calibration, Characterization and first Results with the Ocean...PUBLICATIONS Davis, C. O., M. Kappus , J. Bowles, J. Fisher, J. Antoniades, and M. Carney, “Calibration, Characterization and first Results with the Ocean PHILLS Hyperspectral Imager”, Proceedings of the SPIE, V. 3753, In Press.

Alteration Minerals Extraction Using Airborne Hyperspectral Data Casi and Sasi in Wuyi Metallogenic Belt, China

NASA Astrophysics Data System (ADS)

Huang, Z.; Zheng, J.

2018-04-01

Hydrothermal alteration is an important content in the study of epithermal deposit, and its deep part is often accompanied by porphyry mineralization. The objective of research is to mapping the alteration minerals for mineral exploration using mixture tuned matched filtering (MTMF) approach based on airborne hyperspectral data CASI and SASI in Wuyi metallogenic belt, China, which has complex geological structure and excellent mineralization conditions and high regional forest coverage rate. Gold mineralization is closely related to the Yanshan period epithermal intrusive rocks, and often exists in external contact zone of allgovite, monzomite porphyrite, granite porphyry, quarz porphyry, et al.. The main mineral alteration types include silicification (quartz), sericitization (sericite, illite), pyritization (pyrite), chloritization (chlorite), and partial calcitization (calcite). The alteration minerals extraction based on integrated CASI_SASI reflectance data were processed by MTMF algorithm with the input imagery which was pre-processed by MNF and the input endmember spectra measured by SVC spectrometer to performs MF and add an infeasibility image. The MTMF results provide an estimate to mineral subpixel fractions leading to the abundances of alteration minerals at each pixel and alteration minerals distribution. The accuracy of alteration mineral extraction refers to the extent which it agrees with a set of reference data measured in the field reconnaissance. So the CASI_SASI airborne hyperspectral image provides the efficient way to map the detailed alteration minerals distribution for mineral exploration in high forest coverage area.

Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

NASA Astrophysics Data System (ADS)

Kozoderov, V. V.; Kondranin, T. V.; Dmitriev, E. V.

2017-12-01

The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the " K-weighted neighbors" method that is close to the nonparametric Bayesian classifier.

Airborne Hyperspectral Imaging System

NASA Technical Reports Server (NTRS)

Behar, Alberto E.; Cooper, Moogega; Adler, John; Jacobson, Tobias

2012-01-01

A document discusses a hyperspectral imaging instrument package designed to be carried aboard a helicopter. It was developed to map the depths of Greenland's supraglacial lakes. The instrument is capable of telescoping to twice its original length, allowing it to be retracted with the door closed during takeoff and landing, and manually extended in mid-flight. While extended, the instrument platform provides the attached hyperspectral imager a nadir-centered and unobstructed view of the ground. Before flight, the instrument mount is retracted and securely strapped down to existing anchor points on the floor of the helicopter. When the helicopter reaches the destination lake, the door is opened and the instrument mount is manually extended. Power to the instrument package is turned on, and the data acquisition computer is commanded via a serial cable from an onboard user-operated laptop to begin data collection. After data collection is complete, the instrument package is powered down and the mount retracted, allowing the door to be closed in preparation for landing. The present design for the instrument mount consists of a three-segment telescoping cantilever to allow for a sufficient extended length to see around the landing struts and provide a nadir-centered and unobstructed field of view for the hyperspectral imager. This instrument works on the premise that water preferentially absorbs light with longer wavelengths on the red side of the visible spectrum. This property can be exploited in order to remotely determine the depths of bodies of pure freshwater. An imager flying over such a lake receives light scattered from the surface, the bulk of the water column, and from the lake bottom. The strength of absorption of longer-wavelength light depends on the depth of the water column. Through calibration with in situ measurements of the water depths, a depth-determining algorithm may be developed to determine lake depth from these spectral properties of the

Hyperspectral Systems Increase Imaging Capabilities

NASA Technical Reports Server (NTRS)

2010-01-01

In 1983, NASA started developing hyperspectral systems to image in the ultraviolet and infrared wavelengths. In 2001, the first on-orbit hyperspectral imager, Hyperion, was launched aboard the Earth Observing-1 spacecraft. Based on the hyperspectral imaging sensors used in Earth observation satellites, Stennis Space Center engineers and Institute for Technology Development researchers collaborated on a new design that was smaller and used an improved scanner. Featured in Spinoff 2007, the technology is now exclusively licensed by Themis Vision Systems LLC, of Richmond, Virginia, and is widely used in medical and life sciences, defense and security, forensics, and microscopy.

Classification of urban features using airborne hyperspectral data

NASA Astrophysics Data System (ADS)

Ganesh Babu, Bharath

Accurate mapping and modeling of urban environments are critical for their efficient and successful management. Superior understanding of complex urban environments is made possible by using modern geospatial technologies. This research focuses on thematic classification of urban land use and land cover (LULC) using 248 bands of 2.0 meter resolution hyperspectral data acquired from an airborne imaging spectrometer (AISA+) on 24th July 2006 in and near Terre Haute, Indiana. Three distinct study areas including two commercial classes, two residential classes, and two urban parks/recreational classes were selected for classification and analysis. Four commonly used classification methods -- maximum likelihood (ML), extraction and classification of homogeneous objects (ECHO), spectral angle mapper (SAM), and iterative self organizing data analysis (ISODATA) - were applied to each data set. Accuracy assessment was conducted and overall accuracies were compared between the twenty four resulting thematic maps. With the exception of SAM and ISODATA in a complex commercial area, all methods employed classified the designated urban features with more than 80% accuracy. The thematic classification from ECHO showed the best agreement with ground reference samples. The residential area with relatively homogeneous composition was classified consistently with highest accuracy by all four of the classification methods used. The average accuracy amongst the classifiers was 93.60% for this area. When individually observed, the complex recreational area (Deming Park) was classified with the highest accuracy by ECHO, with an accuracy of 96.80% and 96.10% Kappa. The average accuracy amongst all the classifiers was 92.07%. The commercial area with relatively high complexity was classified with the least accuracy by all classifiers. The lowest accuracy was achieved by SAM at 63.90% with 59.20% Kappa. This was also the lowest accuracy in the entire analysis. This study demonstrates the

Modeling of estuarne chlorophyll a from an airborne scanner

USGS Publications Warehouse

Khorram, Siamak; Catts, Glenn P.; Cloern, James E.; Knight, Allen W.

1987-01-01

Near simultaneous collection of 34 surface water samples and airborne multispectral scanner data provided input for regression models developed to predict surface concentrations of estuarine chlorophyll a. Two wavelength ratios were employed in model development. The ratios werechosen to capitalize on the spectral characteristics of chlorophyll a, while minimizing atmospheric influences. Models were then applied to data previously acquired over the study area thre years earlier. Results are in the form of color-coded displays of predicted chlorophyll a concentrations and comparisons of the agreement among measured surface samples and predictions basedon coincident remotely sensed data. The influence of large variations in fresh-water inflow to the estuary are clearly apparent in the results. The synoptic view provided by remote sensing is another method of examining important estuarine dynamics difficult to observe from in situ sampling alone.

Land cover mapping in Latvia using hyperspectral airborne and simulated Sentinel-2 data

NASA Astrophysics Data System (ADS)

Jakovels, Dainis; Filipovs, Jevgenijs; Brauns, Agris; Taskovs, Juris; Erins, Gatis

2016-08-01

Land cover mapping in Latvia is performed as part of the Corine Land Cover (CLC) initiative every six years. The advantage of CLC is the creation of a standardized nomenclature and mapping protocol comparable across all European countries, thereby making it a valuable information source at the European level. However, low spatial resolution and accuracy, infrequent updates and expensive manual production has limited its use at the national level. As of now, there is no remote sensing based high resolution land cover and land use services designed specifically for Latvia which would account for the country's natural and land use specifics and end-user interests. The European Space Agency launched the Sentinel-2 satellite in 2015 aiming to provide continuity of free high resolution multispectral satellite data thereby presenting an opportunity to develop and adapted land cover and land use algorithm which accounts for national enduser needs. In this study, land cover mapping scheme according to national end-user needs was developed and tested in two pilot territories (Cesis and Burtnieki). Hyperspectral airborne data covering spectral range 400-2500 nm was acquired in summer 2015 using Airborne Surveillance and Environmental Monitoring System (ARSENAL). The gathered data was tested for land cover classification of seven general classes (urban/artificial, bare, forest, shrubland, agricultural/grassland, wetlands, water) and sub-classes specific for Latvia as well as simulation of Sentinel-2 satellite data. Hyperspectral data sets consist of 122 spectral bands in visible to near infrared spectral range (356-950 nm) and 100 bands in short wave infrared (950-2500 nm). Classification of land cover was tested separately for each sensor data and fused cross-sensor data. The best overall classification accuracy 84.2% and satisfactory classification accuracy (more than 80%) for 9 of 13 classes was obtained using Support Vector Machine (SVM) classifier with 109 band

Assessment of chlorophyll-a concentration in the Gulf of Riga using hyperspectral airborne and simulated Sentinel-3 OLCI data

NASA Astrophysics Data System (ADS)

Jakovels, Dainis; Brauns, Agris; Filipovs, Jevgenijs; Taskovs, Juris; Fedorovicha, Dagnija; Paavel, Birgot; Ligi, Martin; Kutser, Tiit

2016-08-01

Remote sensing has proved to be an accurate and reliable tool in clear water environments like oceans or the Mediterranean Sea. However, the current algorithms and methods usually fail on optically complex waters like coastal and inland waters. The whole Baltic Sea can be considered as optically complex coastal waters. Remote assessment of water quality parameters (eg., chlorophyll-a concentration) is of interest for monitoring of marine environment, but hasn't been used as a routine approach in Latvia. In this study, two simultaneous hyperspectral airborne data and in situ measurement campaigns were performed in the Gulf of Riga near the River Daugava mouth in summer 2015 to simulate Sentinel-3 data and test existing algorithms for retrieval of Level 2 Water products. Comparison of historical data showed poor overall correlation between in situ measurements and MERIS chlorophyll-a data products. Better correlation between spectral chl-a data products and in situ water sampling measurements was achieved during simultaneous airborne and field campaign resulting in R2 up to 0.94 for field spectral data, R2 of 0.78 for airborne data. Test of all two band ratio combinations showed that R2 could be improved from 0.63 to 0.94 for hyperspectral airborne data choosing 712 and 728 nm bands instead of 709 and 666 nm, and R2 could be improved from 0.61 to 0.83 for simulated Sentinel-3 OLCI data choosing Oa10 and Oa8 bands instead of Oa11 and Oa8. Repeated campaigns are planned during spring and summer blooms 2016 in the Gulf of Riga to get larger data set for validation and evaluate repeatability. The main challenges remain to acquire as good data as possible within rapidly changing environment and often cloudy weather conditions.

Airborne imaging spectrometers developed in China

NASA Astrophysics Data System (ADS)

Wang, Jianyu; Xue, Yongqi

1998-08-01

Airborne imaging spectral technology, principle means in airborne remote sensing, has been developed rapidly both in the world and in China recently. This paper describes Modular Airborne Imaging Spectrometer (MAIS), Operational Modular Airborne Imaging Spectrometer (OMAIS) and Pushbroom Hyperspectral Imagery (PHI) that have been developed or are being developed in Airborne Remote Sensing Lab of Shanghai Institute of Technical Physics, CAS.

Isograde mapping and mineral identification on the island of Naxos, Greece, using DAIS 7915 hyperspectral data

NASA Astrophysics Data System (ADS)

Echtler, Helmut; Segl, Karl; Dickerhof, Corinna; Chabrillat, Sabine; Kaufmann, Hermann J.

2003-03-01

The ESF-LSF 1997 flight campaign conducted by the German Aerospace Center (DLR) recorded several transects across the island of Naxos using the airborne hyperspectral scanner DAIS. The geological targets cover all major litho-tectonic units of a metamorphic dome with the transition of metamorphic zonations from the outer meta-sedimentary greenschist envelope to the gneissic amphibolite facies and migmatitic core. Mineral identification of alternating marble-dolomite sequences and interlayered schists bearing muscovite and biotite has been accomplished using the airborne hyperspectral DAIS 7915 sensor. Data have been noise filtered based on maximum noise fraction (MNF) and fast Fourier transform (FFT) and converted from radiance to reflectance. For mineral identification, constrained linear spectral unmixing and spectral angle mapper (SAM) algorithms were tested. Due to their unsatisfying results a new approach was developed which consists of a linear mixture modeling and spectral feature fitting. This approach provides more detailed and accurate information. Results are discussed in comparison with detailed geological mapping and additional information. Calcites are clearly separated from dolomites as well as the mica-schist sequences by a good resolution of the mineral muscovite. Thereon an outstanding result represents the very good resolution of the chlorite/mica (muscovite, biotite)-transition defining a metamorphic isograde.

Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

NASA Astrophysics Data System (ADS)

Zhou, Xianfeng; Huang, Wenjiang; Kong, Weiping; Ye, Huichun; Luo, Juhua; Chen, Pengfei

2016-11-01

Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CIred-edge, NDVI-like and ND705 showed convincing results in CNC retrieval. NDVI-like and ND705 were sensitive to detect CNC changes less than 5 g/m2, narrowband and broadband CIred-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CIred-edge and CIgreen) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter

Hyperspectral sensors and the conservation of monumental buildings

NASA Astrophysics Data System (ADS)

Camaiti, Mara; Benvenuti, Marco; Chiarantini, Leandro; Costagliola, Pilar; Moretti, Sandro; Paba, Francesca; Pecchioni, Elena; Vettori, Silvia

2010-05-01

-FieldSpec FP Pro spectroradiometer), which continuously acquires punctual reflectance spectra in the range 350-2500 nm, both in natural light conditions and by a contact probe (fixed geometry of shot). This instrument is used on field for the identification of different materials, as well as for the definition of maps (e.g geological maps) if coupled with other hyperspectral instruments. 2) Hyperspectral sensor SIM-GA (Selex Galileo Multisensor Hyperspectral System), a system with spatial acquisition of data which may be used on an earth as well as on an airborne platform. SIM-GA consists of two electro-optical heads, which operate in the VNIR and SWIR regions, respectively, between 400-1000 nm and 1000-2500 nm (3). Although the spectral signature in the VNIR of many minerals is known, the co-presence of more minerals on a surface can affect the quantitative analysis of gypsum. Different minerals, such as gypsum, calcite, weddellite, whewellite, and other components (i.e. carbon particles in black crusts) are, in fact, commonly found on historical surfaces. In order to illustrate the complexity, but also the potentiality of hyperspectral sensors (portable or remote sensing) for the characterization of stone surfaces, a case study, the Facade of Santa Maria Novella in Florence - Italy, will be presented. References 1) R.N. Clark and G.A. Swayze, 1995, "Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm", in "Summaries of the Fifth Annual JPL Airborne Earth Science Workshop", JPL Publication 95-1,1,39-40 2) E. Ben-Dor, K. Patin, A. Banin and A. Karnieli, 2002, "Mapping of several soil properties using DATS-7915 hyperspectral scanner data. A case study over clayely soils in Israel", International Journal of Remote Sensing, 23(6), 1043-1062 3) S. Vettori, M. Benvenuti, M. Camaiti, L. Chiarantini, P. Costagliola, S. Moretti, E. Pecchioni, 2008, "Assessment of the deterioration status of

Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

NASA Astrophysics Data System (ADS)

Carpentieri, Bruno; Pizzolante, Raffaele

2017-12-01

Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

Hyperspectral forest monitoring and imaging implications

NASA Astrophysics Data System (ADS)

Goodenough, David G.; Bannon, David

2014-05-01

The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing

Postfire soil burn severity mapping with hyperspectral image unmixing

Treesearch

Peter R. Robichaud; Sarah A. Lewis; Denise Y. M. Laes; Andrew T. Hudak; Raymond F. Kokaly; Joseph A. Zamudio

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after...

Toward Improved Hyperspectral Analysis in Semiarid Systems

NASA Astrophysics Data System (ADS)

Glenn, N. F.; Mitchell, J.

2012-12-01

Idaho State University's Boise Center Aerospace Laboratory (BCAL) has processed and applied hyperspectral data for a variety of biophysical sciences in semiarid systems over the past 10 years. HyMap hyperspectral data have been used in most of these studies, along with AVIRIS, CASI, and PIKA-II data. Our studies began with the detection of individual weed species, such as leafy spurge, corroborated with extensive field analysis, including spectrometer data. Early contributions to the field of hyperspectral analysis included the use of: time-series datasets and classification threshold methods for target detection, and subpixel analysis for characterizing weed invasions and post-fire vegetation and soil conditions. Subsequent studies optimized subpixel unmixing performance using spectral subsetting and vegetation abundance investigations. More recent studies have extended the application of hyperspectral data from individual plant species detection to identification of biochemical constituents. We demonstrated field and airborne hyperspectral Nitrogen absorption in sagebrush using combinations of data reduction and spectral transformation techniques (i.e., continuum removal, derivative analysis, partial least squares regression). In spite of these and many other successful demonstrations, gaps still exist in effective species level discrimination due to the high complexity of soil and nonlinear mixing in semiarid shrubland. BCAL studies are currently focusing on complimenting narrowband vegetation indices with LiDAR (light detection and ranging, both airborne and ground-based) derivatives to improve vegetation cover predictions. Future combinations of LiDAR and hyperspectral data will involve exploring the full range spectral information and serve as an integral step in scaling shrub biomass estimates from plot to landscape and regional scales.

Crown-Level Tree Species Classification Using Integrated Airborne Hyperspectral and LIDAR Remote Sensing Data

NASA Astrophysics Data System (ADS)

Wang, Z.; Wu, J.; Wang, Y.; Kong, X.; Bao, H.; Ni, Y.; Ma, L.; Jin, J.

2018-05-01

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79

Hyperspectral Imaging of Forest Resources: The Malaysian Experience

NASA Astrophysics Data System (ADS)

Mohd Hasmadi, I.; Kamaruzaman, J.

2008-08-01

Remote sensing using satellite and aircraft images are well established technology. Remote sensing application of hyperspectral imaging, however, is relatively new to Malaysian forestry. Through a wide range of wavelengths hyperspectral data are precisely capable to capture narrow bands of spectra. Airborne sensors typically offer greatly enhanced spatial and spectral resolution over their satellite counterparts, and able to control experimental design closely during image acquisition. The first study using hyperspectral imaging for forest inventory in Malaysia were conducted by Professor Hj. Kamaruzaman from the Faculty of Forestry, Universiti Putra Malaysia in 2002 using the AISA sensor manufactured by Specim Ltd, Finland. The main objective has been to develop methods that are directly suited for practical tropical forestry application at the high level of accuracy. Forest inventory and tree classification including development of single spectral signatures have been the most important interest at the current practices. Experiences from the studies showed that retrieval of timber volume and tree discrimination using this system is well and some or rather is better than other remote sensing methods. This article reviews the research and application of airborne hyperspectral remote sensing for forest survey and assessment in Malaysia.

Determination of pasture quality using airborne hyperspectral imaging

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, G.; Yule, Ian J.; Irwin, M. E.

2015-10-01

Pasture quality is a critical determinant which influences animal performance (live weight gain, milk and meat production) and animal health. Assessment of pasture quality is therefore required to assist farmers with grazing planning and management, benchmarking between seasons and years. Traditionally, pasture quality is determined by field sampling which is laborious, expensive and time consuming, and the information is not available in real-time. Hyperspectral remote sensing has potential to accurately quantify biochemical composition of pasture over wide areas in great spatial detail. In this study an airborne imaging spectrometer (AisaFENIX, Specim) was used with a spectral range of 380-2500 nm with 448 spectral bands. A case study of a 600 ha hill country farm in New Zealand is used to illustrate the use of the system. Radiometric and atmospheric corrections, along with automatized georectification of the imagery using Digital Elevation Model (DEM), were applied to the raw images to convert into geocoded reflectance images. Then a multivariate statistical method, partial least squares (PLS), was applied to estimate pasture quality such as crude protein (CP) and metabolisable energy (ME) from canopy reflectance. The results from this study revealed that estimates of CP and ME had a R2 of 0.77 and 0.79, and RMSECV of 2.97 and 0.81 respectively. By utilizing these regression models, spatial maps were created over the imaged area. These pasture quality maps can be used for adopting precision agriculture practices which improves farm profitability and environmental sustainability.

Hyperspectral data compression using a Wiener filter predictor

NASA Astrophysics Data System (ADS)

Villeneuve, Pierre V.; Beaven, Scott G.; Stocker, Alan D.

2013-09-01

The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed "Z-Chrome" algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .

Use of field reflectance data for crop mapping using airborne hyperspectral image

NASA Astrophysics Data System (ADS)

Nidamanuri, Rama Rao; Zbell, Bernd

2011-09-01

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question "what is the prospect of using independent reference reflectance spectra for image classification", while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of "non-existence of characteristic reflectance spectral signatures for vegetation", results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.

Atmospheric correction for hyperspectral ocean color sensors

NASA Astrophysics Data System (ADS)

Ibrahim, A.; Ahmad, Z.; Franz, B. A.; Knobelspiesse, K. D.

2017-12-01

NASA's heritage Atmospheric Correction (AC) algorithm for multi-spectral ocean color sensors is inadequate for the new generation of spaceborne hyperspectral sensors, such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and by aerosols from the measured top-of-atmosphere (TOA) radiance. Further, it must also compensate for the absorption by atmospheric gases and correct for reflection and refraction of the air-sea interface. We present and evaluate an improved AC for hyperspectral sensors beyond the heritage approach by utilizing the additional spectral information of the hyperspectral sensor. The study encompasses a theoretical radiative transfer sensitivity analysis as well as a practical application of the Hyperspectral Imager for the Coastal Ocean (HICO) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensors.

Technical Report: Unmanned Helicopter Solution for Survey-Grade Lidar and Hyperspectral Mapping

NASA Astrophysics Data System (ADS)

Kaňuk, Ján; Gallay, Michal; Eck, Christoph; Zgraggen, Carlo; Dvorný, Eduard

2018-05-01

Recent development of light-weight unmanned airborne vehicles (UAV) and miniaturization of sensors provide new possibilities for remote sensing and high-resolution mapping. Mini-UAV platforms are emerging, but powerful UAV platforms of higher payload capacity are required to carry the sensors for survey-grade mapping. In this paper, we demonstrate a technological solution and application of two different payloads for highly accurate and detailed mapping. The unmanned airborne system (UAS) comprises a Scout B1-100 autonomously operating UAV helicopter powered by a gasoline two-stroke engine with maximum take-off weight of 75 kg. The UAV allows for integrating of up to 18 kg of a customized payload. Our technological solution comprises two types of payload completely independent of the platform. The first payload contains a VUX-1 laser scanner (Riegl, Austria) and a Sony A6000 E-Mount photo camera. The second payload integrates a hyperspectral push-broom scanner AISA Kestrel 10 (Specim, Finland). The two payloads need to be alternated if mapping with both is required. Both payloads include an inertial navigation system xNAV550 (Oxford Technical Solutions Ltd., United Kingdom), a separate data link, and a power supply unit. Such a constellation allowed for achieving high accuracy of the flight line post-processing in two test missions. The standard deviation was 0.02 m (XY) and 0.025 m (Z), respectively. The intended application of the UAS was for high-resolution mapping and monitoring of landscape dynamics (landslides, erosion, flooding, or crops growth). The legal regulations for such UAV applications in Switzerland and Slovakia are also discussed.

Recent progress of push-broom infrared hyper-spectral imager in SITP

NASA Astrophysics Data System (ADS)

Wang, Yueming; Hu, Weida; Shu, Rong; Li, Chunlai; Yuan, Liyin; Wang, Jianyu

2017-02-01

In the past decades, hyper-spectral imaging technologies were well developed in SITP, CAS. Many innovations for system design and key parts of hyper-spectral imager were finished. First airborne hyper-spectral imager operating from VNIR to TIR in the world was emerged in SITP. It is well known as OMIS(Operational Modular Imaging Spectrometer). Some new technologies were introduced to improve the performance of hyper-spectral imaging system in these years. A high spatial space-borne hyper-spectral imager aboard Tiangong-1 spacecraft was launched on Sep.29, 2011. Thanks for ground motion compensation and high optical efficiency prismatic spectrometer, a large amount of hyper-spectral imagery with high sensitivity and good quality were acquired in the past years. Some important phenomena were observed. To diminish spectral distortion and expand field of view, new type of prismatic imaging spectrometer based curved prism were proposed by SITP. A prototype of hyper-spectral imager based spherical fused silica prism were manufactured, which can operate from 400nm 2500nm. We also made progress in the development of LWIR hyper-spectral imaging technology. Compact and low F number LWIR imaging spectrometer was designed, manufactured and integrated. The spectrometer operated in a cryogenically-cooled vacuum box for background radiation restraint. The system performed well during flight experiment in an airborne platform. Thanks high sensitivity FPA and high performance optics, spatial resolution and spectral resolution and SNR of system are improved enormously. However, more work should be done for high radiometric accuracy in the future.

Dual mode scanner-tracker

NASA Astrophysics Data System (ADS)

Mongeon, R. J.

1984-11-01

The beam of a laser radar is moved over the field of view by means of a pair of scanner/trackers arranged in cascade along the laser beam. One of the scanner/trackers operates at high speed, with high resolution and a wide field and is located in the demagnified portion of the laser beam. The two scanner/trackers complement each other to achieve high speed, high resolution scanning as well as tracking of moving targets. A beam steering telescope for an airborne laser radar which incorporates the novel dual mode scanner/tracker is also shown. The other scanner/tracker operates at low speed with low resolution and a wide field and is located in the magnified portion of the laser beam.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data.

PubMed

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R

2015-09-11

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite's Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data

PubMed Central

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R.

2015-01-01

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds. PMID:26378538

The selectable hyperspectral airborne remote sensing kit (SHARK) as an enabler for precision agriculture

NASA Astrophysics Data System (ADS)

Holasek, Rick; Nakanishi, Keith; Ziph-Schatzberg, Leah; Santman, Jeff; Woodman, Patrick; Zacaroli, Richard; Wiggins, Richard

2017-04-01

Hyperspectral imaging (HSI) has been used for over two decades in laboratory research, academic, environmental and defense applications. In more recent time, HSI has started to be adopted for commercial applications in machine vision, conservation, resource exploration, and precision agriculture, to name just a few of the economically viable uses for the technology. Corning Incorporated (Corning) has been developing and manufacturing HSI sensors, sensor systems, and sensor optical engines, as well as HSI sensor components such as gratings and slits for over a decade and a half. This depth of experience and technological breadth has allowed Corning to design and develop unique HSI spectrometers with an unprecedented combination of high performance, low cost and low Size, Weight, and Power (SWaP). These sensors and sensor systems are offered with wavelength coverage ranges from the visible to the Long Wave Infrared (LWIR). The extremely low SWaP of Corning's HSI sensors and sensor systems enables their deployment using limited payload platforms such as small unmanned aerial vehicles (UAVs). This paper discusses use of the Corning patented monolithic design Offner spectrometer, the microHSI™, to build a highly compact 400-1000 nm HSI sensor in combination with a small Inertial Navigation System (INS) and micro-computer to make a complete turn-key airborne remote sensing payload. This Selectable Hyperspectral Airborne Remote sensing Kit (SHARK) has industry leading SWaP (1.5 lbs) at a disruptively low price due, in large part, to Corning's ability to manufacture the monolithic spectrometer out of polymers (i.e. plastic) and therefore reduce manufacturing costs considerably. The other factor in lowering costs is Corning's well established in house manufacturing capability in optical components and sensors that further enable cost-effective fabrication. The competitive SWaP and low cost of the microHSI™ sensor is approaching, and in some cases less than the price

An operational multispectral scanner for bathymetric surveys - The ABS NORDA scanner

NASA Technical Reports Server (NTRS)

Haimbach, Stephen P.; Joy, Richard T.; Hickman, G. Daniel

1987-01-01

The Naval Ocean Research and Development Activity (NORDA) is developing the Airborne Bathymetric Survey (ABS) system, which will take shallow water depth soundings from a Navy P-3 aircraft. The system combines active and passive sensors to obtain optical measurements of water depth. The ABS NORDA Scanner is the systems passive multispectral scanner whose design goal is to provide 100 percent coverage of the seafloor, to depths of 20 m in average coastal waters. The ABS NORDA Scanner hardware and operational environment is discussed in detail. The optical model providing the basis for depth extraction is reviewed and the proposed data processing routine discussed.

Detection of spatio-temporal changes of Norway spruce forest stands in Ore Mountains using airborne hyperspectral imagery

NASA Astrophysics Data System (ADS)

Misurec, J.; Kopačková, V.; Lhotáková, Z.; Albrechtova, J.; Campbell, P. K. E.

2015-12-01

The Ore Mountains are an example of the region that suffered from severe environmental pollution caused by long-term coal mining and heavy industry leading to massive dieback of the local Norway spruce forests between the 1970's and 1990's. The situation became getting better at the end of 1990's after pollution loads significantly decreased. In 1998 and 2013, airborne hyperspectral data (with sensor ASAS and APEX, respectively) were used to study recovery of the originally damaged forest stands and compared them with those that have been less affected by environmental pollution. The field campaign (needle biochemical analysis, tree defoliation etc.) accompanied hyperspectral imagery acquisition. An analysis was conducted assessing a set of 16 vegetation indices providing complex information on foliage, biochemistry and canopy biophysics and structure. Five of them (NDVI, NDVI705, VOG1, MSR and TCARI/OSAVI) showing the best results were employed to study spatial gradients as well as temporal changes. The detected gradients are in accordance with ground truth data on representative trees. The obtained results indicate that the original significant differences between the damaged and undamaged stands have been generally levelled until 2013, although it is still possible to detect signs of the previous damages in several cases.

Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK) on the Vision II turbine rotorcraft UAV over the Florida Keys

NASA Astrophysics Data System (ADS)

Holasek, R. E.; Nakanishi, K.; Swartz, B.; Zacaroli, R.; Hill, B.; Naungayan, J.; Herwitz, S.; Kavros, P.; English, D. C.

2013-12-01

As part of the NASA ROSES program, the NovaSol Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK) was flown as the payload on the unmanned Vision II helicopter. The goal of the May 2013 data collection was to obtain high resolution visible and near-infrared (visNIR) hyperspectral data of seagrasses and coral reefs in the Florida Keys. The specifications of the SHARK hyperspectral system and the Vision II turbine rotorcraft will be described along with the process of integrating the payload to the vehicle platform. The minimal size, weight, and power (SWaP) specifications of the SHARK system is an ideal match to the Vision II helicopter and its flight parameters. One advantage of the helicopter over fixed wing platforms is its inherent ability to take off and land in a limited area and without a runway, enabling the UAV to be located in close proximity to the experiment areas and the science team. Decisions regarding integration times, waypoint selection, mission duration, and mission frequency are able to be based upon the local environmental conditions and can be modified just prior to take off. The operational procedures and coordination between the UAV pilot, payload operator, and scientist will be described. The SHARK system includes an inertial navigation system and digital elevation model (DEM) which allows image coordinates to be calculated onboard the aircraft in real-time. Examples of the geo-registered images from the data collection will be shown. SHARK mounted below VTUAV. SHARK deployed on VTUAV over water.

Aerosol, Cloud and Trace Gas Observations Derived from Airborne Hyperspectral Radiance and Direct Beam Measurements in Recent Field Campaigns

NASA Technical Reports Server (NTRS)

Redemann, J.; Flynn, C. J.; Shinozuka, Y.; Kacenelenbogen, M.; Segal-Rosenheimer, M.; LeBlanc, S.; Russell, P. B.; Livingston, J. M.; Schmid, B.; Dunagan, S. E.;

Derivative spectra matching for wetland vegetation identification and classification by hyperspectral image

NASA Astrophysics Data System (ADS)

Wang, Jinnian; Zheng, Lanfen; Tong, Qingxi

1998-08-01

In this paper, we reported some research result in applying hyperspectral remote sensing data in identification and classification of wetland plant species and associations. Hyperspectral data were acquired by Modular Airborne Imaging Spectrometer (MAIS) over Poyang Lake wetland, China. A derivative spectral matching algorithm was used in hyperspectral vegetation analysis. The field measurement spectra were as reference for derivative spectral matching. In the study area, seven wetland plant associations were identified and classified with overall average accuracy is 84.03%.

HYPERSPECTRAL CHANNEL SELECTION FOR WATER QUALITY MONITORING ON THE GREAT MIAMI RIVER, OHIO

EPA Science Inventory

During the summer of 1999, spectral data were collected with a hand-held spectroradiometer, a laboratory spectrometer and airborne hyperspectral sensors from the Great Miami River (GMR), Ohio. Approximately 80 km of the GMR were imaged during a flyover with a Compact Airborne Sp...

Hyperspectral Technique for Detecting Soil Parameters

NASA Astrophysics Data System (ADS)

Garfagnoli, F.; Ciampalini, A.; Moretti, S.; Chiarantini, L.

2011-12-01

In satellite and airborne remote sensing, hyperspectral technique has become a very powerful tool, due to the possibility of rapidly realizing chemical/mineralogical maps of the studied areas. Many studies are trying to customize the algorithms to identify several geo-physical soil properties. The specific objective of this study is to investigate those soil characteristics, such as clay mineral content, influencing degradation processes (soil erosion and shallow landslides), by means of correlation analysis, in order to examine the possibility of predicting the selected property using high-resolution reflectance spectra and images. The study area is located in the Mugello basin, about 30 km north of Firenze (Tuscany, Italy). Agriculturally suitable terrains are assigned mainly to annual crops, marginally to olive groves, vineyards and orchards. Soils mostly belong to Regosols and Cambisols orders. An ASD FieldSpec spectroradiometer was used to obtain reflectance spectra from about 80 dried, crushed and sieved samples under controlled laboratory conditions. Samples were collected simultaneously with the flight of SIM.GA hyperspectral camera from Selex Galileo, over an area of about 5 km2 and their positions were recorded with a differential GPS. The quantitative determination of clay minerals content was performed by means of XRD and Rietveld refinement. Different chemometric techniques were preliminarily tested to correlate mineralogical records with reflectance data. A one component partial least squares regression model yielded a preliminary R2 value of 0.65. A slightly better result was achieved by plotting the absorption peak depth at 2210 versus total clay content (band-depth analysis). The complete SIM.GA hyperspectral geocoded row dataset, with an approximate pixel resolution of 0.6 m (VNIR) and 1.2 m (SWIR), was firstly transformed into at sensor radiance values, by applying calibration coefficients and parameters from laboratory measurements to non

Quality evaluation of pansharpened hyperspectral images generated using multispectral images

NASA Astrophysics Data System (ADS)

Matsuoka, Masayuki; Yoshioka, Hiroki

2012-11-01

Hyperspectral remote sensing can provide a smooth spectral curve of a target by using a set of higher spectral resolution detectors. The spatial resolution of the hyperspectral images, however, is generally much lower than that of multispectral images due to the lower energy of incident radiation. Pansharpening is an image-fusion technique that generates higher spatial resolution multispectral images by combining lower resolution multispectral images with higher resolution panchromatic images. In this study, higher resolution hyperspectral images were generated by pansharpening of simulated lower hyperspectral and higher multispectral data. Spectral and spatial qualities of pansharpened images, then, were accessed in relation to the spectral bands of multispectral images. Airborne hyperspectral data of AVIRIS was used in this study, and it was pansharpened using six methods. Quantitative evaluations of pansharpened image are achieved using two frequently used indices, ERGAS, and the Q index.

Spring wheat-leaf phytomass and yield estimates from airborne scanner and hand-held radiometer measurements

NASA Technical Reports Server (NTRS)

Aase, J. K.; Siddoway, F. H.; Millard, J. P.

1984-01-01

An attempt has been made to relate hand-held radiometer measurements, and airborne multispectral scanner readings, with both different wheat stand densities and grain yield. Aircraft overflights were conducted during the tillering, stem extension and heading period stages of growth, while hand-held radiometer readings were taken throughout the growing season. The near-IR/red ratio was used in the analysis, which indicated that both the aircraft and the ground measurements made possible a differentiation and evaluation of wheat stand densities at an early enough growth stage to serve as the basis of management decisions. The aircraft data also corroborated the hand-held radiometer measurements with respect to yield prediction. Winterkill was readily evaluated.

Simulation of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Richsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

2004-01-01

A software package generates simulated hyperspectral imagery for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport, as well as reflections from surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, "ground truth" is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces, as well as the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for, and a supplement to, field validation data.

Developing a Soil Moisture Index for California Grasslands from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Flamme, H. E.; Roberts, D. A.; Miller, D. L.

2016-12-01

Soil moisture is a key environmental factor controlling vegetation diversity and productivity, evaporation, transpiration, and rainfall runoff. Despite the contribution of soil moisture to ecological productivity, the hydrologic cycle, and erosion, it is currently not being monitored as accurately or as frequently as other environmental factors. Traditional soil moisture monitoring techniques rely on in situ measurements, which become costly when evaluating areas of unevenly distributed soil characteristics and varying topography. Alternatively, satellite remote sensing, such as passive microwave from SMAP, can provide soil moisture but only at very coarse spatial resolutions. Imagery from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) has the potential to allow better spatial and temporal monitoring of soil moisture. This study established a relationship between plant available water and hyperspectral reflectance via linear regressions of data from 2013-2015 for two grassland field sites: 1) near Santa Barbara, California, at Coal Oil Point Reserve (COPR) and 2) Airstrip station (AIRS) at UC Santa Barbara's Sedgwick Reserve near Santa Ynez, California. Volumetric soil moisture measurements at 10 cm and 20 cm depths were provided by meteorological stations situated in COPR and AIRS while reflectance data were extracted from AVIRIS. We found strong correlations between plant available water and bands centered at wavelengths 704 nm and 831 nm, which we used to create Hyperspectral Soil Moisture Index (HSMI): 0.38((ρ831-ρ704)/(ρ831+ρ704))-0.02. HSMI demonstrated a coefficient of determination (R2) of 0.71 for linear regressions of reflectance versus plant available water with a lag time of 28 days. We applied HSMI to the AIRS and COPR grasslands for 2011 AVIRIS scenes. Plant available water values predicted by HSMI were 0.039 higher at AIRS and 0.048 higher at COPR than the field measurements at the sites. Differences in grass species, soil

Detecting subtle environmental change: a multi-temporal airborne imaging spectroscopy approach

NASA Astrophysics Data System (ADS)

Yule, Ian J.; Pullanagari, Reddy R.; Kereszturi, G.

2016-10-01

Airborne and satellite hyperspectral remote sensing is a key technology to observe finite change in ecosystems and environments. The role of such sensors will improve our ability to monitor and mitigate natural and agricultural environments on a much larger spatial scale than can be achieved using field measurements such as soil coring or proximal sensors to estimate the chemistry of vegetation. Hyperspectral sensors for commentarial and scientific activities are increasingly available and cost effective, providing a great opportunity to measure and detect changes in the environment and ecosystem. This can be used to extract critical information to develop more advanced management practices. In this research, we provide an overview of the data acquisition, processing and analysis of airborne, full-spectrum hyperspectral imagery from a small-scale aerial mapping project in hill-country farms in New Zealand, using an AISA Fenix sensor (Specim, Finland). The imagery has been radiometrically and atmospherically corrected, georectified and mosaicked. The hyperspectral data cube was then spectrally and spatially smoothed using Savitzky-Golay and median filter, respectively. The mosaicked imagery used to calculate bio-chemical properties of surface vegetation, such as pasture. Ground samples (n = 200) were collected a few days after the over-flight are used to develop a calibration model using partial least squares regression method. In-leaf nitrogen, potassium and phosphorous concentration were calculated using the reflectance values from the airborne hyperspectral imagery. In total, three surveys of an example property have been acquired that show changes in the pattern of availability of a major element in vegetation canopy, in this case nitrogen.

Hyperspectral image compressing using wavelet-based method

NASA Astrophysics Data System (ADS)

Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng

2017-10-01

Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

USGS Publications Warehouse

Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

2011-01-01

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

NASA Astrophysics Data System (ADS)

Kandare, Kaja; Ørka, Hans Ole; Dalponte, Michele; Næsset, Erik; Gobakken, Terje

2017-08-01

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m2 plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI

Hyperspectral remote sensing of postfire soil properties

Treesearch

Sarah A. Lewis; Peter R. Robichaud; William J. Elliot; Bruce E. Frazier; Joan Q. Wu

2004-01-01

Forest fires may induce changes in soil organic properties that often lead to water repellent conditions within the soil profile that decrease soil infiltration capacity. The remote detection of water repellent soils after forest fires would lead to quicker and more accurate assessment of erosion potential. An airborne hyperspectral image was acquired over the Hayman...

Detection of abandoned mines/caves using airborne LWIR hyperspectral data

NASA Astrophysics Data System (ADS)

Shen, Sylvia S.; Roettiger, Kurt A.

2012-09-01

The detection of underground structures, both natural and man-made, continues to be an important requirement in both the military/intelligence and civil communities. There are estimates that as many as 70,000 abandoned mines/caves exist across the nation. These mines represent significant hazards to public health and safety, and they are of concern to Government agencies at the local, state, and federal levels. NASA is interested in the detection of caves on Mars and the Moon in anticipation of future manned space missions. And, the military/ intelligence community is interested in detecting caves, mines, and other underground structures that may be used to conceal the production of weapons of mass destruction or to harbor insurgents or other persons of interest by the terrorists. Locating these mines/caves scattered over millions of square miles is an enormous task, and limited resources necessitate the development of an efficient and effective broad area search strategy using remote sensing technologies. This paper describes an internally-funded research project of The Aerospace Corporation (Aerospace) to assess the feasibility of using airborne hyperspectral data to detect abandoned cave/mine entrances in a broad-area search application. In this research, we have demonstrated the potential utility of using thermal contrast between the cave/mine entrance and the ambient environment as a discriminatory signature. We have also demonstrated the use of a water vapor absorption line at12.55 μm and a quartz absorption feature at 9.25 μm as discriminatory signatures. Further work is required to assess the broader applicability of these signatures.

Airborne multispectral data collection

NASA Technical Reports Server (NTRS)

Hasell, P. G., Jr.

1974-01-01

Multispectral mapping accomplishments using the M7 airborne scanner are summarized. The M7 system is described and overall results of specific data collection flight operations since June 1971 are reviewed. A major advantage of the M7 system is that all spectral bands of the scanner are in common spatial registration, whereas in the M5 they were not.

Hyperspectral imaging utility for transportation systems

NASA Astrophysics Data System (ADS)

Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver

2015-03-01

The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.

Data products of NASA Goddard's LiDAR, hyperspectral, and thermal airborne imager (G-LiHT)

NASA Astrophysics Data System (ADS)

Corp, Lawrence A.; Cook, Bruce D.; McCorkel, Joel; Middleton, Elizabeth M.

2015-06-01

Scientists in the Biospheric Sciences Laboratory at NASA's Goddard Space Flight Center have undertaken a unique instrument fusion effort for an airborne package that integrates commercial off the shelf LiDAR, Hyperspectral, and Thermal components. G-LiHT is a compact, lightweight and portable system that can be used on a wide range of airborne platforms to support a number of NASA Earth Science research projects and space-based missions. G-LiHT permits simultaneous and complementary measurements of surface reflectance, vegetation structure, and temperature, which provide an analytical framework for the development of new algorithms for mapping plant species composition, plant functional types, biodiversity, biomass, carbon stocks, and plant growth. G-LiHT and its supporting database are designed to give scientists open access to the data that are needed to understand the relationship between ecosystem form and function and to stimulate the advancement of synergistic algorithms. This system will enhance our ability to design new missions and produce data products related to biodiversity and climate change. G-LiHT has been operational since 2011 and has been used to collect data for a number of NASA and USFS sponsored studies, including NASA's Carbon Monitoring System (CMS) and the American ICESat/GLAS Assessment of Carbon (AMIGA-Carb). These acquisitions target a broad diversity of forest communities and ecoregions across the United States and Mexico. Here, we will discuss the components of G-LiHT, their calibration and performance characteristics, operational implementation, and data processing workflows. We will also provide examples of higher level data products that are currently available.

Airborne hyperspectral sensor radiometric self-calibration using near-infrared properties of deep water and vegetation

NASA Astrophysics Data System (ADS)

Barbieux, Kévin; Nouchi, Vincent; Merminod, Bertrand

2016-10-01

Retrieving the water-leaving reflectance from airborne hyperspectral data implies to deal with three steps. Firstly, the radiance recorded by an airborne sensor comes from several sources: the real radiance of the object, the atmospheric scattering, sky and sun glint and the dark current of the sensor. Secondly, the dispersive element inside the sensor (usually a diffraction grating or a prism) could move during the flight, thus shifting the observed spectra on the wavelengths axis. Thirdly, to compute the reflectance, it is necessary to estimate, for each band, what value of irradiance corresponds to a 100% reflectance. We present here our calibration method, relying on the absorption features of the atmosphere and the near-infrared properties of common materials. By choosing proper flight height and flight lines angle, we can ignore atmospheric and sun glint contributions. Autocorrelation plots allow to identify and reduce the noise in our signals. Then, we compute a signal that represents the high frequencies of the spectrum, to localize the atmospheric absorption peaks (mainly the dioxygen peak around 760 nm). Matching these peaks removes the shift induced by the moving dispersive element. Finally, we use the signal collected over a Lambertian, unit-reflectance surface to estimate the ratio of the system's transmittances to its near-infrared transmittance. This transmittance is computed assuming an average 50% reflectance of the vegetation and nearly 0% for water in the near-infrared. Results show great correlation between the output spectra and ground measurements from a TriOS Ramses and the water-insight WISP-3.

Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system: Example for the Jintanzi gold province, China

NASA Astrophysics Data System (ADS)

Liu, Lei; Feng, Jilu; Rivard, Benoit; Xu, Xinliang; Zhou, Jun; Han, Ling; Yang, Junlu; Ren, Guangli

2018-02-01

The Tiangong-1 Hyperspectral Imager (HSI) is a relatively new spaceborne hyperspectral remote sensing system that was launched by the Chinese government on September 29th 2011. The system has 64 shortwave infrared (SWIR) spectral bands (1000-2500 nm) and imagery is at a spatial resolution of 20 m. This study represents an evaluation of Tiangong-1 data for the production of alteration mineral maps. Alteration mineral maps resulting from the analysis of Tiangong-1 HSI data and airborne SASI (Shortwave infrared Airborne Spectrographic Imager) data are compared for the Jintanzi area, Beishan, Gansu province, northwest China where gold bearing veins are documented. The results illustrate the detection of muscovite, kaolinite, chlorite, epidote, calcite and dolomite from Tiangong-1 HSI data and most anomalies seen in the airborne SASI data are captured. The Tiangong-1 data appears to be well suited for the detection of surface mineralogy in support of regional mapping and exploration. The data complements that which will be offered by the Chinese GF-5 Hyperspectral Imager and the German EnMAP system, both scheduled for launch in 2018.

Shift-variant linear system modeling for multispectral scanners

NASA Astrophysics Data System (ADS)

Amini, Abolfazl M.; Ioup, George E.; Ioup, Juliette W.

1995-07-01

Multispectral scanner data are affected both by the spatial impulse response of the sensor and the spectral response of each channel. To achieve a realistic representation for the output data for a given scene spectral input, both of these effects must be incorporated into a forward model. Each channel can have a different spatial response and each has its characteristic spectral response. A forward model is built which includes the shift invariant spatial broadening of the input for the channels and the shift variant spectral response across channels. The model is applied to the calibrated airborne multispectral scanner as well as the airborne terrestrial applications sensor developed at NASA Stennis Space Center.

Hyperresolution: an hyperspectral and high resolution imager for Earth observation

NASA Astrophysics Data System (ADS)

De Vidi, R.; Chiarantini, L.; Bini, A.

2017-11-01

Hyperspectral space imagery is an emerging technology that supports many scientific, civil, security and defence operational applications. The main advantage of this remote sensing technique is that it allows the so-called Feature Extraction: in fact the spectral signature allows the recognition of the materials composing the scene. Hyperspectral Products and their applications have been investigated in the past years by Galileo Avionica to direct the instrument characteristics design. Sample products have been identified in the civil / environment monitoring fields (such as coastal monitoring, vegetation, hot spot and urban classification) and in defense / security applications: their performances have been verified by means of airborne flight campaigns. The Hyperspectral and High Resolution Imager is a space-borne instrument that implement a pushbroom technique to get strip spectral images over the Hyperspectral VNIR and SWIR bands, with a ground sample distance at nadir of 20m in a 20 km wide ground swath, with 200 spectral channels, realizing an average spectral resolution of 10nm. The High Resolution Panchromatic Channel insists in the same swath to allow for multiresolution data fusion of hyperspectral imagery.

Miniature infrared hyperspectral imaging sensor for airborne applications

NASA Astrophysics Data System (ADS)

Hinnrichs, Michele; Hinnrichs, Bradford; McCutchen, Earl

2017-05-01

Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera, both MWIR and LWIR, small enough to serve as a payload on a miniature unmanned aerial vehicles. The optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of the sensor. This new and innovative approach to infrared hyperspectral imaging spectrometer uses micro-optics and will be explained in this paper. The micro-optics are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a mini-UAV or commercial quadcopter. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the spatial resolution. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4 lenslet array on a 1024 x 1024 pixel element focal plane array which gives 16 spectral images of 256 x 256 pixel resolution each

Parallel hyperspectral compressive sensing method on GPU

NASA Astrophysics Data System (ADS)

Bernabé, Sergio; Martín, Gabriel; Nascimento, José M. P.

2015-10-01

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

GPU Lossless Hyperspectral Data Compression System

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh I.; Keymeulen, Didier; Kiely, Aaron B.; Klimesh, Matthew A.

2014-01-01

Hyperspectral imaging systems onboard aircraft or spacecraft can acquire large amounts of data, putting a strain on limited downlink and storage resources. Onboard data compression can mitigate this problem but may require a system capable of a high throughput. In order to achieve a high throughput with a software compressor, a graphics processing unit (GPU) implementation of a compressor was developed targeting the current state-of-the-art GPUs from NVIDIA(R). The implementation is based on the fast lossless (FL) compression algorithm reported in "Fast Lossless Compression of Multispectral-Image Data" (NPO- 42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which operates on hyperspectral data and achieves excellent compression performance while having low complexity. The FL compressor uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. The new Consultative Committee for Space Data Systems (CCSDS) Standard for Lossless Multispectral & Hyperspectral image compression (CCSDS 123) is based on the FL compressor. The software makes use of the highly-parallel processing capability of GPUs to achieve a throughput at least six times higher than that of a software implementation running on a single-core CPU. This implementation provides a practical real-time solution for compression of data from airborne hyperspectral instruments.

Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

NASA Astrophysics Data System (ADS)

Krause, Keith Stuart

The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

A system to geometrically rectify and map airborne scanner imagery and to estimate ground area. [by computer

NASA Technical Reports Server (NTRS)

Spencer, M. M.; Wolf, J. M.; Schall, M. A.

1974-01-01

A system of computer programs were developed which performs geometric rectification and line-by-line mapping of airborne multispectral scanner data to ground coordinates and estimates ground area. The system requires aircraft attitude and positional information furnished by ancillary aircraft equipment, as well as ground control points. The geometric correction and mapping procedure locates the scan lines, or the pixels on each line, in terms of map grid coordinates. The area estimation procedure gives ground area for each pixel or for a predesignated parcel specified in map grid coordinates. The results of exercising the system with simulated data showed the uncorrected video and corrected imagery and produced area estimates accurate to better than 99.7%.

Littoral assessment of mine burial signatures (LAMBS): buried-landmine hyperspectral data collections

NASA Astrophysics Data System (ADS)

Kenton, Arthur C.; Geci, Duane M.; McDonald, James A.; Ray, Kristofer J.; Thomas, Clayton M.; Holloway, John H., Jr.; Petee, Danny A.; Witherspoon, Ned H.

2003-09-01

The objective of the Office of Naval Research (ONR) Rapid Overt Reconnaissance (ROR) program and the Airborne Littoral Reconnaissance Technologies project's Littoral Assessment of Mine Burial Signatures (LAMBS) contract is to determine if electro-optical spectral discriminants exist that are useful for the detection of land mines located in littoral regions. Statistically significant buried mine overburden and background signature data were collected over a wide spectral range (0.35 to 14 μm) to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. The LAMBS program further expands the hyperspectral database previously collected and analyzed on the U.S. Army's Hyperspectral Mine Detection Phenomenology program [see "Detection of Land Mines with Hyperspectral Data," and "Hyperspectral Mine Detection Phenomenology Program," Proc. SPIE Vol. 3710, pp 917-928 and 819-829, AeroSense April 1999] to littoral areas where tidal, surf, and wind action can additionally modify spectral signatures. This work summarizes the LAMBS buried mine collections conducted at three beach sites - an inland bay beach site (Eglin AFB, FL, Site A-22), an Atlantic beach site (Duck, NC), and a Gulf beach site (Eglin AFB, FL, Site A-15). Characteristics of the spectral signatures of the various dry and damp beach sands are presented. These are then compared to buried land mine signatures observed for the tested background types, burial ages, and environmental conditions experienced.

The influence of spectral and spatial resolution in classification approaches: Landsat TM data vs. Hyperspectral data

NASA Astrophysics Data System (ADS)

Rodríguez-Galiano, Víctor; Garcia-Soldado, Maria José; Chica-Olmo, Mario

The importance of accurate and timely information describing the nature and extent of land and natural resources is increasing especially in rapidly growing metropolitan areas. While metropolitan area decision makers are in constant need of current geospatial information on patterns and trends in land cover and land use, relatively little researchers has investigated the influence of the satellite data resolution for monitoring geo-enviromental information. In this research a suite of remote sensing and GIS techniques is applied in a land use mapping study. The main task is to asses the influence of the spatial and spectral resolution in the separability between classes and in the classificatiońs accuracy. This study has been focused in a very dynamical area with respect to land use, located in the province of Granada (SE of Spain). The classifications results of the Airborne Hyperspectral Scanner (AHS, Daedalus Enterprise Inc., WA, EEUU) at different spatial resolutions: 2, 4 and 6 m and Landsat 5 TM data have been compared.

Software for Simulation of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Richtsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

2002-01-01

A package of software generates simulated hyperspectral images for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport as well as surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, 'ground truth' is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces and the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for and a supplement to field validation data.

Linking goniometer measurements to hyperspectral and multisensor imagery for retrieval of beach properties and coastal characterization

NASA Astrophysics Data System (ADS)

Bachmann, Charles M.; Gray, Deric; Abelev, Andrei; Philpot, William; Montes, Marcos J.; Fusina, Robert; Musser, Joseph; Li, Rong-Rong; Vermillion, Michael; Smith, Geoffrey; Korwan, Daniel; Snow, Charlotte; Miller, W. David; Gardner, Joan; Sletten, Mark; Georgiev, Georgi; Truitt, Barry; Killmon, Marcus; Sellars, Jon; Woolard, Jason; Parrish, Christopher; Schwarzscild, Art

2012-06-01

In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements. Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500), topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR'11). Focus areas for VCR'11 were properties of beaches and tidal flats and barrier island vegetation and, in the water column, shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density, moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007 related to beach properties and shallow water bathymetry. A priority for VCR'11 was to collect and model relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery, as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR'11.

Mapping Geology and Vegetation using Hyperspectral Data in Antarctica: Current Challenges, New Solutions and Looking to the Future

NASA Astrophysics Data System (ADS)

Black, M.; Riley, T. R.; Fleming, A. H.; Ferrier, G.; Fretwell, P.; Casanovas, P.

2015-12-01

Antarctica is a unique and geographically remote environment. Traditional field campaigns investigating geology and vegetation in the region encounter numerous challenges including the harsh polar climate, the invasive nature of the work, steep topography and high infrastructure costs. Additionally, such field campaigns are often limited in terms of spatial and temporal resolution, and particularly, the topographical challenges presented in the Antarctic mean that many areas remain inaccessible. Remote Sensing, particularly hyperspectral imaging, may provide a solution to overcome the difficulties associated with field based mapping in the Antarctic. Planned satellite launches, such as EnMAP and HyspIRI, if successful, will yield large-scale, repeated hyperspectral imagery of Antarctica. Hyperspectral imagery has proven mapping capabilities and can yield greater information than can be attained using multispectral data. As a precursor to future satellite imagery, we utilise hyperspectral imagery from the first known airborne hyperspectral survey carried out in the Antarctic by the British Antarctic Survey and partners in 2011. Multiple imaging spectrometers were simultaneously deployed covering the visible, shortwave and thermal infrared regions of the electromagnetic spectrum. Additional data was generated during a field campaign deploying multiple ground spectrometers covering the same wavelengths as the airborne imagers. We utilise this imagery to assess the current challenges and propose some new solutions for mapping vegetation and geology, which may be directly applicable to future satellite hyperspectral imagery in the Antarctic.

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

PubMed Central

Li, Na; Xu, Zhaopeng; Zhao, Huijie; Huang, Xinchen; Drummond, Jane; Wang, Daming

2018-01-01

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively. PMID:29510547

Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests

Treesearch

Kimberly M. Carlson; Gregory P. Asner; R. Flint Hughes; Rebecca Ostertag; Roberta E. Martin

2007-01-01

Mapping biological diversity is a high priority for conservation research, management and policy development, but few studies have provided diversity data at high spatial resolution from remote sensing. We used airborne imaging spectroscopy to map woody vascular plant species richness in lowland tropical forest ecosystems in Hawaii. Hyperspectral signatures spanning...

Hyperspectral Image Classification using a Self-Organizing Map

NASA Technical Reports Server (NTRS)

Martinez, P.; Gualtieri, J. A.; Aguilar, P. L.; Perez, R. M.; Linaje, M.; Preciado, J. C.; Plaza, A.

2001-01-01

The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.

Classification of High Spatial Resolution, Hyperspectral ...

EPA Pesticide Factsheets

EPA announced the availability of the final report,Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

Hyperspectral target detection analysis of a cluttered scene from a virtual airborne sensor platform using MuSES

NASA Astrophysics Data System (ADS)

Packard, Corey D.; Viola, Timothy S.; Klein, Mark D.

2017-10-01

The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been

Integration of a synthetic vision system with airborne laser range scanner-based terrain referenced navigation for precision approach guidance

NASA Astrophysics Data System (ADS)

Uijt de Haag, Maarten; Campbell, Jacob; van Graas, Frank

2005-05-01

Synthetic Vision Systems (SVS) provide pilots with a virtual visual depiction of the external environment. When using SVS for aircraft precision approach guidance systems accurate positioning relative to the runway with a high level of integrity is required. Precision approach guidance systems in use today require ground-based electronic navigation components with at least one installation at each airport, and in many cases multiple installations to service approaches to all qualifying runways. A terrain-referenced approach guidance system is envisioned to provide precision guidance to an aircraft without the use of ground-based electronic navigation components installed at the airport. This autonomy makes it a good candidate for integration with an SVS. At the Ohio University Avionics Engineering Center (AEC), work has been underway in the development of such a terrain referenced navigation system. When used in conjunction with an Inertial Measurement Unit (IMU) and a high accuracy/resolution terrain database, this terrain referenced navigation system can provide navigation and guidance information to the pilot on a SVS or conventional instruments. The terrain referenced navigation system, under development at AEC, operates on similar principles as other terrain navigation systems: a ground sensing sensor (in this case an airborne laser scanner) gathers range measurements to the terrain; this data is then matched in some fashion with an onboard terrain database to find the most likely position solution and used to update an inertial sensor-based navigator. AEC's system design differs from today's common terrain navigators in its use of a high resolution terrain database (~1 meter post spacing) in conjunction with an airborne laser scanner which is capable of providing tens of thousands independent terrain elevation measurements per second with centimeter-level accuracies. When combined with data from an inertial navigator the high resolution terrain database and

Hyperspectral data collection for the assessment of target detection algorithms: the Viareggio 2013 trial

NASA Astrophysics Data System (ADS)

Rossi, Alessandro; Acito, Nicola; Diani, Marco; Corsini, Giovanni; De Ceglie, Sergio Ugo; Riccobono, Aldo; Chiarantini, Leandro

2014-10-01

Airborne hyperspectral imagery is valuable for military and civilian applications, such as target identification, detection of anomalies and changes within multiple acquisitions. In target detection (TD) applications, the performance assessment of different algorithms is an important and critical issue. In this context, the small number of public available hyperspectral data motivated us to perform an extensive measurement campaign including various operating scenarios. The campaign was organized by CISAM in cooperation with University of Pisa, Selex ES and CSSN-ITE, and it was conducted in Viareggio, Italy in May, 2013. The Selex ES airborne hyperspectral sensor SIM.GA was mounted on board of an airplane to collect images over different sites in the morning and afternoon of two subsequent days. This paper describes the hyperspectral data collection of the trial. Four different sites were set up, representing a complex urban scenario, two parking lots and a rural area. Targets with dimensions comparable to the sensor ground resolution were deployed in the sites to reproduce different operating situations. An extensive ground truth documentation completes the data collection. Experiments to test anomalous change detection techniques were set up changing the position of the deployed targets. Search and rescue scenarios were simulated to evaluate the performance of anomaly detection algorithms. Moreover, the reflectance signatures of the targets were measured on the ground to perform spectral matching in varying atmospheric and illumination conditions. The paper presents some preliminary results that show the effectiveness of hyperspectral data exploitation for the object detection tasks of interest in this work.

Assessment of target detection limits in hyperspectral data

NASA Astrophysics Data System (ADS)

Gross, W.; Boehler, J.; Schilling, H.; Middelmann, W.; Weyermann, J.; Wellig, P.; Oechslin, R.; Kneubuehler, M.

2015-10-01

Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband sensors. The comparably low spatial resolution is compensated by the fact that small targets, even below image resolution, can still be classified. The goal of this paper is to determine the target size to spatial resolution ratio for successful classification of different target and background materials. Airborne hyperspectral data is used to simulate data with known mixture ratios and to estimate the detection threshold for given false alarm rates. The data was collected in July 2014 over Greding, Germany, using airborne aisaEAGLE and aisaHAWK hyperspectral sensors. On the ground, various target materials were placed on natural background. The targets were four quadratic molton patches with an edge length of 7 meters in the colors black, white, grey and green. Also, two different types of polyethylene (camouflage nets) with an edge length of approximately 5.5 meters were deployed. Synthetic data is generated from the original data using spectral mixtures. Target signatures are linearly combined with different background materials in specific ratios. The simulated mixtures are appended to the original data and the target areas are removed for evaluation. Commonly used classification algorithms, e.g. Matched Filtering, Adaptive Cosine Estimator are used to determine the detection limit. Fixed false alarm rates are employed to find and analyze certain regions where false alarms usually occur first. A combination of 18 targets and 12 backgrounds is analyzed for three VNIR and two SWIR data sets of the same area.

Hyperspectral Data Processing and Mapping of Soil Parameters: Preliminary Data from Tuscany (Italy)

NASA Astrophysics Data System (ADS)

Garfagnoli, F.; Moretti, S.; Catani, F.; Innocenti, L.; Chiarantini, L.

2010-12-01

Hyperspectral imaging has become a very powerful remote sensing tool for its capability of performing chemical and physical analysis of the observed areas. The objective of this study is to retrieve and characterize clay mineral content of the cultivated layer of soils, from both airborne hyperspectral and field spectrometry surveys in the 400-2500 nm spectral range. Correlation analysis is used to examine the possibility to predict the selected property using high-resolution reflectance spectra and images. The study area is located in the Mugello basin, about 30 km north of Firenze (Tuscany, Italy). Agriculturally suitable terrains are assigned mainly to annual crops, marginally to olive groves, vineyards and orchards. Soils mostly belong to Regosols and Cambisols orders. About 80 topsoil samples scattered all over the area were collected simultaneously with the flight of SIM.GA hyperspectral camera from Selex Galileo. The quantitative determination of clay minerals content in soil samples was performed by means of XRD and Rietveld refinement. An ASD FieldSpec spectroradiometer was used to obtain reflectance spectra from dried, crushed and sieved samples under controlled laboratory conditions. Different chemometric techniques (multiple linear regression, vertex component analysis, partial least squares regression and band depth analysis) were preliminarily tested to correlate mineralogical records with reflectance data. A one component partial least squares regression model yielded a preliminary R2 value of 0.65. A similar result was achieved by plotting the absorption peak depth at 2210 versus total clay mineral content (band-depth analysis). A complete hyperspectral geocoded reflectance dataset was collected using SIM.GA hyperspectral image sensor from Selex-Galileo, mounted on board of the University of Firenze ultra light aircraft. The approximate pixel resolution was 0.6 m (VNIR) and 1.2 m (SWIR). Airborne SIM.GA row data were firstly transformed into at

Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction

NASA Astrophysics Data System (ADS)

Gross, W.; Boehler, J.; Twizer, K.; Kedem, B.; Lenz, A.; Kneubuehler, M.; Wellig, P.; Oechslin, R.; Schilling, H.; Rotman, S.; Middelmann, W.

2016-10-01

Hyperspectral remote sensing data can be used for civil and military applications to robustly detect and classify target objects. High spectral resolution of hyperspectral data can compensate for the comparatively low spatial resolution, which allows for detection and classification of small targets, even below image resolution. Hyperspectral data sets are prone to considerable spectral redundancy, affecting and limiting data processing and algorithm performance. As a consequence, data reduction strategies become increasingly important, especially in view of near-real-time data analysis. The goal of this paper is to analyze different strategies for hyperspectral band selection algorithms and their effect on subpixel classification for different target and background materials. Airborne hyperspectral data is used in combination with linear target simulation procedures to create a representative amount of target-to-background ratios for evaluation of detection limits. Data from two different airborne hyperspectral sensors, AISA Eagle and Hawk, are used to evaluate transferability of band selection when using different sensors. The same target objects were recorded to compare the calculated detection limits. To determine subpixel classification results, pure pixels from the target materials are extracted and used to simulate mixed pixels with selected background materials. Target signatures are linearly combined with different background materials in varying ratios. The commonly used classification algorithms Adaptive Coherence Estimator (ACE) is used to compare the detection limit for the original data with several band selection and data reduction strategies. The evaluation of the classification results is done by assuming a fixed false alarm ratio and calculating the mean target-to-background ratio of correctly detected pixels. The results allow drawing conclusions about specific band combinations for certain target and background combinations. Additionally

Estimation of the fraction of absorbed photosynthetically active radiation (fPAR) in maize canopies using LiDAR data and hyperspectral imagery.

PubMed

Qin, Haiming; Wang, Cheng; Zhao, Kaiguang; Xi, Xiaohuan

2018-01-01

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) for maize canopies are important for maize growth monitoring and yield estimation. The goal of this study is to explore the potential of using airborne LiDAR and hyperspectral data to better estimate maize fPAR. This study focuses on estimating maize fPAR from (1) height and coverage metrics derived from airborne LiDAR point cloud data; (2) vegetation indices derived from hyperspectral imagery; and (3) a combination of these metrics. Pearson correlation analyses were conducted to evaluate the relationships among LiDAR metrics, hyperspectral metrics, and field-measured fPAR values. Then, multiple linear regression (MLR) models were developed using these metrics. Results showed that (1) LiDAR height and coverage metrics provided good explanatory power (i.e., R2 = 0.81); (2) hyperspectral vegetation indices provided moderate interpretability (i.e., R2 = 0.50); and (3) the combination of LiDAR metrics and hyperspectral metrics improved the LiDAR model (i.e., R2 = 0.88). These results indicate that LiDAR model seems to offer a reliable method for estimating maize fPAR at a high spatial resolution and it can be used for farmland management. Combining LiDAR and hyperspectral metrics led to better performance of maize fPAR estimation than LiDAR or hyperspectral metrics alone, which means that maize fPAR retrieval can benefit from the complementary nature of LiDAR-detected canopy structure characteristics and hyperspectral-captured vegetation spectral information.

COUPLING HYPERSPECTRAL REMOTE SENSING WITH FIELD SPECTROMETRY TO MONITOR INLAND WATER QUALITY PARAMETERS

EPA Science Inventory

Visible to near-infrared, airborne hyperspectral data were successfully used to estimate water quality parameters such as chlorophyll a, turbidity and total phosphorus from the Great Miami River, Ohio. During the summer of 1999, spectral data were collected with a hand-held fiel...

Geometric correction of synchronous scanned Operational Modular Imaging Spectrometer II hyperspectral remote sensing images using spatial positioning data of an inertial navigation system

NASA Astrophysics Data System (ADS)

Zhou, Xiaohu; Neubauer, Franz; Zhao, Dong; Xu, Shichao

2015-01-01

The high-precision geometric correction of airborne hyperspectral remote sensing image processing was a hard nut to crack, and conventional methods of remote sensing image processing by selecting ground control points to correct the images are not suitable in the correction process of airborne hyperspectral image. The optical scanning system of an inertial measurement unit combined with differential global positioning system (IMU/DGPS) is introduced to correct the synchronous scanned Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing images. Posture parameters, which were synchronized with the OMIS II, were first obtained from the IMU/DGPS. Second, coordinate conversion and flight attitude parameters' calculations were conducted. Third, according to the imaging principle of OMIS II, mathematical correction was applied and the corrected image pixels were resampled. Then, better image processing results were achieved.

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

NASA Astrophysics Data System (ADS)

Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

2013-05-01

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

Design study for Thermal Infrared Multispectral Scanner (TIMS)

NASA Technical Reports Server (NTRS)

Stanich, C. G.; Osterwisch, F. G.; Szeles, D. M.; Houtman, W. H.

1981-01-01

The feasibility of dividing the 8-12 micrometer thermal infrared wavelength region into six spectral bands by an airborne line scanner system was investigated. By combining an existing scanner design with a 6 band spectrometer, a system for the remote sensing of Earth resources was developed. The elements in the spectrometer include an off axis reflective collimator, a reflective diffraction grating, a triplet germanium imaging lens, a photoconductive mercury cadmium telluride sensor array, and the mechanical assembly to hold these parts and maintain their optical alignment across a broad temperature range. The existing scanner design was modified to accept the new spectrometer and two field filling thermal reference sources.

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2010-01-01

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Spatially explicit modelling of forest structure and function using airborne lidar and hyperspectral remote sensing data combined with micrometeorological measurements

NASA Astrophysics Data System (ADS)

Thomas, Valerie Anne

This research models canopy-scale photosynthesis at the Groundhog River Flux Site through the integration of high-resolution airborne remote sensing data and micrometeorological measurements collected from a flux tower. Light detection and ranging (lidar) data are analysed to derive models of tree structure, including: canopy height, basal area, crown closure, and average aboveground biomass. Lidar and hyperspectral remote sensing data are used to model canopy chlorophyll (Chl) and carotenoid concentrations (known to be good indicators of photosynthesis). The integration of lidar and hyperspectral data is applied to derive spatially explicit models of the fraction of photosynthetically active radiation (fPAR) absorbed by the canopy as well as a species classification for the site. These products are integrated with flux tower meteorological measurements (i.e., air temperature and global solar radiation) collected on a continuous basis over 2004 to apply the C-Fix model of carbon exchange to the site. Results demonstrate that high resolution lidar and lidar-hyperspectral integration techniques perform well in the boreal mixedwood environment. Lidar models are well correlated with forest structure, despite the complexities introduced in the mixedwood case (e.g., r2=0.84, 0.89, 0.60, and 0.91, for mean dominant height, basal area, crown closure, and average aboveground biomass). Strong relationships are also shown for canopy scale chlorophyll/carotenoid concentration analysis using integrated lidar-hyperspectral techniques (e.g., r2=0.84, 0.84, and 0.82 for Chl(a), Chl(a+b), and Chl(b)). Examination of the spatially explicit models of fPAR reveal distinct spatial patterns which become increasingly apparent throughout the season due to the variation in species groupings (and canopy chlorophyll concentration) within the 1 km radius surrounding the flux tower. Comparison of results from the modified local-scale version of the C-Fix model to tower gross ecosystem

Design of a concise Féry-prism hyperspectral imaging system based on multi-configuration

NASA Astrophysics Data System (ADS)

Dong, Wei; Nie, Yun-feng; Zhou, Jin-song

2013-08-01

In order to meet the needs of space borne and airborne hyperspectral imaging system for light weight, simplification and high spatial resolution, a novel design of Féry-prism hyperspectral imaging system based on Zemax multi-configuration method is presented. The novel structure is well arranged by analyzing optical monochromatic aberrations theoretically, and the optical structure of this design is concise. The fundamental of this design is Offner relay configuration, whereas the secondary mirror is replaced by Féry-prism with curved surfaces and a reflective front face. By reflection, the light beam passes through the Féry-prism twice, which promotes spectral resolution and enhances image quality at the same time. The result shows that the system can achieve light weight and simplification, compared to other hyperspectral imaging systems. Composed of merely two spherical mirrors and one achromatized Féry-prism to perform both dispersion and imaging functions, this structure is concise and compact. The average spectral resolution is 6.2nm; The MTFs for 0.45~1.00um spectral range are greater than 0.75, RMSs are less than 2.4um; The maximal smile is less than 10% pixel, while the keystones is less than 2.8% pixel; image quality approximates the diffraction limit. The design result shows that hyperspectral imaging system with one modified Féry-prism substituting the secondary mirror of Offner relay configuration is feasible from the perspective of both theory and practice, and possesses the merits of simple structure, convenient optical alignment, and good image quality, high resolution in space and spectra, adjustable dispersive nonlinearity. The system satisfies the requirements of airborne or space borne hyperspectral imaging system.

Hyperspectral Cubesat Constellation for Rapid Natural Hazard Response

NASA Astrophysics Data System (ADS)

Mandl, D.; Huemmrich, K. F.; Ly, V. T.; Handy, M.; Ong, L.; Crum, G.

2015-12-01

With the advent of high performance space networks that provide total coverage for Cubesats, the paradigm for low cost, high temporal coverage with hyperspectral instruments becomes more feasible. The combination of ground cloud computing resources, high performance with low power consumption onboard processing, total coverage for the cubesats and social media provide an opprotunity for an architecture that provides cost-effective hyperspectral data products for natural hazard response and decision support. This paper provides a series of pathfinder efforts to create a scalable Intelligent Payload Module(IPM) that has flown on a variety of airborne vehicles including Cessna airplanes, Citation jets and a helicopter and will fly on an Unmanned Aerial System (UAS) hexacopter to monitor natural phenomena. The IPM's developed thus far were developed on platforms that emulate a satellite environment which use real satellite flight software, real ground software. In addition, science processing software has been developed that perform hyperspectral processing onboard using various parallel processing techniques to enable creation of onboard hyperspectral data products while consuming low power. A cubesat design was developed that is low cost and that is scalable to larger consteallations and thus can provide daily hyperspectral observations for any spot on earth. The design was based on the existing IPM prototypes and metrics that were developed over the past few years and a shrunken IPM that can perform up to 800 Mbps throughput. Thus this constellation of hyperspectral cubesats could be constantly monitoring spectra with spectral angle mappers after Level 0, Level 1 Radiometric Correction, Atmospheric Correction processing. This provides the opportunity daily monitoring of any spot on earth on a daily basis at 30 meter resolution which is not available today.

Determining density of maize canopy. 2: Airborne multispectral scanner data

NASA Technical Reports Server (NTRS)

Stoner, E. R.; Baumgardner, M. F.; Cipra, J. E.

1971-01-01

Multispectral scanner data were collected in two flights over a light colored soil background cover plot at an altitude of 305 m. Energy in eleven reflective wavelength band from 0.45 to 2.6 microns was recorded. Four growth stages of maize (Zea mays L.) gave a wide range of canopy densities for each flight date. Leaf area index measurements were taken from the twelve subplots and were used as a measure of canopy density. Ratio techniques were used to relate uncalibrated scanner response to leaf area index. The ratios of scanner data values for the 0.72 to 0.92 micron wavelength band over the 0.61 to 0.70 micron wavelength band were calculated for each plot. The ratios related very well to leaf area index for a given flight date. The results indicated that spectral data from maize canopies could be of value in determining canopy density.

First hyperspectral survey of the deep seafloor: DISCOL area, Peru Basin

NASA Astrophysics Data System (ADS)

Dumke, Ines; Nornes, Stein M.; Ludvigsen, Martin

2017-04-01

Conventional hyperspectral seafloor surveys using airborne or satellite platforms are typically limited to shallow coastal areas. This limitation is due to the requirement for illumination by sunlight, which does not penetrate into deeper waters. For hyperspectral studies in deeper marine environments, such as the deep sea, a close-range, sunlight-independent survey approach is therefore required. Here, we present the first hyperspectral data from the deep seafloor. The data were acquired in 4200 m water depth in the DISCOL (disturbance-recolonization) area in the Peru Basin (SW Pacific). This area is characterized by seafloor manganese nodules and recolonization by benthic fauna after a seafloor disturbance experiment conducted in 1989, and was revisited in 2015 by the JPI Oceans cruise SO-242. The acquisition setup consisted of a new Underwater Hyperspectral Imager (UHI) mounted on a remotely operated vehicle (ROV), which provided illumination of the seafloor. High spatial and spectral resolution were achieved by an ROV altitude of 1 m and recording of 112 spectral bands between 380 nm and 800 nm (4 nm resolution). Spectral classification was performed to classify manganese nodules and benthic fauna and map their distribution in the study area. The results demonstrate the high potential of underwater hyperspectral imaging in mapping and classifying seafloor deposits and habitats.

Prediction of senescent rangeland canopy structural attributes with airborne hyperspectral imagery

USDA-ARS?s Scientific Manuscript database

Canopy structural and chemical data are needed for senescent, mixed-grass prairie landscapes in autumn, yet models driven by image data are lacking for rangelands dominated by non-photosynthetically active vegetation (NPV). Here, we report how aerial hyperspectral imagery might be modeled to predic...

Hyperspectral analysis of columbia spotted frog habitat

USGS Publications Warehouse

Shive, J.P.; Pilliod, D.S.; Peterson, C.R.

2010-01-01

Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96 (44 wetlands) correctly classified with the 2002 data and 89 (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74 (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72 (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats. ?? 2010 The Wildlife Society.

Phenotyping Drought Tolerance and Yield Potential of Warm-Season Legumes Through Field- and Airborne-Based Hyperspectral VSWIR Sensing

NASA Astrophysics Data System (ADS)

Drewry, D.; Berny-Mier y Teran, J. C.; Dutta, D.; Gepts, P.

2017-12-01

Hyperspectral sensing in the visible through shortwave infrared (VSWIR) portion of the spectrum has been demonstrated to provide significant information on the structural and functional properties of vegetation, resulting in powerful techniques to discern species differences, characterize crop nutrient or water stress, and quantify the density of foliage in agricultural fields. Modern machine-learning techniques allow for the entire set of spectral bands, on the order of hundreds with modern field and airborne spectrometers, to be used to develop models that can simultaneously retrieve a variety of foliar chemical compounds and hydrological and structural states. The application of these techniques, in the context of leaf-level measurements of VSWIR reflectance, or more complicated remote airborne surveys, has the potential to revolutionize high-throughput methods to phenotype germplasm that optimizes yield, resource-use efficiencies, or alternate objectives related to disease resistance or biomass accumulation, for example. Here we focus on breeding trials for a set of warm-season legumes, conducted in both greenhouse and field settings, and spanning a set of diverse genotypes providing a range of adaptation to drought and yield potential in the context of the semi-arid climate cultivation. At the leaf-level, a large set of spectral reflectance measurements spanning 400-2500 nanometers were made for plants across various growth stages in field experiments that induced severe drought, along with sampling for relevant trait values. Here we will discuss the development and performance of algorithms for a range of leaf traits related to gas exchange, leaf structure, hydrological status, nutrient contents and stable isotope discrimination, along with their relationships to drought resistance and yield. We likewise discuss the effectiveness of quantifying relevant foliar and canopy traits through airborne imaging spectroscopy from small unmanned vehicles (sUAVs), and

A New Hyperspectral Designed for Small UAS Tested in Real World Applications

NASA Astrophysics Data System (ADS)

Marcucci, E.; Saiet, E., II; Hatfield, M. C.

2014-12-01

The ability to investigate landscape and vegetation from airborne instruments offers many advantages, including high resolution data, ability to deploy instruments over a specific area, and repeat measurements. The Alaska Center for Unmanned Aircraft Systems Integration (ACUASI) has recently integrated a hyperspectral imaging camera onto their Ptarmigan hexacopter. The Rikola Hyperspectral Camera manufactured by VTT and Rikola, Ltd. is capable of obtaining data within the 400-950 nm range with an accuracy of ~1 nm. Using the compact flash on the UAV limits the maximum number of channels to 24 this summer. The camera uses a single frame to sequentially record the spectral bands of interest in a 37° field-of-view. Because the camera collects data as single frames it takes a finite amount of time to compile the complete spectral. Although each frame takes only 5 nanoseconds, co-registration of frames is still required. The hovering ability of the hexacopter helps eliminate frame shift. GPS records data for incorporation into a larger dataset. Conservatively, the Ptarmigan can fly at an altitude of 400 feet, for 15 minutes, and 7000 feet away from the operator. The airborne hyperspectral instrument will be extremely useful to scientists as a platform that can provide data on-request. Since the spectral range of the camera is ideal for the study of vegetation, this study 1) examines seasonal changes of vegetation of the Fairbanks area, 2) ground-truths satellite measurements, and 3) ties vegetation conditions around a weather tower to the tower readings. Through this proof of concept, ACUASI provides a means for scientists to request the most up-to-date and location-specific data for their field sites. Additionally, the resolution of the airborne instruments is much higher than that of satellite data, these may be readily tasked, and they have the advantage over manned flights in terms of manpower and cost.

Hyperspectral Imager for the Coastal Ocean: instrument description and first images.

PubMed

Lucke, Robert L; Corson, Michael; McGlothlin, Norman R; Butcher, Steve D; Wood, Daniel L; Korwan, Daniel R; Li, Rong R; Snyder, Willliam A; Davis, Curt O; Chen, Davidson T

2011-04-10

The Hyperspectral Imager for the Coastal Ocean (HICO) is the first spaceborne hyperspectral sensor designed specifically for the coastal ocean and estuarial, riverine, or other shallow-water areas. The HICO generates hyperspectral images, primarily over the 400-900 nm spectral range, with a ground sample distance of ≈90 m (at nadir) and a high signal-to-noise ratio. The HICO is now operating on the International Space Station (ISS). Its cross-track and along-track fields of view are 42 km (at nadir) and 192 km, respectively, for a total scene area of 8000 km(2). The HICO is an innovative prototype sensor that builds on extensive experience with airborne sensors and makes extensive use of commercial off-the-shelf components to build a space sensor at a small fraction of the usual cost and time. Here we describe the instrument's design and characterization and present early images from the ISS. © 2011 Optical Society of America

Accumulating pyramid spatial-spectral collaborative coding divergence for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Sun, Hao; Zou, Huanxin; Zhou, Shilin

2016-03-01

Detection of anomalous targets of various sizes in hyperspectral data has received a lot of attention in reconnaissance and surveillance applications. Many anomaly detectors have been proposed in literature. However, current methods are susceptible to anomalies in the processing window range and often make critical assumptions about the distribution of the background data. Motivated by the fact that anomaly pixels are often distinctive from their local background, in this letter, we proposed a novel hyperspectral anomaly detection framework for real-time remote sensing applications. The proposed framework consists of four major components, sparse feature learning, pyramid grid window selection, joint spatial-spectral collaborative coding and multi-level divergence fusion. It exploits the collaborative representation difference in the feature space to locate potential anomalies and is totally unsupervised without any prior assumptions. Experimental results on airborne recorded hyperspectral data demonstrate that the proposed methods adaptive to anomalies in a large range of sizes and is well suited for parallel processing.

Trace gas detection in hyperspectral imagery using the wavelet packet subspace

NASA Astrophysics Data System (ADS)

Salvador, Mark A. Z.

This dissertation describes research into a new remote sensing method to detect trace gases in hyperspectral and ultra-spectral data. This new method is based on the wavelet packet transform. It attempts to improve both the computational tractability and the detection of trace gases in airborne and spaceborne spectral imagery. Atmospheric trace gas research supports various Earth science disciplines to include climatology, vulcanology, pollution monitoring, natural disasters, and intelligence and military applications. Hyperspectral and ultra-spectral data significantly increases the data glut of existing Earth science data sets. Spaceborne spectral data in particular significantly increases spectral resolution while performing daily global collections of the earth. Application of the wavelet packet transform to the spectral space of hyperspectral and ultra-spectral imagery data potentially improves remote sensing detection algorithms. It also facilities the parallelization of these methods for high performance computing. This research seeks two science goals, (1) developing a new spectral imagery detection algorithm, and (2) facilitating the parallelization of trace gas detection in spectral imagery data.

Mapping the distribution of materials in hyperspectral data using the USGS Material Identification and Characterization Algorithm (MICA)

USGS Publications Warehouse

Kokaly, R.F.; King, T.V.V.; Hoefen, T.M.

2011-01-01

Identifying materials by measuring and analyzing their reflectance spectra has been an important method in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow scientists to detect materials and map their distributions across the landscape. With new satellite-borne hyperspectral sensors planned for the future, for example, HYSPIRI (HYPerspectral InfraRed Imager), robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral-feature based analysis of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described in this paper. The core concepts and calculations of MICA are presented. A MICA command file has been developed and applied to map minerals in the full-country coverage of the 2007 Afghanistan HyMap hyperspectral data. ?? 2011 IEEE.

How Cities Breathe: Ground-Referenced, Airborne Hyperspectral Imaging Precursor Measurements To Space-Based Monitoring

NASA Technical Reports Server (NTRS)

Leifer, Ira; Tratt, David; Quattrochi, Dale; Bovensmann, Heinrich; Gerilowski, Konstantin; Buchwitz, Michael; Burrows, John

2013-01-01

Methane's (CH4) large global warming potential (Shindell et al., 2012) and likely increasing future emissions due to global warming feedbacks emphasize its importance to anthropogenic greenhouse warming (IPCC, 2007). Furthermore, CH4 regulation has far greater near-term climate change mitigation potential versus carbon dioxide CO2, the other major anthropogenic Greenhouse Gas (GHG) (Shindell et al., 2009). Uncertainties in CH4 budgets arise from the poor state of knowledge of CH4 sources - in part from a lack of sufficiently accurate assessments of the temporal and spatial emissions and controlling factors of highly variable anthropogenic and natural CH4 surface fluxes (IPCC, 2007) and the lack of global-scale (satellite) data at sufficiently high spatial resolution to resolve sources. Many important methane (and other trace gases) sources arise from urban and mega-urban landscapes where anthropogenic activities are centered - most of humanity lives in urban areas. Studying these complex landscape tapestries is challenged by a wide and varied range of activities at small spatial scale, and difficulty in obtaining up-to-date landuse data in the developed world - a key desire of policy makers towards development of effective regulations. In the developing world, challenges are multiplied with additional political access challenges. As high spatial resolution satellite and airborne data has become available, activity mapping applications have blossomed - i.e., Google maps; however, tap a minute fraction of remote sensing capabilities due to limited (three band) spectral information. Next generation approaches that incorporate high spatial resolution hyperspectral and ultraspectral data will allow detangling of the highly heterogeneous usage megacity patterns by providing diagnostic identification of chemical composition from solids (refs) to gases (refs). To properly enable these next generation technologies for megacity include atmospheric radiative transfer modeling

Unsupervised hierarchical partitioning of hyperspectral images: application to marine algae identification

NASA Astrophysics Data System (ADS)

Chen, B.; Chehdi, K.; De Oliveria, E.; Cariou, C.; Charbonnier, B.

2015-10-01

In this paper a new unsupervised top-down hierarchical classification method to partition airborne hyperspectral images is proposed. The unsupervised approach is preferred because the difficulty of area access and the human and financial resources required to obtain ground truth data, constitute serious handicaps especially over large areas which can be covered by airborne or satellite images. The developed classification approach allows i) a successive partitioning of data into several levels or partitions in which the main classes are first identified, ii) an estimation of the number of classes automatically at each level without any end user help, iii) a nonsystematic subdivision of all classes of a partition Pj to form a partition Pj+1, iv) a stable partitioning result of the same data set from one run of the method to another. The proposed approach was validated on synthetic and real hyperspectral images related to the identification of several marine algae species. In addition to highly accurate and consistent results (correct classification rate over 99%), this approach is completely unsupervised. It estimates at each level, the optimal number of classes and the final partition without any end user intervention.

Regional prediction of soil organic carbon content over croplands using airborne hyperspectral data

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefebvre, Josias; Chehdi, Kacem

2015-04-01

This study was carried out in the framework of the Prostock-Gessol3 and the BASC-SOCSENSIT projects, dedicated to the spatial monitoring of the effects of exogenous organic matter land application on soil organic carbon storage. It aims at identifying the potential of airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks which were georeferenced. Tracks were atmospherically corrected using a set of 22 synchronous field spectra of both bare soils, black and white targets and impervious surfaces. Atmospherically corrected track tiles were mosaicked at a 2 m-resolution resulting in a 66 Gb image. A SPOT4 satellite image was acquired the same day in the framework of the SPOT4-Take Five program of the French Space Agency (CNES) which provided it with atmospheric correction. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then NDVI calculation and thresholding enabled to map agricultural fields with bare soil. All 18 sampled sites known to be bare at this very date were correctly included in this map. A total of 85 sites sampled in 2013 or in the 3 previous years were identified as bare by means of this map. Predictions were made from the mosaic spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples. The use of the total sample including 27 sites under cloud shadows led to non-significant results. Considering 43 sites outside cloud shadows only, median

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.

Spectral difference analysis and airborne imaging classification for citrus greening infected trees

USDA-ARS?s Scientific Manuscript database

Citrus greening, also called Huanglongbing (HLB), became a devastating disease spread through citrus groves in Florida, since it was first found in 2005. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were acquired to detect citrus greening infected trees in 20...

Oil spill characterization thanks to optical airborne imagery during the NOFO campaign 2015

NASA Astrophysics Data System (ADS)

Viallefont-Robinet, F.; Ceamanos, X.; Angelliaume, S.; Miegebielle, V.

2017-10-01

One of the objectives of the NAOMI (New Advanced Observation Method Integration) research project, fruit of a partnership between Total and ONERA, is to work on the detection, the quantification and the characterization of offshore hydrocarbon at the sea surface using airborne remote sensing. In this framework, work has been done to characterize the spectral signature of hydrocarbons in lab in order to build a database of oil spectral signatures. The main objective of this database is to provide spectral libraries for data processing algorithms to be applied to airborne VNIRSWIR hyperspectral images. A campaign run by the NOFO institute (Norwegian Clean Seas Association for Operating Companies) took place in 2015 to test anti-pollution equipment. During this campaign, several hydrocarbon products, including an oil emulsion, were released into the sea, off the Norwegian coast. The NOFO team allowed the NAOMI project to acquire data over the resulting oil slicks using the SETHI system, which is an airborne remote sensing imaging system developed by ONERA. SETHI integrates a new generation of optoelectronic and radar payloads and can operate over a wide range of frequency bands. SETHI is a pod-based system operating onboard a Falcon 20 Dassault aircraft, which is owned by AvDEF. For these experiments, imaging sensors were constituted by 2 synthetic aperture radar (SAR), working at X and L bands in a full polarimetric mode (HH, HV, VH, VV) and 2 HySpex hyperspectral cameras working in the VNIR (0,4 to 1 μm) and SWIR (1 to 2,5 μm) spectral ranges. A sample of the oil emulsion that was used during the campaign was sent to our laboratory for analysis. Measurements of its transmission and of its reflectance in the VNIR and SWIR spectral domains have been performed at ONERA with a Perkin Elmer spectroradiometer and a spectrogoniometer. Several samples of the oil emulsion were prepared in order to measure spectral variations according to oil thickness, illumination angle

Improved Airborne System for Sensing Wildfires

NASA Technical Reports Server (NTRS)

McKeown, Donald; Richardson, Michael

2008-01-01

The Wildfire Airborne Sensing Program (WASP) is engaged in a continuing effort to develop an improved airborne instrumentation system for sensing wildfires. The system could also be used for other aerial-imaging applications, including mapping and military surveillance. Unlike prior airborne fire-detection instrumentation systems, the WASP system would not be based on custom-made multispectral line scanners and associated custom- made complex optomechanical servomechanisms, sensors, readout circuitry, and packaging. Instead, the WASP system would be based on commercial off-the-shelf (COTS) equipment that would include (1) three or four electronic cameras (one for each of three or four wavelength bands) instead of a multispectral line scanner; (2) all associated drive and readout electronics; (3) a camera-pointing gimbal; (4) an inertial measurement unit (IMU) and a Global Positioning System (GPS) receiver for measuring the position, velocity, and orientation of the aircraft; and (5) a data-acquisition subsystem. It would be necessary to custom-develop an integrated sensor optical-bench assembly, a sensor-management subsystem, and software. The use of mostly COTS equipment is intended to reduce development time and cost, relative to those of prior systems.

Hyperspectral Soil Mapper (HYSOMA) software interface: Review and future plans

NASA Astrophysics Data System (ADS)

Chabrillat, Sabine; Guillaso, Stephane; Eisele, Andreas; Rogass, Christian

2014-05-01

With the upcoming launch of the next generation of hyperspectral satellites that will routinely deliver high spectral resolution images for the entire globe (e.g. EnMAP, HISUI, HyspIRI, HypXIM, PRISMA), an increasing demand for the availability/accessibility of hyperspectral soil products is coming from the geoscience community. Indeed, many robust methods for the prediction of soil properties based on imaging spectroscopy already exist and have been successfully used for a wide range of soil mapping airborne applications. Nevertheless, these methods require expert know-how and fine-tuning, which makes them used sparingly. More developments are needed toward easy-to-access soil toolboxes as a major step toward the operational use of hyperspectral soil products for Earth's surface processes monitoring and modelling, to allow non-experienced users to obtain new information based on non-expensive software packages where repeatability of the results is an important prerequisite. In this frame, based on the EU-FP7 EUFAR (European Facility for Airborne Research) project and EnMAP satellite science program, higher performing soil algorithms were developed at the GFZ German Research Center for Geosciences as demonstrators for end-to-end processing chains with harmonized quality measures. The algorithms were built-in into the HYSOMA (Hyperspectral SOil MApper) software interface, providing an experimental platform for soil mapping applications of hyperspectral imagery that gives the choice of multiple algorithms for each soil parameter. The software interface focuses on fully automatic generation of semi-quantitative soil maps such as soil moisture, soil organic matter, iron oxide, clay content, and carbonate content. Additionally, a field calibration option calculates fully quantitative soil maps provided ground truth soil data are available. Implemented soil algorithms have been tested and validated using extensive in-situ ground truth data sets. The source of the HYSOMA

Airborne Thermal Infrared Multispectral Scanner (TIMS) images over disseminated gold deposits, Osgood Mountains, Humboldt County, Nevada

NASA Technical Reports Server (NTRS)

Krohn, M. Dennis

1986-01-01

The U.S. Geological Survey (USGS) acquired airborne Thermal Infrared Multispectral Scanner (TIMS) images over several disseminated gold deposits in northern Nevada in 1983. The aerial surveys were flown to determine whether TIMS data could depict jasperoids (siliceous replacement bodies) associated with the gold deposits. The TIMS data were collected over the Pinson and Getchell Mines in the Osgood Mountains, the Carlin, Maggie Creek, Bootstrap, and other mines in the Tuscarora Mountains, and the Jerritt Canyon Mine in the Independence Mountains. The TIMS data seem to be a useful supplement to conventional geochemical exploration for disseminated gold deposits in the western United States. Siliceous outcrops are readily separable in the TIMS image from other types of host rocks. Different forms of silicification are not readily separable, yet, due to limitations of spatial resolution and spectral dynamic range. Features associated with the disseminated gold deposits, such as the large intrusive bodies and fault structures, are also resolvable on TIMS data. Inclusion of high-resolution thermal inertia data would be a useful supplement to the TIMS data.

Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

NASA Astrophysics Data System (ADS)

Alonzo, Mike Gerard

Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service's i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

USDA-ARS?s Scientific Manuscript database

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified ...

Terrestrial hyperspectral image shadow restoration through fusion with terrestrial lidar

NASA Astrophysics Data System (ADS)

Hartzell, Preston J.; Glennie, Craig L.; Finnegan, David C.; Hauser, Darren L.

2017-05-01

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from exclusively airborne observations to include terrestrial modalities. In contrast to airborne collection geometry, hyperspectral imagery captured from terrestrial cameras is prone to extensive solar shadowing on vertical surfaces leading to reductions in pixel classification accuracies or outright removal of shadowed areas from subsequent analysis tasks. We demonstrate the use of lidar spatial information for sub-pixel HSI shadow detection and the restoration of shadowed pixel spectra via empirical methods that utilize sunlit and shadowed pixels of similar material composition. We examine the effectiveness of radiometrically calibrated lidar intensity in identifying these similar materials in sun and shade conditions and further evaluate a restoration technique that leverages ratios derived from the overlapping lidar laser and HSI wavelengths. Simulations of multiple lidar wavelengths, i.e., multispectral lidar, indicate the potential for HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance of shadowed HSI pixels is quantified for imagery of a geologic outcrop through improvements in spectral shape, spectral scale, and HSI band correlation.

Hyperspectral Analysis of Rice Phenological Stages in Northeast China

NASA Astrophysics Data System (ADS)

Gnyp, M. L.; Yao, Y.; Yu, K.; Huang, S.; Aasen, H.; Lenz-Wiedemann, V. I. S.; Miao, Y.; Bareth, G.

2012-07-01

The objective of this contribution is to monitor rice (Oryza sativa L., irrigated lowland rice) growth with multitemporal hyperspectral data during different phenological stages in Northeast China (Sanjiang Plain). Multitemporal hyperspectral data were measured with field spectroradiometers (ASD Inc.: QualitySpec and FieldSpec3) for two field experiments and nine farmers' fields. The field measurements were carried out together with corresponding measurements of agronomic data (aboveground biomass [AGB], Leaf Area Index [LAI], number of tillers). Eight selected standard hyperspectral vegetation indices (VIs), proved in several studies to be highly correlated with AGB or LAI, were calculated on the measured experimental field data. Additionally, the best two-band combinations for the Normalized Ratio Index (NRI) were determined. The results indicate that the NRI performed better than the selected standard VIs at the stages of stem elongation, booting and heading and also across all stages. Especially during the stem elongation stage (R2 = 0.76) and across all stages (R2 = 0.70), the NRI performed best. When applying the NRI on the farmers' field data, the performance was lower (R2 < 0.60). Overall, the sensitive individual wavelengths (±10 nm) for the best two-band combinations were detected at 711 and 799 nm (for tillering stage), 1575 and 1678 nm (for stem elongation stage), 515 and 695 nm (for booting stage), and 533 and 713 nm (for all stages). The results suggest that hyperspectral-based methods can estimate paddy rice AGB with a satisfying accuracy. In the context of precision agriculture, the findings are useful for future development of new hyperspectral devices such as scanners or cameras which could be fixed on tractors or unmanned aerial vehicles (UAVs).

Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

NASA Astrophysics Data System (ADS)

Schilling, Hendrik; Lenz, Andreas; Gross, Wolfgang; Perpeet, Dominik; Wuttke, Sebastian; Middelmann, Wolfgang

2013-10-01

Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany.

Characterisation methods for the hyperspectral sensor HySpex at DLR's calibration home base

NASA Astrophysics Data System (ADS)

Baumgartner, Andreas; Gege, Peter; Köhler, Claas; Lenhard, Karim; Schwarzmaier, Thomas

2012-09-01

The German Aerospace Center's (DLR) Remote Sensing Technology Institute (IMF) operates a laboratory for the characterisation of imaging spectrometers. Originally designed as Calibration Home Base (CHB) for the imaging spectrometer APEX, the laboratory can be used to characterise nearly every airborne hyperspectral system. Characterisation methods will be demonstrated exemplarily with HySpex, an airborne imaging spectrometer system from Norsk Elektro Optikks A/S (NEO). Consisting of two separate devices (VNIR-1600 and SWIR-320me) the setup covers the spectral range from 400 nm to 2500 nm. Both airborne sensors have been characterised at NEO. This includes measurement of spectral and spatial resolution and misregistration, polarisation sensitivity, signal to noise ratios and the radiometric response. The same parameters have been examined at the CHB and were used to validate the NEO measurements. Additionally, the line spread functions (LSF) in across and along track direction and the spectral response functions (SRF) for certain detector pixels were measured. The high degree of lab automation allows the determination of the SRFs and LSFs for a large amount of sampling points. Despite this, the measurement of these functions for every detector element would be too time-consuming as typical detectors have 105 elements. But with enough sampling points it is possible to interpolate the attributes of the remaining pixels. The knowledge of these properties for every detector element allows the quantification of spectral and spatial misregistration (smile and keystone) and a better calibration of airborne data. Further laboratory measurements are used to validate the models for the spectral and spatial properties of the imaging spectrometers. Compared to the future German spaceborne hyperspectral Imager EnMAP, the HySpex sensors have the same or higher spectral and spatial resolution. Therefore, airborne data will be used to prepare for and validate the spaceborne system

Hyperspectral imaging system for whole corn ear surface inspection

NASA Astrophysics Data System (ADS)

Yao, Haibo; Kincaid, Russell; Hruska, Zuzana; Brown, Robert L.; Bhatnagar, Deepak; Cleveland, Thomas E.

2013-05-01

Aflatoxin is a mycotoxin produced mainly by Aspergillus flavus (A.flavus) and Aspergillus parasitiucus fungi that grow naturally in corn. Very serious health problems such as liver damage and lung cancer can result from exposure to high toxin levels in grain. Consequently, many countries have established strict guidelines for permissible levels in consumables. Conventional chemical-based analytical methods used to screen for aflatoxin such as thin-layer chromatography (TLC) and high performance liquid chromatography (HPLC) are time consuming, expensive, and require the destruction of samples as well as proper training for data interpretation. Thus, it has been a continuing effort within the research community to find a way to rapidly and non-destructively detect and possibly quantify aflatoxin contamination in corn. One of the more recent developments in this area is the use of spectral technology. Specifically, fluorescence hyperspectral imaging offers a potential rapid, and non-invasive method for contamination detection in corn infected with toxigenic A.flavus spores. The current hyperspectral image system is designed for scanning flat surfaces, which is suitable for imaging single or a group of corn kernels. In the case of a whole corn cob, it is preferred to be able to scan the circumference of the corn ear, appropriate for whole ear inspection. This paper discusses the development of a hyperspectral imaging system for whole corn ear imaging. The new instrument is based on a hyperspectral line scanner using a rotational stage to turn the corn ear.

Commodity cluster and hardware-based massively parallel implementations of hyperspectral imaging algorithms

NASA Astrophysics Data System (ADS)

Plaza, Antonio; Chang, Chein-I.; Plaza, Javier; Valencia, David

2006-05-01

The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.

The airborne infrared scanner as a geophysical research tool

USGS Publications Warehouse

Friedman, Jules D.

1970-01-01

The infrared scanner is proving to be an effective anomaly-mapping tool, albeit one which depicts surface emission directly and heat mass transfer from depths only indirectly and at a threshold level 50 to 100 times the normal conductive heat flow of the earth. Moreover, successive terrain observations are affected by time-dependent variables such as the diurnal and seasonal warming and cooling cycle of a point on the earth's surface. In planning precise air borne surveys of radiant flux from the earth's surface, account must be taken of background noise created by variations in micrometeorological factors and emissivity of surface materials, as well as the diurnal temperature cycle. The effect of the diurnal cycle may be minimized by planning predawn aerial surveys. In fact, the diurnal change is very small for most water bodies and the emissivity factor for water (e) =~ 1 so a minimum background noise is characteristic of scanner records of calm water surfaces.

Using hyperspectral remote sensing for land cover classification

NASA Astrophysics Data System (ADS)

Zhang, Wendy W.; Sriharan, Shobha

2005-01-01

This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earth-sensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. These sensors are designed to cover a wide range of the electromagnetic spectrum and are generating enormous amounts of data that must be processed, stored, and made available to the user community. The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40 and 2.45 mm. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty Fellowship Program at Stennis Space Center. Research Systems Inc.'s (RSI) ENVI software package was used in this application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data endmembers.

Land use classification utilizing remote multispectral scanner data and computer analysis techniques

NASA Technical Reports Server (NTRS)

Leblanc, P. N.; Johannsen, C. J.; Yanner, J. E.

1973-01-01

An airborne multispectral scanner was used to collect the visible and reflective infrared data. A small subdivision near Lafayette, Indiana was selected as the test site for the urban land use study. Multispectral scanner data were collected over the subdivision on May 1, 1970 from an altitude of 915 meters. The data were collected in twelve wavelength bands from 0.40 to 1.00 micrometers by the scanner. The results indicated that computer analysis of multispectral data can be very accurate in classifying and estimating the natural and man-made materials that characterize land uses in an urban scene.

NASA Cold Land Processes Experiment (CLPX 2002/03): Airborne remote sensing

Treesearch

Don Cline; Simon Yueh; Bruce Chapman; Boba Stankov; Al Gasiewski; Dallas Masters; Kelly Elder; Richard Kelly; Thomas H. Painter; Steve Miller; Steve Katzberg; Larry Mahrt

2009-01-01

This paper describes the airborne data collected during the 2002 and 2003 Cold Land Processes Experiment (CLPX). These data include gamma radiation observations, multi- and hyperspectral optical imaging, optical altimetry, and passive and active microwave observations of the test areas. The gamma observations were collected with the NOAA/National Weather Service Gamma...

Characterization of forest crops with a range of nutrient and water treatments using AISA Hyperspectral Imagery.

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

Gong, Binglei; Im, Jungho; Jensen, John, R.

2012-07-01

This research examined the utility of Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for estimating the biomass of three forest crops---sycamore, sweetgum and loblolly pine--planted in experimental plots with a range of fertilization and irrigation treatments on the Savannah River Site near Aiken, South Carolina.

Use of Airborne Hyperspectral Data in the Simulation of Satellite Images

NASA Astrophysics Data System (ADS)

de Miguel, Eduardo; Jimenez, Marcos; Ruiz, Elena; Salido, Elena; Gutierrez de la Camara, Oscar

2016-08-01

The simulation of future images is part of the development phase of most Earth Observation missions. This simulation uses frequently as starting point images acquired from airborne instruments. These instruments provide the required flexibility in acquisition parameters (time, date, illumination and observation geometry...) and high spectral and spatial resolution, well above the target values (as required by simulation tools). However, there are a number of important problems hampering the use of airborne imagery. One of these problems is that observation zenith angles (OZA), are far from those that the misisons to be simulated would use.We examine this problem by evaluating the difference in ground reflectance estimated from airborne images for different observation/illumination geometries. Next, we analyze a solution for simulation purposes, in which a Bi- directional Reflectance Distribution Function (BRDF) model is attached to an image of the isotropic surface reflectance. The results obtained confirm the need for reflectance anisotropy correction when using airborne images for creating a reflectance map for simulation purposes. But this correction should not be used without providing the corresponding estimation of BRDF, in the form of model parameters, to the simulation teams.

Derivative Analysis of AVIRIS Hyperspectral Data for the Detection of Plant Stress

NASA Technical Reports Server (NTRS)

Estep, Lee; Berglund, Judith

2001-01-01

A remote sensing campaign was conducted over a U.S. Department of Agriculture test site at Shelton, Nebraska. The test field was set off in blocks that were differentially treated with nitrogen. Four replicates of 0-kg/ha to 200-kg/ha, in 50-kg/ha increments, were present. Low-altitude AVIRIS hyperspectral data were collected over the site in 224 spectral bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to evaluate published, derivative-based algorithms for the detection of plant stress, different derivative-based approaches were applied to the collected AVIRIS image cube. The results indicate that, given good quality hyperspectral imagery, derivative techniques compare favorably with simple, well known band ratio algorithms for detection of plant stress.

Assessing exergy of forest ecosystem using airborne and satellite data

NASA Astrophysics Data System (ADS)

Brovkina, Olga; Fabianek, Tomas; Lukes, Petr; Zemek, Frantisek

2017-04-01

Interactions of the energy flows of forest ecosystem with environment are formed by a suite of forest structure, functions and pathways of self-control. According to recent thermodynamic theory for open systems, concept of exergy of solar radiation has been applied to estimate energy consumptions on evapotranspiration and biomass production in forest ecosystem or to indicate forest decline and human land use impact on ecosystem stability. However, most of the methods for exergy estimation in forest ecosystem is not stable and its physical meaning remains on the surface. This study was aimed to contribute to understanding the exergy of forest ecosystem using combination of remote sensing (RS) and eddy covariance technologies, specifically: 1/to explore exergy of solar radiation depending on structure of solar spectrum (number of spectral bands of RS data), and 2/to explore the relationship between exergy and flux tower eddy covariance measurements. Two study forest sites were located in Western Beskids in the Czech Republic. The first site was dominated by young Norway spruce, the second site was dominated by mature European beech. Airborne hyperspectral data in VNIR, SWIR and TIR spectral regions were acquired 9 times for study sites during a vegetation periods in 2015-2016. Radiometric, geometric and atmospheric corrections of airborne data were performed. Satellite multispectral Landsat-8 cloud-free 21 scenes were downloaded and atmospherically corrected for the period from April to November 2015-2016. Evapotranspiration and latent heat fluxes were collected from operating flux towers located on study sites according to date and time of remote sensing data acquisition. Exergy was calculated for each satellite and airborne scene using various combinations of spectral bands as: Ex=E^out (K+ln E^out/E^in )+R, where Ein is the incoming solar energy, Eout is the reflected solar energy, R = Ein-Eout is absorbed energy, Eout/Ein is albedo and K is the Kullback increment

CNR LARA project, Italy: Airborne laboratory for environmental research

NASA Technical Reports Server (NTRS)

Bianchi, R.; Cavalli, R. M.; Fiumi, L.; Marino, C. M.; Pignatti, S.

1995-01-01

The increasing interest for the environmental problems and the study of the impact on the environment due to antropic activity produced an enhancement of remote sensing applications. The Italian National Research Council (CNR) established a new laboratory for airborne hyperspectral imaging, the LARA Project (Laboratorio Aero per Ricerche Ambientali - Airborne Laboratory for Environmental Research), equipping its airborne laboratory, a CASA-212, mainly with the Daedalus AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) instrument. MIVIS's channels, spectral bandwidths, and locations are chosen to meet the needs of scientific research for advanced applications of remote sensing data. MIVIS can make significant contributions to solving problems in many diverse areas such as geologic exploration, land use studies, mineralogy, agricultural crop studies, energy loss analysis, pollution assessment, volcanology, forest fire management and others. The broad spectral range and the many discrete narrow channels of MIVIS provide a fine quantization of spectral information that permits accurate definition of absorption features from a variety of materials, allowing the extraction of chemical and physical information of our environment. The availability of such a hyperspectral imager, that will operate mainly in the Mediterranean area, at the present represents a unique opportunity for those who are involved in environmental studies and land-management to collect systematically large-scale and high spectral-spatial resolution data of this part of the world. Nevertheless, MIVIS deployments will touch other parts of the world, where a major interest from the international scientific community is present.

Filtering high resolution hyperspectral imagery and analyzing it for quantification of water quality parameters and aquatic vegetation

NASA Astrophysics Data System (ADS)

Pande-Chhetri, Roshan

High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water

GPU Lossless Hyperspectral Data Compression System for Space Applications

NASA Technical Reports Server (NTRS)

Keymeulen, Didier; Aranki, Nazeeh; Hopson, Ben; Kiely, Aaron; Klimesh, Matthew; Benkrid, Khaled

2012-01-01

On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. At JPL, a novel, adaptive and predictive technique for lossless compression of hyperspectral data, named the Fast Lossless (FL) algorithm, was recently developed. This technique uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. Because of its outstanding performance and suitability for real-time onboard hardware implementation, the FL compressor is being formalized as the emerging CCSDS Standard for Lossless Multispectral & Hyperspectral image compression. The FL compressor is well-suited for parallel hardware implementation. A GPU hardware implementation was developed for FL targeting the current state-of-the-art GPUs from NVIDIA(Trademark). The GPU implementation on a NVIDIA(Trademark) GeForce(Trademark) GTX 580 achieves a throughput performance of 583.08 Mbits/sec (44.85 MSamples/sec) and an acceleration of at least 6 times a software implementation running on a 3.47 GHz single core Intel(Trademark) Xeon(Trademark) processor. This paper describes the design and implementation of the FL algorithm on the GPU. The massively parallel implementation will provide in the future a fast and practical real-time solution for airborne and space applications.

Fusion of Terrestrial and Airborne Laser Data for 3D modeling Applications

NASA Astrophysics Data System (ADS)

Mohammed, Hani Mahmoud

This thesis deals with the 3D modeling phase of the as-built large BIM projects. Among several means of BIM data capturing, such as photogrammetric or range tools, laser scanners have been one of the most efficient and practical tool for a long time. They can generate point clouds with high resolution for 3D models that meet nowadays' market demands. The current 3D modeling projects of as-built BIMs are mainly focused on using one type of laser scanner data, such as Airborne or Terrestrial. According to the literatures, no significant (few) efforts were made towards the fusion of heterogeneous laser scanner data despite its importance. The importance of the fusion of heterogeneous data arises from the fact that no single type of laser data can provide all the information about BIM, especially for large BIM projects that are existing on a large area, such as university buildings, or Heritage places. Terrestrial laser scanners are able to map facades of buildings and other terrestrial objects. However, they lack the ability to map roofs or higher parts in the BIM project. Airborne laser scanner on the other hand, can map roofs of the buildings efficiently and can map only small part of the facades. Short range laser scanners can map the interiors of the BIM projects, while long range scanners are used for mapping wide exterior areas in BIM projects. In this thesis the long range laser scanner data obtained in the Stop-and-Go mapping mode, the short range laser scanner data, obtained in a fully static mapping mode, and the airborne laser data are all fused together to bring a complete effective solution for a large BIM project. Working towards the 3D modeling of BIM projects, the thesis framework starts with the registration of the data, where a new fast automatic registration algorithm were developed. The next step is to recognize the different objects in the BIM project (classification), and obtain 3D models for the buildings. The last step is the development of an

Development of Hyperspectral Remote Sensing Capability For the Early Detection and Monitoring of Harmful Algal Blooms (HABs) in the Great Lakes

NASA Technical Reports Server (NTRS)

Lekki, John; Anderson, Robert; Nguyen, Quang-Viet; Demers, James; Leshkevich, George; Flatico, Joseph; Kojima, Jun

2013-01-01

Hyperspectral imagers have significant capability for detecting and classifying waterborne constituents. One particularly appropriate application of such instruments in the Great Lakes is to detect and monitor the development of potentially Harmful Algal Blooms (HABs). Two generations of small hyperspectral imagers have been built and tested for aircraft based monitoring of harmful algal blooms. In this paper a discussion of the two instruments as well as field studies conducted using these instruments will be presented. During the second field study, in situ reflectance data was obtained from the Research Vessel Lake Guardian in conjunction with reflectance data obtained with the hyperspectral imager from overflights of the same locations. A comparison of these two data sets shows that the airborne hyperspectral imager closely matches measurements obtained from instruments on the lake surface and thus positively supports its utilization for detecting and monitoring HABs.

Overhead longwave infrared hyperspectral material identification using radiometric models

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

Zelinski, M. E.

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimalmore » atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.« less

A Field Portable Hyperspectral Goniometer for Coastal Characterization

NASA Technical Reports Server (NTRS)

Bachmann, Charles M.; Gray, Deric; Abelev, Andrei; Philpot, William; Fusina, Robert A.; Musser, Joseph A.; Vermillion, Michael; Doctor, Katarina; White, Maurice; Georgiev, Georgi

2012-01-01

During an airborne multi-sensor remote sensing experiment at the Virginia Coast Reserve (VCR) Long Term Ecological Research (LTER) site in June 2011 (VCR '11), first measurements were taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). GOPHER measures the angular distribution of hyperspectral reflectance. GOPHER was constructed for NRL by Spectra Vista Corporation (SVC) and the University of Lethbridge through a capital equipment purchase in 2010. The GOPHER spectrometer is an SVC HR -1024, which measures hyperspectral reflectance over the range from 350 -2500 nm, the visible, near infrared, and short-wave infrared. During measurements, the spectrometer travels along a zenith quarter -arc track that can rotate in azimuth, allowing for measurement of the bi-directional reflectance distribution function (BRDF) over the whole hemisphere. The zenith arc has a radius of approximately 2m, and the spectrometer scan pattern can be programmed on the fly during calibration and validation efforts. The spectrometer and zenith arc assembly can be raised and lowered along a mast to allow for measurement of uneven terrain or vegetation canopies of moderate height. Hydraulics on the chassis allow for leveling of the instrument in the field. At just over 400 lbs, GOPHER is a field portable instrument and can be transformed into a compact trailer assembly for movement over long distances in the field.

Ore minerals textural characterization by hyperspectral imaging

NASA Astrophysics Data System (ADS)

Bonifazi, Giuseppe; Picone, Nicoletta; Serranti, Silvia

2013-02-01

The utilization of hyperspectral detection devices, for natural resources mapping/exploitation through remote sensing techniques, dates back to the early 1970s. From the first devices utilizing a one-dimensional profile spectrometer, HyperSpectral Imaging (HSI) devices have been developed. Thus, from specific-customized devices, originally developed by Governmental Agencies (e.g. NASA, specialized research labs, etc.), a lot of HSI based equipment are today available at commercial level. Parallel to this huge increase of hyperspectral systems development/manufacturing, addressed to airborne application, a strong increase also occurred in developing HSI based devices for "ground" utilization that is sensing units able to play inside a laboratory, a processing plant and/or in an open field. Thanks to this diffusion more and more applications have been developed and tested in this last years also in the materials sectors. Such an approach, when successful, is quite challenging being usually reliable, robust and characterised by lower costs if compared with those usually associated to commonly applied analytical off- and/or on-line analytical approaches. In this paper such an approach is presented with reference to ore minerals characterization. According to the different phases and stages of ore minerals and products characterization, and starting from the analyses of the detected hyperspectral firms, it is possible to derive useful information about mineral flow stream properties and their physical-chemical attributes. This last aspect can be utilized to define innovative process mineralogy strategies and to implement on-line procedures at processing level. The present study discusses the effects related to the adoption of different hardware configurations, the utilization of different logics to perform the analysis and the selection of different algorithms according to the different characterization, inspection and quality control actions to apply.

Fusion of remotely sensed data from airborne and ground-based sensors for cotton regrowth study

USDA-ARS?s Scientific Manuscript database

The study investigated the use of aerial multispectral imagery and ground-based hyperspectral data for the discrimination of different crop types and timely detection of cotton plants over large areas. Airborne multispectral imagery and ground-based spectral reflectance data were acquired at the sa...

Comparative analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Hyperspectral Thermal Emission Spectrometer (HyTES) longwave infrared (LWIR) hyperspectral data for geologic mapping

NASA Astrophysics Data System (ADS)

Kruse, Fred A.

2015-05-01

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and spatially coincident Hyperspectral Thermal Emission Spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada, USA. AVIRIS, with 224 bands at 10 nm spectral resolution over the range 0.4 - 2.5 μm at 3-meter spatial resolution were converted to reflectance using an atmospheric model. HyTES data with 256 bands at approximately 17 nm spectral resolution covering the 8 - 12 μm range at 4-meter spatial resolution were converted to emissivity using a longwave infrared (LWIR) radiative transfer atmospheric compensation model and a normalized temperature-emissivity separation approach. Key spectral endmembers were separately extracted for each wavelength region and identified, and the predominant material at each pixel was mapped for each range using Mixture-Tuned-Matched Filtering (MTMF), a partial unmixing approach. AVIRIS mapped iron oxides, clays, mica, and silicification (hydrothermal alteration); and the difference between calcite and dolomite. HyTES separated and mapped several igneous phases (not possible using AVIRIS), silicification, and validated separation of calcite from dolomite. Comparison of the material maps from the different modes, however, reveals complex overlap, indicating that multiple materials/processes exist in many areas. Combined and integrated analyses were performed to compare individual results and more completely characterize occurrences of multiple materials. Three approaches were used 1) integrated full-range analysis, 2) combined multimode classification, and 3) directed combined analysis in geologic context. Results illustrate that together, these two datasets provide an improved picture of the distribution of geologic units and subsequent alteration.

Oil Spill AISA+ Hyperspectral Data Detection Based on Different Sea Surface Glint Suppression Methods

NASA Astrophysics Data System (ADS)

Yang, J.; Ren, G.; Ma, Y.; Dong, L.; Wan, J.

2018-04-01

The marine oil spill is a sudden event, and the airborne hyperspectral means to detect the oil spill is an important part of the rapid response. Sun glint, the specular reflection of sun light from water surface to sensor, is inevitable due to the limitation of observation geometry, which makes so much bright glint in image that it is difficult to extract oil spill feature information from the remote sensing data. This paper takes AISA+ airborne hyperspectral oil spill image as data source, using multi-scale wavelet transform, enhanced Lee filter, enhanced Frost filter and mean filter method for sea surface glint suppression of images. And then the classical SVM method is used for the oil spill information detection, and oil spill information distribution map obtained by human-computer interactive interpretation is used to verify the accuracy of oil spill detection. The results show that the above methods can effectively suppress the sea surface glints and improve the accuracy of oil spill detection. The enhanced Lee filter method has the highest detection accuracy of 88.28 %, which is 12.2 % higher than that of the original image.

Quantification of Water Quality Parameters for the Wabash River Using Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Tan, J.; Cherkauer, K. A.; Chaubey, I.

2011-12-01

Increasingly impaired water bodies in the agriculturally dominated Midwestern United States pose a risk to water supplies, aquatic ecology and contribute to the eutrophication of the Gulf of Mexico. Improving regional water quality calls for new techniques for monitoring and managing water quality over large river systems. Optical indicators of water quality enable a timely and cost-effective method for observing and quantifying water quality conditions by remote sensing. Compared to broad spectral sensors such as Landsat, which observe reflectance over limited spectral bands, hyperspectral sensors should have significant advantages in their ability to estimate water quality parameters because they are designed to split the spectral signature into hundreds of very narrow spectral bands increasing their ability to resolve optically sensitive water quality indicators. Two airborne hyperspectral images were acquired over the Wabash River using a ProSpecTIR-VS2 sensor system on May 15th, 2010. These images were analyzed together with concurrent in-stream water quality data collected to assess our ability to extract optically sensitive constituents. Utilizing the correlation between in-stream data and reflectance from the hyperspectral images, models were developed to estimate the concentrations of chlorophyll a, dissolved organic carbon and total suspended solids. Models were developed using the full array of hyperspectral bands, as well as Landsat bands synthesized by averaging hyperspectral bands within the Landsat spectral range. Higher R2 and lower RMSE values were found for the models taking full advantage of the hyperspectral sensor, supporting the conclusion that the hyperspectral sensor was better at predicting the in-stream concentrations of chlorophyll a, dissolved organic carbon and total suspended solids in the Wabash River. Results also suggest that predictive models may not be the same for the Wabash River as for its tributaries.

Collation of earth resources data collected by ERIM airborne sensors

NASA Technical Reports Server (NTRS)

Hasell, P. G., Jr.

1975-01-01

Earth resources imagery from nine years of data collection with developmental airborne sensors is cataloged for reference. The imaging sensors include single and multiband line scanners and side-looking radars. The operating wavelengths of the sensors include ultraviolet, visible and infrared band scanners, and X- and L-band radar. Imagery from all bands (radar and scanner) were collected at some sites and many sites had repeated coverage. The multiband scanner data was radiometrically calibrated. Illustrations show how the data can be used in earth resource investigations. References are made to published reports which have made use of the data in completed investigations. Data collection sponsors are identified and a procedure described for gaining access to the data.

Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

NASA Astrophysics Data System (ADS)

Vahidi, Vahid; Saberinia, Ebrahim; Regentova, Emma E.

2017-10-01

A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA

Treesearch

Sarah A. Lewis; Andrew T. Hudak; Roger D. Ottmar; Peter R. Robichaud; Leigh B. Lentile; Sharon M. Hood; James B. Cronan; Penny Morgan

2011-01-01

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in...

PROGRAM ASPECT - FOR REMOTE SENSING OF AIRBORNE PLUMES

EPA Science Inventory

The SAFEGUARD program is a multi-sensor program for the detection and imaging of chemical plumes and vapors. The system is composed of an airborne sensor suite including an infrared line scanner and a high-speed fourier transform infrared spectrometer. Both systems are integrat...

The Hyperspectral Satellite and Program EnMAP (Environmental Monitoring and Analysis Program)

NASA Astrophysics Data System (ADS)

Stuffler, T.; Kaufmann, C.; Hofer, S.; Förster, K. P.; Schreier, G.; Mueller, A.; Penné, B.

2008-08-01

In the upcoming generation of satellite sensors, hyperspectral instruments will play a significant role and are considered world-wide within different future planning. Our team has successfully finished the Phase B study for the advanced hyperspectral mission EnMAP. Routine operations shall start in 2012. The scientific lead of the mission is at the GFZ and the industrial prime ship at Kayser-Threde. The performance of the hyperspectral instrument allows for a detailed monitoring, characterisation and parameter extraction of rock/soil targets, vegetation, and inland and coastal waters on a global scale supporting a wide variety of applications in agriculture, forestry, water management and geology. The EnMAP instrument provides over 240 continuous spectral bands in the wavelength range between 420 - 2450 nm with a ground resolution of 30 m x 30 m. Thus, the broad science and application community can draw from an extensive and highly resolved pool of information supporting the modelling and optimisation process on their results. The operation of an airborne system (ARES) as an element in the HGF hyperspectral network and the ongoing evolution concerning data handling and extraction procedures, will support the later inclusion process of EnMAP into the growing scientist and user communities. As a scientific pathfinder mission a broad international science community has raised larger interest in the hyperspectral data sets as well as value adding companies investigating the commercial potential of EnMAP. The presented paper describes the instrument and mission highlighting the data application and the actual status in the EnMAP planning phase.

The French proposal for a high spatial resolution Hyperspectral mission

NASA Astrophysics Data System (ADS)

Carrère, Véronique; Briottet, Xavier; Jacquemoud, Stéphane; Marion, Rodolphe; Bourguignon, Anne; Chami, Malik; Chanussot, Jocelyn; Chevrel, Stéphane; Deliot, Philippe; Dumont, Marie; Foucher, Pierre-Yves; Gomez, Cécile; Roman-Minghelli, Audrey; Sheeren, David; Weber, Christiane; Lefèvre, Marie-José; Mandea, Mioara

2014-05-01

More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 - 2.5 µm] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

Second International Airborne Remote Sensing Conference and Exhibition

NASA Technical Reports Server (NTRS)

1996-01-01

The conference provided four days of displays and scientific presentations on applications, technology, a science of sub-orbital data gathering and analysis. The twelve displayed aircraft equipped with sophisticated instrumentation represented a wide range of environmental and reconnaissance missions,including marine pollution control, fire detection, Open Skies Treaty verification, thermal mapping, hydrographical measurements, military research, ecological and agricultural observations, geophysical research, atmospheric and meterological observations, and aerial photography. The U.S. Air Force and the On-Site Inspection Agency displayed the new Open Skies Treaty verification Boeing OC 135B that promotes international monitoring of military forces and activities. SRl's Jetstream uses foliage and ground penetrating SAR for forest inventories, toxic waste delineation, and concealed target and buried unexploded ordnance detection. Earth Search Sciences's Gulfstream 1 with prototype miniaturized airborne hyperspectral imaging equipment specializes in accurate mineral differentiation, low-cost hydrocarbon exploration, and nonproliferation applications. John E. Chance and the U.S. Army Corps of Engineers displayed the Bell 2 helicopter with SHOALS that performs hydrographic surveying of navigation projects, coastal environment assessment, and nautical charting surveys. Bechtel Nevada and U.S. DOE displayed both the Beech King AIR B-200 platform equipped to provide first response to nuclear accidents and routine environmental surveillance, and the MBB BO-105 helicopter used in spectral analysis for environmental assessment and military appraisal. NASA Ames Research Center's high-altitude Lockheed ER-2 assists in earth resources monitoring research in atmospheric chemistry, oceanography, and electronic sensors; ozone and greenhouse studies and satellite calibration and data validation. Ames also showcased the Learjet 24 Airborne Observatory that completed missions in Venus

A preliminary report of multispectral scanner data from the Cleveland harbor study

NASA Technical Reports Server (NTRS)

Shook, D.; Raquet, C.; Svehla, R.; Wachter, D.; Salzman, J.; Coney, T.; Gedney, D.

1975-01-01

Imagery obtained from an airborne multispectral scanner is presented. A synoptic view of the entire study area is shown for a number of time periods and for a number of spectral bands. Using several bands, sediment distributions, thermal plumes, and Rhodamine B dye distributions are shown.

Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

PubMed

Varga, Timothy A; Asner, Gregory P

2008-04-01

Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread.

Using Hyperspectral Imagery to Identify Turfgrass Stresses

NASA Technical Reports Server (NTRS)

Hutto, Kendall; Shaw, David

2008-01-01

The use of a form of remote sensing to aid in the management of large turfgrass fields (e.g. golf courses) has been proposed. A turfgrass field of interest would be surveyed in sunlight by use of an airborne hyperspectral imaging system, then the raw observational data would be preprocessed into hyperspectral reflectance image data. These data would be further processed to identify turfgrass stresses, to determine the spatial distributions of those stresses, and to generate maps showing the spatial distributions. Until now, chemicals and water have often been applied, variously, (1) indiscriminately to an entire turfgrass field without regard to localization of specific stresses or (2) to visible and possibly localized signs of stress for example, browning, damage from traffic, or conspicuous growth of weeds. Indiscriminate application is uneconomical and environmentally unsound; the amounts of water and chemicals consumed could be insufficient in some areas and excessive in most areas, and excess chemicals can leak into the environment. In cases in which developing stresses do not show visible signs at first, it could be more economical and effective to take corrective action before visible signs appear. By enabling early identification of specific stresses and their locations, the proposed method would provide guidance for planning more effective, more economical, and more environmentally sound turfgrass-management practices, including application of chemicals and water, aeration, and mowing. The underlying concept of using hyperspectral imagery to generate stress maps as guides to efficient management of vegetation in large fields is not new; it has been applied in the growth of crops to be harvested. What is new here is the effort to develop an algorithm that processes hyperspectral reflectance data into spectral indices specific to stresses in turfgrass. The development effort has included a study in which small turfgrass plots that were, variously, healthy or

Atmospheric correction of short-wave hyperspectral imagery using a fast, full-scattering 1DVar retrieval scheme

NASA Astrophysics Data System (ADS)

Thelen, J.-C.; Havemann, S.; Taylor, J. P.

2012-06-01

Here, we present a new prototype algorithm for the simultaneous retrieval of the atmospheric profiles (temperature, humidity, ozone and aerosol) and the surface reflectance from hyperspectral radiance measurements obtained from air/space-borne, hyperspectral imagers such as the 'Airborne Visible/Infrared Imager (AVIRIS) or Hyperion on board of the Earth Observatory 1. The new scheme, proposed here, consists of a fast radiative transfer code, based on empirical orthogonal functions (EOFs), in conjunction with a 1D-Var retrieval scheme. The inclusion of an 'exact' scattering code based on spherical harmonics, allows for an accurate treatment of Rayleigh scattering and scattering by aerosols, water droplets and ice-crystals, thus making it possible to also retrieve cloud and aerosol optical properties, although here we will concentrate on non-cloudy scenes. We successfully tested this new approach using two hyperspectral images taken by AVIRIS, a whiskbroom imaging spectrometer operated by the NASA Jet Propulsion Laboratory.

Early detection of toxigenic fungi on maize by hyperspectral imaging analysis.

PubMed

Del Fiore, A; Reverberi, M; Ricelli, A; Pinzari, F; Serranti, S; Fabbri, A A; Bonifazi, G; Fanelli, C

2010-11-15

Fungi can grow on many food commodities. Some fungal species, such as Aspergillus flavus, Aspergillus parasiticus, Aspergillus niger and Fusarium spp., can produce, under suitable conditions, mycotoxins, secondary metabolites which are toxic for humans and animals. Toxigenic fungi are a real issue, especially for the cereal industry. The aim of this work is to carry out a non destructive, hyperspectral imaging-based method to detect toxigenic fungi on maize kernels, and to discriminate between healthy and diseased kernels. A desktop spectral scanner equipped with an imaging based spectrometer ImSpector- Specim V10, working in the visible-near infrared spectral range (400-1000 nm) was used. The results show that the hyperspectral imaging is able to rapidly discriminate commercial maize kernels infected with toxigenic fungi from uninfected controls when traditional methods are not yet effective: i.e. from 48 h after inoculation with A. niger or A. flavus. Copyright © 2010 Elsevier B.V. All rights reserved.

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

Analysis of hyper-spectral AVIRIS image data over a mixed-conifer forest in Maine

NASA Technical Reports Server (NTRS)

Lawrence, William T.; Shimabukuro, Yosio E.; Gao, Bo-Cai

1993-01-01

An introduction to some of the potential uses of hyperspectral data for ecosystem analysis is presented. The examples given are derived from a digital dataset acquired over a sub-boreal forest in central Maine in 1990 by the NASA-JPL Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument gathers data from 400 to 2500 nm in 224 channels at bandwidths of approximately 10 nm. As a preview to the uses of the hyperspectral data, several products from this dataset were extracted. They range from the traditional false color composite made from simulated Thematic Mapper bands and the well known normalized difference vegetation index to much more exotic products such as fractions of vegetation, soil and shade based on linear spectral mixing models and estimates of the leaf water content at the landscape level derived using spectrum-matching techniques. Our research and that of many others indicates that the hyperspectral datasets carry much important information which is only beginning to be understood. This analysis gives an initial indication of the utility of hyperspectral data. Much work still remains to be done in algorithm development and in understanding the physics behind the complex information signal carried in the hyperspectral datasets. This work must be carried out to provide the fullest science support for high spectral resolution data to be acquired by many of the instruments to be launched as part of the Earth Observing System program in the mid-1990's.

LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas

NASA Astrophysics Data System (ADS)

Dekker, Rob J.; Schwering, Piet B. W.; Benoist, Koen W.; Pignatti, Stefano; Santini, Federico; Friman, Ola

2013-05-01

This paper studies change detection of LWIR (Long Wave Infrared) hyperspectral imagery. Goal is to improve target acquisition and situation awareness in urban areas with respect to conventional techniques. Hyperspectral and conventional broadband high-spatial-resolution data were collected during the DUCAS trials in Zeebrugge, Belgium, in June 2011. LWIR data were acquired using the ITRES Thermal Airborne Spectrographic Imager TASI-600 that operates in the spectral range of 8.0-11.5 μm (32 band configuration). Broadband data were acquired using two aeroplanemounted FLIR SC7000 MWIR cameras. Acquisition of the images was around noon. To limit the number of false alarms due to atmospheric changes, the time interval between the images is less than 2 hours. Local co-registration adjustment was applied to compensate for misregistration errors in the order of a few pixels. The targets in the data that will be analysed in this paper are different kinds of vehicles. Change detection algorithms that were applied and evaluated are Euclidean distance, Mahalanobis distance, Chronochrome (CC), Covariance Equalisation (CE), and Hyperbolic Anomalous Change Detection (HACD). Based on Receiver Operating Characteristics (ROC) we conclude that LWIR hyperspectral has an advantage over MWIR broadband change detection. The best hyperspectral detector is HACD because it is most robust to noise. MWIR high spatial-resolution broadband results show that it helps to apply a false alarm reduction strategy based on spatial processing.

Use of airborne hyperspectral data to estimate residual heavy metal contamination and acidification potential in the Guadiamar floodplain Andalusia, Spain after the Aznacollar mining accident

NASA Astrophysics Data System (ADS)

Kemper, Thomas; Sommer, Stefan

2004-10-01

Field and airborne hyperspectral data was used to map residual contamination after a mining accident, by applying spectral mixture modelling. Test case was the Aznalcollar Mine (Southern Spain) accident, where heavy metal bearing sludge from a tailings pond was distributed over large areas of the Guadiamar flood plain. Although the sludge and the contaminated topsoils have been removed mechanically in the whole affected area, still high abundance of pyritic material remained on the ground. During dedicated field campaigns in two subsequent years soil samples were collected for geochemical and spectral laboratory analysis and spectral field measurements were carried out in parallel to data acquisition with the HyMap sensor. A Variable Multiple Endmember Spectral Mixture Analysis (VMESMA) tool was used providing possibilities of multiple endmember unmixing, aiming to estimate the quantities and distribution of the remaining tailings material. A spectrally based zonal partition of the area was introduced to allow the application of different submodels to the selected areas. Based on an iterative feedback process, the unmixing performance could be improved in each stage until an optimum level was reached. The sludge abundances obtained by unmixing the hyperspectral spectral data were confirmed by the field observations and chemical measurements of samples taken in the area. The semi-quantitative sludge abundances of residual pyritic material could be transformed into quantitative information for an assessment of acidification risk and distribution of residual heavy metal contamination based on an artificial mixture experiment. The unmixing of the second year images allowed identification of secondary minerals of pyrite as indicators of pyrite oxidation and associated acidification.

Hyperspectral signatures and WorldView-3 imagery of Indian River Lagoon and Banana River Estuarine water and bottom types

NASA Astrophysics Data System (ADS)

Bostater, Charles R.; Oney, Taylor S.; Rotkiske, Tyler; Aziz, Samin; Morrisette, Charles; Callahan, Kelby; Mcallister, Devin

2017-10-01

Hyperspectral signatures and imagery collected during the spring and summer of 2017 and 2016 are presented. Ground sampling distances (GSD) and pixel sizes were sampled from just over a meter to less than 4.0 mm. A pushbroom hyperspectral imager was used to calculate bidirectional reflectance factor (BRF) signatures. Hyperspectral signatures of different water types and bottom habitats such as submerged seagrasses, drift algae and algal bloom waters were scanned using a high spectral and digital resolution solid state spectrograph. WorldView-3 satellite imagery with minimal water wave sun glint effects was used to demonstrate the ability to detect bottom features using a derivative reflectance spectroscopy approach with the 1.3 m GSD multispectral satellite channels centered at the solar induced fluorescence band. The hyperspectral remote sensing data collected from the Banana River and Indian River Lagoon watersheds represents previously unknown signatures to be used in satellite and airborne remote sensing of water in turbid waters along the US Atlantic Ocean coastal region and the Florida littoral zone.

Seagrass Identification Using High-Resolution 532nm Bathymetric LiDAR and Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Pan, Z.; Prasad, S.; Starek, M. J.; Fernandez Diaz, J. C.; Glennie, C. L.; Carter, W. E.; Shrestha, R. L.; Singhania, A.; Gibeaut, J. C.

2013-12-01

Seagrass provides vital habitat for marine fisheries and is a key indicator species of coastal ecosystem vitality. Monitoring seagrass is therefore an important environmental initiative, but measuring details of seagrass distribution over large areas via remote sensing has proved challenging. Developments in airborne bathymetric light detection and ranging (LiDAR) provide great potential in this regard. Traditional bathymetric LiDAR systems have been limited in their ability to map within the shallow water zone (< 1 m) where seagrass is typically present due to limitations in receiver response and laser pulse length. Emergent short-pulse width bathymetric LiDAR sensors and waveform processing algorithms enable depth measurements in shallow water environments previously inaccessible. This 3D information of the benthic layer can be applied to detect seagrass and characterize its distribution. Researchers with the National Center for Airborne Laser Mapping (NCALM) at the University of Houston (UH) and the Coastal and Marine Geospatial Sciences Lab (CMGL) of the Harte Research Institute at Texas A&M University-Corpus Christi conducted a coordinated airborne and boat-based survey of the Redfish Bay State Scientific Area as part of a collaborative study to investigate the capabilities of bathymetric LiDAR and hyperspectral imaging for seagrass mapping. Redfish Bay, located along the middle Texas coast of the Gulf of Mexico, is a state scientific area designated for the purpose of protecting and studying native seagrasses. Redfish Bay is part of the broader Coastal Bend Bays estuary system recognized by the US Environmental Protection Agency (EPA) as a national estuary of significance. For this survey, UH acquired high-resolution discrete-return and full-waveform bathymetric data using their Optech Aquarius 532 nm green LiDAR. In a separate flight, UH collected 2 sets of hyperspectral imaging data (1.2-m pixel resolution and 72 bands, and 0.6m pixel resolution and 36

Postfire soil burn severity mapping with hyperspectral image unmixing

USGS Publications Warehouse

Robichaud, P.R.; Lewis, S.A.; Laes, D.Y.M.; Hudak, A.T.; Kokaly, R.F.; Zamudio, J.A.

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, Gábor; Yule, I. J.

2016-07-01

On-farm assessment of mixed pasture nutrient concentrations is important for animal production and pasture management. Hyperspectral imaging is recognized as a potential tool to quantify the nutrient content of vegetation. However, it is a great challenge to estimate macro and micro nutrients in heterogeneous mixed pastures. In this study, canopy reflectance data was measured by using a high resolution airborne visible-to-shortwave infrared (Vis-SWIR) imaging spectrometer measuring in the wavelength region 380-2500 nm to predict nutrient concentrations, nitrogen (N) phosphorus (P), potassium (K), sulfur (S), zinc (Zn), sodium (Na), manganese (Mn) copper (Cu) and magnesium (Mg) in heterogeneous mixed pastures across a sheep and beef farm in hill country, within New Zealand. Prediction models were developed using four different methods which are included partial least squares regression (PLSR), kernel PLSR, support vector regression (SVR), random forest regression (RFR) algorithms and their performance compared using the test data. The results from the study revealed that RFR produced highest accuracy (0.55 ⩽ R2CV ⩽ 0.78; 6.68% ⩽ nRMSECV ⩽ 26.47%) compared to all other algorithms for the majority of nutrients (N, P, K, Zn, Na, Cu and Mg) described, and the remaining nutrients (S and Mn) were predicted with high accuracy (0.68 ⩽ R2CV ⩽ 0.86; 13.00% ⩽ nRMSECV ⩽ 14.64%) using SVR. The best training models were used to extrapolate over the whole farm with the purpose of predicting those pasture nutrients and expressed through pixel based spatial maps. These spatially registered nutrient maps demonstrate the range and geographical location of often large differences in pasture nutrient values which are normally not measured and therefore not included in decision making when considering more effective ways to utilized pasture.

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

NASA Technical Reports Server (NTRS)

Green, Robert O. (Editor)

1992-01-01

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively.

Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land

PubMed Central

Bassani, Cristiana; Cavalli, Rosa Maria; Pignatti, Stefano

2010-01-01

Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550nm (τ550) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ550 with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ550 and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ550 retrieved by Module A (r2 = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r2 ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness. PMID:22163558

Hyperspectral imaging—An advanced instrument concept for the EnMAP mission (Environmental Mapping and Analysis Programme)

NASA Astrophysics Data System (ADS)

Stuffler, Timo; Förster, Klaus; Hofer, Stefan; Leipold, Manfred; Sang, Bernhard; Kaufmann, Hermann; Penné, Boris; Mueller, Andreas; Chlebek, Christian

2009-10-01

In the upcoming generation of satellite sensors, hyperspectral instruments will play a significant role. This payload type is considered world-wide within different future planning. Our team has now successfully finalized the Phase B study for the advanced hyperspectral mission EnMAP (Environmental Mapping and Analysis Programme), Germans next optical satellite being scheduled for launch in 2012. GFZ in Potsdam has the scientific lead on EnMAP, Kayser-Threde in Munich is the industrial prime. The EnMAP instrument provides over 240 continuous spectral bands in the wavelength range between 420 and 2450 nm with a ground resolution of 30 m×30 m. Thus, the broad science and application community can draw from an extensive and highly resolved pool of information supporting the modeling and optimization process on their results. The performance of the hyperspectral instrument allows for a detailed monitoring, characterization and parameter extraction of rock/soil targets, vegetation, and inland and coastal waters on a global scale supporting a wide variety of applications in agriculture, forestry, water management and geology. The operation of an airborne system (ARES) as an element in the HGF hyperspectral network and the ongoing evolution concerning data handling and extraction procedures, will support the later inclusion process of EnMAP into the growing scientist and user communities.

Hyper-spectral Atmospheric Sounding. Appendixes 1

NASA Technical Reports Server (NTRS)

Smith, W. L.; Zhou, D. K.; Revercomb, H. E.; Huang, H. L.; Antonelli, P.; Mango, S. A.

2002-01-01

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) is the first hyper-spectral remote sounding system to be orbited aboard a geosynchronous satellite. The GETS is designed to obtain revolutionary observations of the four dimensional atmospheric temperature, moisture, and wind structure as well as the distribution of the atmospheric trace gases, CO and O3. Although GIFTS will not be orbited until 2006-2008, a glimpse at the its measurement capabilities has been obtained by analyzing data from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Test-bed-Interferometer (NAST-I) and Aqua satellite Atmospheric Infrared Sounder (AIRS). In this paper we review the GIFTS experiment and empirically assess measurement expectations based on meteorological profiles retrieved from the NAST aircraft and Aqua satellite AIRS spectral radiances.

Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 3: AIRSAR Workshop

NASA Technical Reports Server (NTRS)

Vanzyl, Jakob (Editor)

1993-01-01

This publication contains the summaries for the Fourth Annual JPL Airborne Geoscience Workshop, held in Washington, D.C. on October 25-29, 1993. The main workshop is divided into three smaller workshops as follows: The Airborne Visible/Infrared Spectrometer (AVIRIS) workshop, on October 25-26, whose summaries appear in Volume 1; The Thermal Infrared Multispectral Scanner (TIMS) workshop, on October 27, whose summaries appear in Volume 2; and The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on October 28-29, whose summaries appear in this volume, Volume 3.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data

PubMed Central

Vanegas, Fernando; Weiss, John; Gonzalez, Felipe

2018-01-01

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analysing and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications. PMID:29342101

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data.

PubMed

Vanegas, Fernando; Bratanov, Dmitry; Powell, Kevin; Weiss, John; Gonzalez, Felipe

2018-01-17

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used-the sensors, the UAV, and the flight operations-the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

NASA Technical Reports Server (NTRS)

Cook, Bruce; Corp, Lawrence; Nelson, Ross; Morton, Douglas; Ranson, Kenneth J.; Masek, Jeffrey; Middleton, Elizabeth

2012-01-01

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation

Airborne ultrasound applied to anthropometry--physical and technical principles.

PubMed

Lindström, K; Mauritzson, L; Benoni, G; Willner, S

1983-01-01

Airborne ultrasound has been utilized for remote measurement of distance, direction, size, form, volume and velocity. General anthropometrical measurements are performed with a newly constructed real-time linear array scanner. To make full use of the method, we expect a rapid development of high-frequency ultrasound transducers for use in air.

Airborne camera and spectrometer experiments and data evaluation

NASA Astrophysics Data System (ADS)

Lehmann, F. F.; Bucher, T.; Pless, S.; Wohlfeil, J.; Hirschmüller, H.

2009-09-01

New stereo push broom camera systems have been developed at German Aerospace Centre (DLR). The new small multispectral systems (Multi Functional Camerahead - MFC, Advanced Multispectral Scanner - AMS) are light weight, compact and display three or five RGB stereo lines of 8000, 10 000 or 14 000 pixels, which are used for stereo processing and the generation of Digital Surface Models (DSM) and near True Orthoimage Mosaics (TOM). Simultaneous acquisition of different types of MFC-cameras for infrared and RGB data has been successfully tested. All spectral channels record the image data in full resolution, pan-sharpening is not necessary. Analogue to the line scanner data an automatic processing chain for UltraCamD and UltraCamX exists. The different systems have been flown for different types of applications; main fields of interest among others are environmental applications (flooding simulations, monitoring tasks, classification) and 3D-modelling (e.g. city mapping). From the DSM and TOM data Digital Terrain Models (DTM) and 3D city models are derived. Textures for the facades are taken from oblique orthoimages, which are created from the same input data as the TOM and the DOM. The resulting models are characterised by high geometric accuracy and the perfect fit of image data and DSM. The DLR is permanently developing and testing a wide range of sensor types and imaging platforms for terrestrial and space applications. The MFC-sensors have been flown in combination with laser systems and imaging spectrometers and special data fusion products have been developed. These products include hyperspectral orthoimages and 3D hyperspectral data.

Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

NASA Technical Reports Server (NTRS)

Blonksi, Slawomir; Gasser, Gerald; Russell, Jeffrey; Ryan, Robert; Terrie, Greg; Zanoni, Vicki

2001-01-01

Multispectral data requirements for Earth science applications are not always studied rigorously studied before a new remote sensing system is designed. A study of the spatial resolution, spectral bandpasses, and radiometric sensitivity requirements of real-world applications would focus the design onto providing maximum benefits to the end-user community. To support systematic studies of multispectral data requirements, the Applications Research Toolbox (ART) has been developed at NASA's Stennis Space Center. The ART software allows users to create and assess simulated datasets while varying a wide range of system parameters. The simulations are based on data acquired by existing multispectral and hyperspectral instruments. The produced datasets can be further evaluated for specific end-user applications. Spectral synthesis of multispectral images from hyperspectral data is a key part of the ART software. In this process, hyperspectral image cubes are transformed into multispectral imagery without changes in spatial sampling and resolution. The transformation algorithm takes into account spectral responses of both the synthesized, broad, multispectral bands and the utilized, narrow, hyperspectral bands. To validate the spectral synthesis algorithm, simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments. Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations.

Summaries of the Seventh JPL Airborne Earth Science Workshop January 12-16, 1998. Volume 1; AVIRIS Workshop

NASA Technical Reports Server (NTRS)

Green, Robert O. (Editor)

1998-01-01

This publication contains the summaries for the Seventh JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 12-16, 1998. The main workshop is divided into three smaller workshops, and each workshop has a volume as follows: (1) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop; (2) Airborne Synthetic Aperture Radar (AIRSAR) Workshop; and (3) Thermal Infrared Multispectral Scanner (TIMS) Workshop. This Volume 1 publication contains 58 papers taken from the AVIRIS workshop.

Hyperspectral imaging flow cytometer

DOEpatents

Sinclair, Michael B.; Jones, Howland D. T.

2017-10-25

A hyperspectral imaging flow cytometer can acquire high-resolution hyperspectral images of particles, such as biological cells, flowing through a microfluidic system. The hyperspectral imaging flow cytometer can provide detailed spatial maps of multiple emitting species, cell morphology information, and state of health. An optimized system can image about 20 cells per second. The hyperspectral imaging flow cytometer enables many thousands of cells to be characterized in a single session.

Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

NASA Technical Reports Server (NTRS)

Green, Robert O. (Editor)

1993-01-01

This publication contains the summaries for the Fourth Annual JPL Airborne Geoscience Workshop, held in Washington, D. C. October 25-29, 1993 The main workshop is divided into three smaller workshops as follows: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, October 25-26 (the summaries for this workshop appear in this volume, Volume 1); The Thermal Infrared Multispectral Scanner (TMIS) workshop, on October 27 (the summaries for this workshop appear in Volume 2); and The Airborne Synthetic Aperture Radar (AIRSAR) workshop, October 28-29 (the summaries for this workshop appear in Volume 3).

Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

NASA Technical Reports Server (NTRS)

Vermillion, Stephanie C.; Raqueno, Rolando; Simmons, Rulon

2001-01-01

A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

Modified algorithm for mineral identification in LWIR hyperspectral imagery

NASA Astrophysics Data System (ADS)

Yousefi, Bardia; Sojasi, Saeed; Liaigre, Kévin; Ibarra Castanedo, Clemente; Beaudoin, Georges; Huot, François; Maldague, Xavier P. V.; Chamberland, Martin

2017-05-01

The applications of hyperspectral infrared imagery in the different fields of research are significant and growing. It is mainly used in remote sensing for target detection, vegetation detection, urban area categorization, astronomy and geological applications. The geological applications of this technology mainly consist in mineral identification using in airborne or satellite imagery. We address a quantitative and qualitative assessment of mineral identification in the laboratory conditions. We strive to identify nine different mineral grains (Biotite, Diopside, Epidote, Goethite, Kyanite, Scheelite, Smithsonite, Tourmaline, Quartz). A hyperspectral camera in the Long Wave Infrared (LWIR, 7.7-11.8 ) with a LW-macro lens providing a spatial resolution of 100 μm, an infragold plate, and a heating source are the instruments used in the experiment. The proposed algorithm clusters all the pixel-spectra in different categories. Then the best representatives of each cluster are chosen and compared with the ASTER spectral library of JPL/NASA through spectral comparison techniques, such as Spectral angle mapper (SAM) and Normalized Cross Correlation (NCC). The results of the algorithm indicate significant computational efficiency (more than 20 times faster) as compared to previous algorithms and have shown a promising performance for mineral identification.

Airborne multidimensional integrated remote sensing system

NASA Astrophysics Data System (ADS)

Xu, Weiming; Wang, Jianyu; Shu, Rong; He, Zhiping; Ma, Yanhua

2006-12-01

In this paper, we present a kind of airborne multidimensional integrated remote sensing system that consists of an imaging spectrometer, a three-line scanner, a laser ranger, a position & orientation subsystem and a stabilizer PAV30. The imaging spectrometer is composed of two sets of identical push-broom high spectral imager with a field of view of 22°, which provides a field of view of 42°. The spectral range of the imaging spectrometer is from 420nm to 900nm, and its spectral resolution is 5nm. The three-line scanner is composed of two pieces of panchromatic CCD and a RGB CCD with 20° stereo angle and 10cm GSD(Ground Sample Distance) with 1000m flying height. The laser ranger can provide height data of three points every other four scanning lines of the spectral imager and those three points are calibrated to match the corresponding pixels of the spectral imager. The post-processing attitude accuracy of POS/AV 510 used as the position & orientation subsystem, which is the aerial special exterior parameters measuring product of Canadian Applanix Corporation, is 0.005° combined with base station data. The airborne multidimensional integrated remote sensing system was implemented successfully, performed the first flying experiment on April, 2005, and obtained satisfying data.

Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

PubMed Central

de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

2012-01-01

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops. PMID:22629171

Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops.

PubMed

de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

2012-01-01

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

Sparse graph regularization for robust crop mapping using hyperspectral remotely sensed imagery with very few in situ data

NASA Astrophysics Data System (ADS)

Xue, Zhaohui; Du, Peijun; Li, Jun; Su, Hongjun

2017-02-01

The generally limited availability of training data relative to the usually high data dimension pose a great challenge to accurate classification of hyperspectral imagery, especially for identifying crops characterized with highly correlated spectra. However, traditional parametric classification models are problematic due to the need of non-singular class-specific covariance matrices. In this research, a novel sparse graph regularization (SGR) method is presented, aiming at robust crop mapping using hyperspectral imagery with very few in situ data. The core of SGR lies in propagating labels from known data to unknown, which is triggered by: (1) the fraction matrix generated for the large unknown data by using an effective sparse representation algorithm with respect to the few training data serving as the dictionary; (2) the prediction function estimated for the few training data by formulating a regularization model based on sparse graph. Then, the labels of large unknown data can be obtained by maximizing the posterior probability distribution based on the two ingredients. SGR is more discriminative, data-adaptive, robust to noise, and efficient, which is unique with regard to previously proposed approaches and has high potentials in discriminating crops, especially when facing insufficient training data and high-dimensional spectral space. The study area is located at Zhangye basin in the middle reaches of Heihe watershed, Gansu, China, where eight crop types were mapped with Compact Airborne Spectrographic Imager (CASI) and Shortwave Infrared Airborne Spectrogrpahic Imager (SASI) hyperspectral data. Experimental results demonstrate that the proposed method significantly outperforms other traditional and state-of-the-art methods.

Hyperspectral image processing methods

USDA-ARS?s Scientific Manuscript database

Hyperspectral image processing refers to the use of computer algorithms to extract, store and manipulate both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. A typical hyperspectral image processing work...

Informed Source Separation of Atmospheric and Surface Signal Contributions in Shortwave Hyperspectral Imagery using Non-negative Matrix Factorization

NASA Astrophysics Data System (ADS)

Wright, L.; Coddington, O.; Pilewskie, P.

2015-12-01

Current challenges in Earth remote sensing require improved instrument spectral resolution, spectral coverage, and radiometric accuracy. Hyperspectral instruments, deployed on both aircraft and spacecraft, are a growing class of Earth observing sensors designed to meet these challenges. They collect large amounts of spectral data, allowing thorough characterization of both atmospheric and surface properties. The higher accuracy and increased spectral and spatial resolutions of new imagers require new numerical approaches for processing imagery and separating surface and atmospheric signals. One potential approach is source separation, which allows us to determine the underlying physical causes of observed changes. Improved signal separation will allow hyperspectral instruments to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. In this work, we investigate a Non-negative Matrix Factorization (NMF) method for the separation of atmospheric and land surface signal sources. NMF offers marked benefits over other commonly employed techniques, including non-negativity, which avoids physically impossible results, and adaptability, which allows the method to be tailored to hyperspectral source separation. We adapt our NMF algorithm to distinguish between contributions from different physically distinct sources by introducing constraints on spectral and spatial variability and by using library spectra to inform separation. We evaluate our NMF algorithm with simulated hyperspectral images as well as hyperspectral imagery from several instruments including, the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), NASA Hyperspectral Imager for the Coastal Ocean (HICO) and National Ecological Observatory Network (NEON) Imaging Spectrometer.

A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm

NASA Astrophysics Data System (ADS)

Tomljenovic, Ivan; Tiede, Dirk; Blaschke, Thomas

2016-10-01

In the past two decades Object-Based Image Analysis (OBIA) established itself as an efficient approach for the classification and extraction of information from remote sensing imagery and, increasingly, from non-image based sources such as Airborne Laser Scanner (ALS) point clouds. ALS data is represented in the form of a point cloud with recorded multiple returns and intensities. In our work, we combined OBIA with ALS point cloud data in order to identify and extract buildings as 2D polygons representing roof outlines in a top down mapping approach. We performed rasterization of the ALS data into a height raster for the purpose of the generation of a Digital Surface Model (DSM) and a derived Digital Elevation Model (DEM). Further objects were generated in conjunction with point statistics from the linked point cloud. With the use of class modelling methods, we generated the final target class of objects representing buildings. The approach was developed for a test area in Biberach an der Riß (Germany). In order to point out the possibilities of the adaptation-free transferability to another data set, the algorithm has been applied ;as is; to the ISPRS Benchmarking data set of Toronto (Canada). The obtained results show high accuracies for the initial study area (thematic accuracies of around 98%, geometric accuracy of above 80%). The very high performance within the ISPRS Benchmark without any modification of the algorithm and without any adaptation of parameters is particularly noteworthy.

Classification of Hyperspectral Data Based on Guided Filtering and Random Forest

NASA Astrophysics Data System (ADS)

Ma, H.; Feng, W.; Cao, X.; Wang, L.

2017-09-01

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to "the curse of dimensionality". In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

Thermal hyperspectral chemical imaging

NASA Astrophysics Data System (ADS)

Holma, Hannu; Hyvärinen, Timo; Mattila, Antti-Jussi; Kormano, Ilkka

2012-06-01

Several chemical compounds have their strongest spectral signatures in the thermal region. This paper presents three push-broom thermal hyperspectral imagers. The first operates in MWIR (2.8-5 μm) with 35 nm spectral resolution. It consists of uncooled imaging spectrograph and cryogenically cooled InSb camera, with spatial resolution of 320/640 pixels and image rate to 400 Hz. The second imager covers LWIR in 7.6-12 μm with 32 spectral bands. It employs an uncooled microbolometer array and spectrograph. These imagers have been designed for chemical mapping in reflection mode in industry and laboratory. An efficient line-illumination source has been developed, and it makes possible thermal hyperspectral imaging in reflection with much higher signal and SNR than is obtained from room temperature emission. Application demonstrations including sorting of dark plastics and mineralogical mapping of drill cores are presented. The third imager utilizes a cryo-cooled MCT array with precisely temperature stabilized optics. The optics is not cooled, but instrument radiation is suppressed by special filtering and corrected by BMC (Background-Monitoring-on-Chip) method. The approach provides excellent sensitivity in an instrument which is portable and compact enough for installation in UAVs. The imager has been verified in 7.6 to 12.3 μm to provide NESR of 18 mW/(m2 sr μm) at 10 μm for 300 K target with 100 spectral bands and 384 spatial samples. It results in SNR of higher than 500. The performance makes possible various applications from gas detection to mineral exploration and vegetation surveys. Results from outdoor and airborne experiments are shown.

A hyperspectral image projector for hyperspectral imagers

NASA Astrophysics Data System (ADS)

Rice, Joseph P.; Brown, Steven W.; Neira, Jorge E.; Bousquet, Robert R.

2007-04-01

We have developed and demonstrated a Hyperspectral Image Projector (HIP) intended for system-level validation testing of hyperspectral imagers, including the instrument and any associated spectral unmixing algorithms. HIP, based on the same digital micromirror arrays used in commercial digital light processing (DLP*) displays, is capable of projecting any combination of many different arbitrarily programmable basis spectra into each image pixel at up to video frame rates. We use a scheme whereby one micromirror array is used to produce light having the spectra of endmembers (i.e. vegetation, water, minerals, etc.), and a second micromirror array, optically in series with the first, projects any combination of these arbitrarily-programmable spectra into the pixels of a 1024 x 768 element spatial image, thereby producing temporally-integrated images having spectrally mixed pixels. HIP goes beyond conventional DLP projectors in that each spatial pixel can have an arbitrary spectrum, not just arbitrary color. As such, the resulting spectral and spatial content of the projected image can simulate realistic scenes that a hyperspectral imager will measure during its use. Also, the spectral radiance of the projected scenes can be measured with a calibrated spectroradiometer, such that the spectral radiance projected into each pixel of the hyperspectral imager can be accurately known. Use of such projected scenes in a controlled laboratory setting would alleviate expensive field testing of instruments, allow better separation of environmental effects from instrument effects, and enable system-level performance testing and validation of hyperspectral imagers as used with analysis algorithms. For example, known mixtures of relevant endmember spectra could be projected into arbitrary spatial pixels in a hyperspectral imager, enabling tests of how well a full system, consisting of the instrument + calibration + analysis algorithm, performs in unmixing (i.e. de-convolving) the

EnGeoMAP - geological applications within the EnMAP hyperspectral satellite science program

NASA Astrophysics Data System (ADS)

Boesche, N. K.; Mielke, C.; Rogass, C.; Guanter, L.

2016-12-01

Hyperspectral investigations from near field to space substantially contribute to geological exploration and mining monitoring of raw material and mineral deposits. Due to their spectral characteristics, large mineral occurrences and minefields can be identified from space and the spatial distribution of distinct proxy minerals be mapped. In the frame of the EnMAP hyperspectral satellite science program a mineral and elemental mapping tool was developed - the EnGeoMAP. It contains a basic mineral mapping and a rare earth element mapping approach. This study shows the performance of EnGeoMAP based on simulated EnMAP data of the rare earth element bearing Mountain Pass Carbonatite Complex, USA, and the Rodalquilar and Lomilla Calderas, Spain, which host the economically relevant gold-silver, lead-zinc-silver-gold and alunite deposits. The mountain pass image data was simulated on the basis of AVIRIS Next Generation images, while the Rodalquilar data is based on HyMap images. The EnGeoMAP - Base approach was applied to both images, while the mountain pass image data were additionally analysed using the EnGeoMAP - REE software tool. The results are mineral and elemental maps that serve as proxies for the regional lithology and deposit types. The validation of the maps is based on chemical analyses of field samples. Current airborne sensors meet the spatial and spectral requirements for detailed mineral mapping and future hyperspectral space borne missions will additionally provide a large coverage. For those hyperspectral missions, EnGeoMAP is a rapid data analysis tool that is provided to spectral geologists working in mineral exploration.

Image quality measures to assess hyperspectral compression techniques

NASA Astrophysics Data System (ADS)

Lurie, Joan B.; Evans, Bruce W.; Ringer, Brian; Yeates, Mathew

1994-12-01

The term 'multispectral' is used to describe imagery with anywhere from three to about 20 bands of data. The images acquired by Landsat and similar earth sensing satellites including the French Spot platform are typical examples of multispectral data sets. Applications range from crop observation and yield estimation, to forestry, to sensing of the environment. The wave bands typically range from the visible to thermal infrared and are fractions of a micron wide. They may or may not be contiguous. Thus each pixel will have several spectral intensities associated with it but detailed spectra are not obtained. The term 'hyperspectral' is typically used for spectral data encompassing hundreds of samples of a spectrum. Hyperspectral, electro-optical sensors typically operate in the visible and near infrared bands. Their characteristic property is the ability to resolve a large number (typically hundreds) of contiguous spectral bands, thus producing a detailed profile of the electromagnetic spectrum. Like multispectral sensors, recently developed hyperspectral sensors are often also imaging sensors, measuring spectral over a two dimensional spatial array of picture elements of pixels. The resulting data is thus inherently three dimensional - an array of samples in which two dimensions correspond to spatial position and the third to wavelength. The data sets, commonly referred to as image cubes or datacubes (although technically they are often rectangular solids), are very rich in information but quickly become unwieldy in size, generating formidable torrents of data. Both spaceborne and airborne hyperspectral cameras exist and are in use today. The data is unique in its ability to provide high spatial and spectral resolution simultaneously, and shows great promise in both military and civilian applications. A data analysis system has been built at TRW under a series of Internal Research and Development projects. This development has been prompted by the business

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 3: AIRSAR Workshop

NASA Technical Reports Server (NTRS)

Vanzyl, Jakob (Editor)

1995-01-01

This publication is the third containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in Volume 1; (2) The Airborne synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in this volume; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2.

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 1: AVIRIS Workshop

NASA Technical Reports Server (NTRS)

Green, Robert O. (Editor)

1995-01-01

This publication is the first of three containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in this volume; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in Volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2.

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 2: TIMS Workshop

NASA Technical Reports Server (NTRS)

Realmuto, Vincent J. (Editor)

1995-01-01

This publication is the second volume of the summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop on January 23-24. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop on January 25-26. The summaries for this workshop appear in volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop on January 26. The summaries for this workshop appear in this volume.

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU.

PubMed

Giordano, Rossella; Guccione, Pietro

2017-05-19

In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA's CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target's related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area.

Hyperspectral target detection using manifold learning and multiple target spectra

DOE PAGES

Ziemann, Amanda K.; Theiler, James; Messinger, David W.

2016-03-31

Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform these detection analyses. For a d-dimensional hyperspectral image, however, where d is the number of spectral bands, it is common for the data to inherently occupy an m-dimensional space with m << d. In the remote sensing community, this has led to recent interestmore » in the use of manifold learning, which seeks to characterize the embedded lower-dimensional, nonlinear manifold that the data discretely approximate. The research presented in this paper focuses on a graph theory and manifold learning approach to target detection, using an adaptive version of locally linear embedding that is biased to separate target pixels from background pixels. Finally, this approach incorporates multiple target signatures for a particular material, accounting for the spectral variability that is often present within a solid material of interest.« less

Optimization of spectral bands for hyperspectral remote sensing of forest vegetation

NASA Astrophysics Data System (ADS)

Dmitriev, Egor V.; Kozoderov, Vladimir V.

2013-10-01

Optimization principles of accounting for the most informative spectral channels in hyperspectral remote sensing data processing serve to enhance the efficiency of the employed high-productive computers. The problem of pattern recognition of the remotely sensed land surface objects with the accent on the forests is outlined from the point of view of the spectral channels optimization on the processed hyperspectral images. The relevant computational procedures are tested using the images obtained by the produced in Russia hyperspectral camera that was installed on a gyro-stabilized platform to conduct the airborne flight campaigns. The Bayesian classifier is used for the pattern recognition of the forests with different tree species and age. The probabilistically optimal algorithm constructed on the basis of the maximum likelihood principle is described to minimize the probability of misclassification given by this classifier. The classification error is the major category to estimate the accuracy of the applied algorithm by the known holdout cross-validation method. Details of the related techniques are presented. Results are shown of selecting the spectral channels of the camera while processing the images having in mind radiometric distortions that diminish the classification accuracy. The spectral channels are selected of the obtained subclasses extracted from the proposed validation techniques and the confusion matrices are constructed that characterize the age composition of the classified pine species as well as the broad age-class recognition for the pine and birch species with the fully illuminated parts of their crowns.

On-orbit characterization of hyperspectral imagers

NASA Astrophysics Data System (ADS)

McCorkel, Joel

Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne- and satellite-based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This dissertation presents a method for determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on a multispectral sensor, Moderate-resolution Imaging Spectroradiometer (MODIS), as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. A method to predict hyperspectral surface reflectance using a combination of MODIS data and spectral shape information is developed and applied for the characterization of Hyperion. Spectral shape information is based on RSG's historical in situ data for the Railroad Valley test site and spectral library data for the Libyan test site. Average atmospheric parameters, also based on historical measurements, are used in reflectance prediction and transfer to space. Results of several cross-calibration scenarios that differ in image acquisition coincidence, test site, and reference sensor are found for the characterization of Hyperion. These are compared with results from the reflectance-based approach of vicarious calibration, a well-documented method developed by the RSG that serves as a baseline for calibration performance for the cross-calibration method developed here. Cross-calibration provides results that are within 2% of those of reflectance-based results in most spectral regions. Larger disagreements exist

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 2: TIMS Workshop

NASA Technical Reports Server (NTRS)

Realmuto, Vincent J. (Editor)

1992-01-01

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; the summaries for this workshop appear in Volume 1; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; the summaries for this workshop appear in Volume 2; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5; the summaries for this workshop appear in Volume 3.

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 3: AIRSAR Workshop

NASA Technical Reports Server (NTRS)

Vanzyl, Jakob (Editor)

1992-01-01

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; the summaries for this workshop appear in Volume 1; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; the summaries for this workshop appear in Volume 2; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5; the summaries for this workshop appear in Volume 3.

A novel scene-based non-uniformity correction method for SWIR push-broom hyperspectral sensors

NASA Astrophysics Data System (ADS)

Hu, Bin-Lin; Hao, Shi-Jing; Sun, De-Xin; Liu, Yin-Nian

2017-09-01

A novel scene-based non-uniformity correction (NUC) method for short-wavelength infrared (SWIR) push-broom hyperspectral sensors is proposed and evaluated. This method relies on the assumption that for each band there will be ground objects with similar reflectance to form uniform regions when a sufficient number of scanning lines are acquired. The uniform regions are extracted automatically through a sorting algorithm, and are used to compute the corresponding NUC coefficients. SWIR hyperspectral data from airborne experiment are used to verify and evaluate the proposed method, and results show that stripes in the scenes have been well corrected without any significant information loss, and the non-uniformity is less than 0.5%. In addition, the proposed method is compared to two other regular methods, and they are evaluated based on their adaptability to the various scenes, non-uniformity, roughness and spectral fidelity. It turns out that the proposed method shows strong adaptability, high accuracy and efficiency.

Active/passive scanning. [airborne multispectral laser scanners for agricultural and water resources applications

NASA Technical Reports Server (NTRS)

Woodfill, J. R.; Thomson, F. J.

1979-01-01

The paper deals with the design, construction, and applications of an active/passive multispectral scanner combining lasers with conventional passive remote sensors. An application investigation was first undertaken to identify remote sensing applications where active/passive scanners (APS) would provide improvement over current means. Calibration techniques and instrument sensitivity are evaluated to provide predictions of the APS's capability to meet user needs. A preliminary instrument design was developed from the initial conceptual scheme. A design review settled the issues of worthwhile applications, calibration approach, hardware design, and laser complement. Next, a detailed mechanical design was drafted and construction of the APS commenced. The completed APS was tested and calibrated in the laboratory, then installed in a C-47 aircraft and ground tested. Several flight tests completed the test program.

Hyperspectral sensing of forests

NASA Astrophysics Data System (ADS)

Goodenough, David G.; Dyk, Andrew; Chen, Hao; Hobart, Geordie; Niemann, K. Olaf; Richardson, Ash

2007-11-01

Canada contains 10% of the world's forests covering an area of 418 million hectares. The sustainable management of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of new and improved information products to resource managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory, forest health, foliar biochemistry, biomass, and aboveground carbon than are currently available. This paper surveys recent methods and results in hyperspectral sensing of forests and describes space initiatives for hyperspectral sensing.

A manifold learning approach to target detection in high-resolution hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ziemann, Amanda K.

Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m << d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into

Prototype Holographic Atmospheric Scanner for Environmental Remote Sensing (PHASERS)

NASA Technical Reports Server (NTRS)

Guerra, David V.; Schwemmer, Geary K.; Wooten, Albert D., Jr.; Chaudhuri, Sandipan S.; Wilkerson, Thomas D.

1995-01-01

A ground-based atmospheric lidar system that utilizes a Holographic Optical Telescope and Scanner has been developed and successfully operated to obtain atmospheric backscatter profiles. The Prototype Holographic Atmospheric Scanner for Environmental Remote Sensing is built around a volume phase reflection Holographic Optical Element. This single optical element both directs and collimates the outgoing laser beam as well as collects, focuses, and filters the atmospheric laser backscatter, while offering significant weight savings over existing telescope mirror technology. Conical scanning is accomplished as the HOE rotates on a turntable sweeping the 1.2 mrad field of view around a 42deg cone. During this technology demonstration, atmospheric aerosol and cloud return signals have been received in both stationary and scanning modes. The success of this program has led to the further development of this technology for integration into airborne and eventually satellite earth observing scanning lidar telescopes.

Carbon dioxide of Pu`u`O`o volcanic plume at Kilauea retrieved by AVIRIS hyperspectral data

USGS Publications Warehouse

Spinetti, C.; Carrere, V.; Buongiorno, M. Fabrizia; Sutton, A.J.; Elias, T.

2008-01-01

A remote sensing approach permits for the first time the derivation of a map of the carbon dioxide concentration in a volcanic plume. The airborne imaging remote sensing overcomes the typical difficulties associated with the ground measurements and permits rapid and large views of the volcanic processes together with the measurements of volatile components exolving from craters. Hyperspectral images in the infrared range (1900-2100??nm), where carbon dioxide absorption lines are present, have been used. These images were acquired during an airborne campaign by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over the Pu`u` O`o Vent situated at the Kilauea East Rift zone, Hawaii. Using a radiative transfer model to simulate the measured up-welling spectral radiance and by applying the newly developed mapping technique, the carbon dioxide concentration map of the Pu`u` O`o Vent plume were obtained. The carbon dioxide integrated flux rate were calculated and a mean value of 396 ?? 138??t d- 1 was obtained. This result is in agreement, within the measurements errors, with those of the ground measurements taken during the airborne campaign. ?? 2008 Elsevier Inc.

Airborne monitoring of crop canopy temperatures for irrigation scheduling and yield prediction

NASA Technical Reports Server (NTRS)

Millard, J. P.; Jackson, R. D.; Goettelman, R. C.; Reginato, R. J.; Idso, S. B.; Lapado, R. L.

1977-01-01

Airborne and ground measurements were made on April 1 and 29, 1976, over a USDA test site consisting mostly of wheat in various stages of water stress, but also including alfalfa and bare soil. These measurements were made to evaluate the feasibility of measuring crop temperatures from aircraft so that a parameter termed stress degree day, SDD, could be computed. Ground studies have shown that SDD is a valuable indicator of a crop's water needs, and that it can be related to irrigation scheduling and yield. The aircraft measurement program required predawn and afternoon flights coincident with minimum and maximum crop temperatures. Airborne measurements were made with an infrared line scanner and with color IR photography. The scanner data were registered, subtracted, and color-coded to yield pseudo-colored temperature-difference images. Pseudo-colored images reading directly in daily SDD increments were also produced. These maps enable a user to assess plant water status and thus determine irrigation needs and crop yield potentials.

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

PubMed

Allison, Robert S; Johnston, Joshua M; Craig, Gregory; Jennings, Sion

2016-08-18

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring

PubMed Central

Allison, Robert S.; Johnston, Joshua M.; Craig, Gregory; Jennings, Sion

2016-01-01

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context. PMID:27548174

Canopy Spectral Invariants. Part 2; Application to Classification of Forest Types from Hyperspectral Data

NASA Technical Reports Server (NTRS)

Schull, M. A.; Knyazikhin, Y.; Xu, L.; Samanta, A.; Carmona, P. L.; Lepine, L.; Jenkins, J. P.; Ganguly, S.; Myneni, R. B.

2011-01-01

Many studies have been conducted to demonstrate the ability of hyperspectral data to discriminate plant dominant species. Most of them have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing or unknown land cover. In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy reflectance in terms of the leaf spectral scattering and two spectrally invariant and structurally varying variables - recollision and directional escape probabilities. The methodology is based on the idea of retrieving spectrally invariant parameters from hyperspectral data first, and then relating their values to structural characteristics of three-dimensional canopy structure. Theoretical and empirical analyses of ground and airborne data acquired by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over two sites in New England, USA, suggest that the canopy spectral invariants convey information about canopy structure at both the macro- and micro-scales. The total escape probability (one minus recollision probability) varies as a power function with the exponent related to the number of nested hierarchical levels present in the pixel. Its base is a geometrical mean of the local total escape probabilities and accounts for the cumulative effect of canopy structure over a wide range of scales. The ratio of the directional to the total escape probability becomes independent of the number of hierarchical levels and is a function of the canopy structure at the macro-scale such as tree spatial distribution, crown shape and size, within-crown foliage density and ground cover. These properties allow for the natural

Radiometric characterization of hyperspectral imagers using multispectral sensors

NASA Astrophysics Data System (ADS)

McCorkel, Joel; Thome, Kurt; Leisso, Nathan; Anderson, Nikolaus; Czapla-Myers, Jeff

2009-08-01

The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of MODIS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most bands as well as similar agreement between results that employ the different MODIS sensors as a reference.

Hyperspectral Fluorescence and Reflectance Imaging Instrument

NASA Technical Reports Server (NTRS)

Ryan, Robert E.; O'Neal, S. Duane; Lanoue, Mark; Russell, Jeffrey

2008-01-01

The system is a single hyperspectral imaging instrument that has the unique capability to acquire both fluorescence and reflectance high-spatial-resolution data that is inherently spatially and spectrally registered. Potential uses of this instrument include plant stress monitoring, counterfeit document detection, biomedical imaging, forensic imaging, and general materials identification. Until now, reflectance and fluorescence spectral imaging have been performed by separate instruments. Neither a reflectance spectral image nor a fluorescence spectral image alone yields as much information about a target surface as does a combination of the two modalities. Before this system was developed, to benefit from this combination, analysts needed to perform time-consuming post-processing efforts to co-register the reflective and fluorescence information. With this instrument, the inherent spatial and spectral registration of the reflectance and fluorescence images minimizes the need for this post-processing step. The main challenge for this technology is to detect the fluorescence signal in the presence of a much stronger reflectance signal. To meet this challenge, the instrument modulates artificial light sources from ultraviolet through the visible to the near-infrared part of the spectrum; in this way, both the reflective and fluorescence signals can be measured through differencing processes to optimize fluorescence and reflectance spectra as needed. The main functional components of the instrument are a hyperspectral imager, an illumination system, and an image-plane scanner. The hyperspectral imager is a one-dimensional (line) imaging spectrometer that includes a spectrally dispersive element and a two-dimensional focal plane detector array. The spectral range of the current imaging spectrometer is between 400 to 1,000 nm, and the wavelength resolution is approximately 3 nm. The illumination system consists of narrowband blue, ultraviolet, and other discrete

Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 2: TIMS Workshop

NASA Technical Reports Server (NTRS)

Realmuto, Vincent J. (Editor)

1993-01-01

This is volume 2 of a three volume set of publications that contain the summaries for the Fourth Annual JPL Airborne Geoscience Workshop, held in Washington, D.C. on October 25-29, 1993. The main workshop is divided into three smaller workshops as follows: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on October 25-26. The summaries for this workshop appear in Volume 1. The Thermal Infrared Multispectral Scanner (TIMS) workshop, on October 27. The summaries for this workshop appear in Volume 2. The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on October 28-29. The summaries for this workshop appear in Volume 3.

Hyperspectral remote sensing of vegetation and agricultural crops: knowledge gain and knowledge gap after 40 years of research

USGS Publications Warehouse

Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo; Edited by Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

2011-01-01

The focus of this chapter was to summarize the advances made over last 40+ years, as reported in various chapters of this book, in understanding, modeling, and mapping terrestrial vegetation using hyperspectral remote sensing (or imaging spectroscopy) using sensors that are ground-based, truck-mounted, airborne, and spaceborne. As we have seen in various chapters of this book and synthesized in this chapter, the advances made include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracies (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (b), (e) ability to access stress resulting from causes such as management practices, pests and disease, water deficit or excess; , and (f) establishing more sensitive wavebands and indices to detect plant water\\moisture content. The advent of spaceborne hyperspectral sensors (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming NASA’s HyspIRI) and numerous methods and techniques espoused in this book to overcome Hughes phenomenon or data redundancy when handling large volumes of hyperspectral data have generated tremendous interest in advancing our hyperspectral applications knowledge base over larger spatial extent such as region, nation, continent, and globe.

An automated geometric correction system for airborne multispectral scanner imagery

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

Lewis-King, E.; Tinney, L.; Brickey, D.

1996-10-01

The United States Department of Energy (USDOE) maintains a Remote Sensing Laboratory (RSL) to support nuclear related programs of the US Government. The mission of the organization includes both emergency response and more routine environmental assessments of nuclear facilities. The USDOE RSL maintains a small fleet of specially equipped aircraft that are used as platforms for remote sensor systems. The aircraft include helicopters, light aircraft, and a business jet suitable for high altitude acquisitions. Multispectral scanners flown on these platforms are subject to geometric distortions related to variations in aircraft orientation (pitch, roll, and yaw), position, and velocity during datamore » acquistions.« less

Using hyperspectral imagery to assist federal forest monitoring and restoration projects in the Southern Rocky Mountains, Colorado

NASA Astrophysics Data System (ADS)

Wamser, Kyle

Hyperspectral imagery and the corresponding ability to conduct analysis below the pixel level have tremendous potential to aid in landcover monitoring. During large ecosystem restoration projects, being able to monitor specific aspects of the recovery over large and often inaccessible areas under constrained finances are major challenges. The Civil Air Patrol's Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) can provide hyperspectral data in most parts of the United States at relatively low cost. Although designed specifically for use in locating downed aircraft, the imagery holds the potential to identify specific aspects of landcover at far greater fidelity than traditional multispectral means. The goals of this research were to improve the use of ARCHER hyperspectral imagery to classify sub-canopy and open-area vegetation in coniferous forests located in the Southern Rockies and to determine how much fidelity might be lost from a baseline of 1 meter spatial resolution resampled to 2 and 5 meter pixel size to simulate higher altitude collection. Based on analysis comparing linear spectral unmixing with a traditional supervised classification, the linear spectral unmixing proved to be statistically superior. More importantly, however, linear spectral unmixing provided additional sub-pixel information that was unavailable using other techniques. The second goal of determining fidelity loss based on spatial resolution was more difficult to determine due to how the data are represented. Furthermore, the 2 and 5 meter imagery were obtained by resampling the 1 meter imagery and therefore may not be representative of the quality of actual 2 or 5 meter imagery. Ultimately, the information derived from this research may be useful in better utilizing hyperspectral imagery to conduct forest monitoring and assessment.

Using mid-range laser scanners to digitize cultural-heritage sites.

PubMed

Spring, Adam P; Peters, Caradoc; Minns, Tom

2010-01-01

Here, we explore new, more accessible ways of modeling 3D data sets that both professionals and amateurs can employ in areas such as architecture, forensics, geotechnics, cultural heritage, and even hobbyist modeling. To support our arguments, we present images from a recent case study in digital preservation of cultural heritage using a mid-range laser scanner. Our appreciation of the increasing variety of methods for capturing 3D spatial data inspired our research. Available methods include photogrammetry, airborne lidar, sonar, total stations (a combined electronic and optical survey instrument), and midand close-range scanning.1 They all can produce point clouds of varying density. In our case study, the point cloud produced by a mid-range scanner demonstrates how open source software can make modeling and disseminating data easier. Normally, researchers would model this data using expensive specialized software, and the data wouldn't extend beyond the laser-scanning community.

Multi-pass encoding of hyperspectral imagery with spectral quality control

NASA Astrophysics Data System (ADS)

Wasson, Steven; Walker, William

2015-05-01

Multi-pass encoding is a technique employed in the field of video compression that maximizes the quality of an encoded video sequence within the constraints of a specified bit rate. This paper presents research where multi-pass encoding is extended to the field of hyperspectral image compression. Unlike video, which is primarily intended to be viewed by a human observer, hyperspectral imagery is processed by computational algorithms that generally attempt to classify the pixel spectra within the imagery. As such, these algorithms are more sensitive to distortion in the spectral dimension of the image than they are to perceptual distortion in the spatial dimension. The compression algorithm developed for this research, which uses the Karhunen-Loeve transform for spectral decorrelation followed by a modified H.264/Advanced Video Coding (AVC) encoder, maintains a user-specified spectral quality level while maximizing the compression ratio throughout the encoding process. The compression performance may be considered near-lossless in certain scenarios. For qualitative purposes, this paper presents the performance of the compression algorithm for several Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion datasets using spectral angle as the spectral quality assessment function. Specifically, the compression performance is illustrated in the form of rate-distortion curves that plot spectral angle versus bits per pixel per band (bpppb).

Thermal Infrared Spectral Imager for Airborne Science Applications

NASA Technical Reports Server (NTRS)

Johnson, William R.; Hook, Simon J.; Mouroulis, Pantazis; Wilson, Daniel W.; Gunapala, Sarath D.; Hill, Cory J.; Mumolo, Jason M.; Eng, Bjorn T.

2009-01-01

An airborne thermal hyperspectral imager is under development which utilizes the compact Dyson optical configuration and quantum well infrared photo detector (QWIP) focal plane array. The Dyson configuration uses a single monolithic prism-like grating design which allows for a high throughput instrument (F/1.6) with minimal ghosting, stray-light and large swath width. The configuration has the potential to be the optimal imaging spectroscopy solution for lighter-than-air (LTA) vehicles and unmanned aerial vehicles (UAV) due to its small form factor and relatively low power requirements. The planned instrument specifications are discussed as well as design trade-offs. Calibration testing results (noise equivalent temperature difference, spectral linearity and spectral bandwidth) and laboratory emissivity plots from samples are shown using an operational testbed unit which has similar specifications as the final airborne system. Field testing of the testbed unit was performed to acquire plots of apparent emissivity for various known standard minerals (such as quartz). A comparison is made using data from the ASTER spectral library.

Band-Moment Compression of AVIRIS Hyperspectral Data and its Use in the Detection of Vegetation Stress

NASA Technical Reports Server (NTRS)

Estep, L.; Davis, B.

2001-01-01

A remote sensing campaign was conducted over a U.S. Department of Agriculture test farm at Shelton, Nebraska. An experimental field was set off in plots that were differentially treated with anhydrous ammonia. Four replicates of 0-kg/ha to 200-kg/ha plots, in 50-kg/ha increments, were set out in a random block design. Low-altitude (GSD of 3 m) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data were collected over the site in 224 bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to reduce data load while maintaining or enhancing algorithm performance for vegetation stress detection, band-moment compression and analysis was applied to the AVIRIS image cube. The results indicated that band-moment techniques compress the AVIRIS dataset significantly while retaining the capability of detecting environmentally induced vegetation stress.

Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes.

PubMed

Prasad, Dilip K; Agarwal, Krishna

2016-03-22

We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL's SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

NASA Astrophysics Data System (ADS)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as

Performance limitations of temperature-emissivity separation techniques in long-wave infrared hyperspectral imaging applications

NASA Astrophysics Data System (ADS)

Pieper, Michael; Manolakis, Dimitris; Truslow, Eric; Cooley, Thomas; Brueggeman, Michael; Jacobson, John; Weisner, Andrew

2017-08-01

Accurate estimation or retrieval of surface emissivity from long-wave infrared or thermal infrared (TIR) hyperspectral imaging data acquired by airborne or spaceborne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation and temperature-emissivity separation (TES). The most widely used TES algorithms for hyperspectral imaging data assume that the emissivity spectra for solids are smooth compared to the atmospheric transmission function. We develop a model to explain and evaluate the performance of TES algorithms using a smoothing approach. Based on this model, we identify three sources of error: the smoothing error of the emissivity spectrum, the emissivity error from using the incorrect temperature, and the errors caused by sensor noise. For each TES smoothing technique, we analyze the bias and variability of the temperature errors, which translate to emissivity errors. The performance model explains how the errors interact to generate temperature errors. Since we assume exact knowledge of the atmosphere, the presented results provide an upper bound on the performance of TES algorithms based on the smoothness assumption.

Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra

NASA Astrophysics Data System (ADS)

Vaudour, E.; Gilliot, J. M.; Bel, L.; Lefevre, J.; Chehdi, K.

2016-07-01

This study aimed at identifying the potential of Vis-NIR airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soil types comprised haplic luvisols, calcaric cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks. Tracks were atmospherically corrected then mosaicked at a 2 m-resolution using a set of 24 synchronous field spectra of bare soils, black and white targets and impervious surfaces. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then calculation and thresholding of NDVI from an atmospherically corrected SPOT image acquired the same day enabled to map agricultural fields with bare soil. A total of 101 sites sampled either in 2013 or in the 3 previous years and in 2015 were identified as bare by means of this map. Predictions were made from the mosaic AISA spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples, considering 74 sites outside cloud shadows only, and different sampling strategies for selecting calibration samples. Validation root-mean-square errors (RMSE) were comprised between 3.73 and 4.49 g Kg-1 and were ∼4 g Kg-1 in median. The most performing models in terms of coefficient of determination (R2) and Residual Prediction Deviation (RPD) values were the calibration models derived either from Kennard-Stone or conditioned Latin Hypercube sampling on smoothed spectra. The most generalizable model leading to lowest RMSE value of 3.73 g Kg-1 at the regional scale and 1.44 g Kg-1 at the within-field scale and low bias was the cross-validated leave

Aerosol and cloud properties derived from hyperspectral transmitted light in the southeast Atlantic sampled during field campaign deployments in 2016 and 2017

NASA Astrophysics Data System (ADS)

LeBlanc, S. E.; Redemann, J.; Flynn, C. J.; Segal-Rosenhaimer, M.; Kacenelenbogen, M. S.; Shinozuka, Y.; Pistone, K.; Karol, Y.; Schmidt, S.; Cochrane, S.; Chen, H.; Meyer, K.; Ferrare, R. A.; Burton, S. P.; Hostetler, C. A.; Hair, J. W.

2017-12-01

We present aerosol and cloud properties collected from airborne remote-sensing measurements in the southeast Atlantic during the recent NASA ObseRvations of CLouds above Aerosols and their intEractionS (ORACLES) field campaign. During the biomass burning seasons of September 2016 and August 2017, we sampled aerosol layers which overlaid marine stratocumulus clouds off the southwestern coast of Africa. We sampled these aerosol layers and the underlying clouds from the NASA P3 airborne platform with the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR). Aerosol optical depth (AOD), along with trace gas content in the atmospheric column (water vapor, NO2, and O3), is obtained from the attenuation in the sun's direct beam, measured at the altitude of the airborne platform. Using hyperspectral transmitted light measurements from 4STAR, in conjunction with hyperspectral hemispheric irradiance measurements from the Solar Spectral Flux Radiometers (SSFR), we also obtained aerosol intensive properties (asymmetry parameter, single scattering albedo), aerosol size distributions, cloud optical depth (COD), cloud particle effective radius, and cloud thermodynamic phase. Aerosol intensive properties are retrieved from measurements of angularly resolved skylight and flight level spectral albedo using the inversion used with measurements from AERONET (Aerosol Robotic Network) that has been modified for airborne use. The cloud properties are obtained from 4STAR measurements of scattered light below clouds. We show a favorable initial comparison of the above-cloud AOD measured by 4STAR to this same product retrieved from measurements by the MODIS instrument on board the TERRA and AQUA satellites. The layer AOD observed above clouds will also be compared to integrated aerosol extinction profile measurements from the High Spectral Resolution Lidar-2 (HSRL-2).

Lidar and Hyperspectral Remote Sensing for the Analysis of Coniferous Biomass Stocks and Fluxes

NASA Astrophysics Data System (ADS)

Halligan, K. Q.; Roberts, D. A.

2006-12-01

Airborne lidar and hyperspectral data can improve estimates of aboveground carbon stocks and fluxes through their complimentary responses to vegetation structure and biochemistry. While strong relationships have been demonstrated between lidar-estimated vegetation structural parameters and field data, research is needed to explore the portability of these methods across a range of topographic conditions, disturbance histories, vegetation type and climate. Additionally, research is needed to evaluate contributions of hyperspectral data in refining biomass estimates and determination of fluxes. To address these questions we are a conducting study of lidar and hyperspectral remote sensing data across sites including coniferous forests, broadleaf deciduous forests and a tropical rainforest. Here we focus on a single study site, Yellowstone National Park, where tree heights, stem locations, above ground biomass and basal area were mapped using first-return small-footprint lidar data. A new method using lidar intensity data was developed for separating the terrain and vegetation components in lidar data using a two-scale iterative local minima filter. Resulting Digital Terrain Models (DTM) and Digital Canopy Models (DCM) were then processed to retrieve a diversity of vertical and horizontal structure metrics. Univariate linear models were used to estimate individual tree heights while stepwise linear regression was used to estimate aboveground biomass and basal area. Three small-area field datasets were compared for their utility in model building and validation of vegetation structure parameters. All structural parameters were linearly correlated with lidar-derived metrics, with higher accuracies obtained where field and imagery data were precisely collocated . Initial analysis of hyperspectral data suggests that vegetation health metrics including measures of live and dead vegetation and stress indices may provide good indicators of carbon flux by mapping vegetation

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

USGS Publications Warehouse

Tian, Y.Q.; Yu, Q.; Zimmerman, M.J.; Flint, S.; Waldron, M.C.

2010-01-01

This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral-derived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a

Water depth measurement using an airborne pulsed neon laser system

NASA Technical Reports Server (NTRS)

Hoge, F. E.; Swift, R. N.; Frederick, E. B.

1980-01-01

The paper presents the water depth measurement using an airborne pulsed neon laser system. The results of initial base-line field test results of NASA airborne oceanographic lidar in the bathymetry mode are given, with water-truth measurements of depth and beam attenuation coefficients by boat taken at the same time as overflights to aid in determining the system's operational performance. The nadir-angle tests and field-of-view data are presented; this laser bathymetry system is an improvement over prior models in that (1) the surface-to-bottom pulse waveform is digitally recorded on magnetic tape, and (2) wide-swath mapping data may be routinely acquired using a 30 deg full-angle conical scanner.

Radiometric Characterization of Hyperspectral Imagers using Multispectral Sensors

NASA Technical Reports Server (NTRS)

McCorkel, Joel; Kurt, Thome; Leisso, Nathan; Anderson, Nikolaus; Czapla-Myers, Jeff

2009-01-01

The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these test sites are not always successful due to weather and funding availability. Therefore, RSG has also automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor, This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral a imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (M0DIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of M0DlS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most brands as well as similar agreement between results that employ the different MODIS sensors as a reference.

False alarm recognition in hyperspectral gas plume identification

DOEpatents

Conger, James L [San Ramon, CA; Lawson, Janice K [Tracy, CA; Aimonetti, William D [Livermore, CA

2011-03-29

According to one embodiment, a method for analyzing hyperspectral data includes collecting first hyperspectral data of a scene using a hyperspectral imager during a no-gas period and analyzing the first hyperspectral data using one or more gas plume detection logics. The gas plume detection logic is executed using a low detection threshold, and detects each occurrence of an observed hyperspectral signature. The method also includes generating a histogram for all occurrences of each observed hyperspectral signature which is detected using the gas plume detection logic, and determining a probability of false alarm (PFA) for all occurrences of each observed hyperspectral signature based on the histogram. Possibly at some other time, the method includes collecting second hyperspectral data, and analyzing the second hyperspectral data using the one or more gas plume detection logics and the PFA to determine if any gas is present. Other systems and methods are also included.

The Influence of Endmember Selection Method in Extracting Impervious Surface from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Wang, J.; Feng, B.

2016-12-01

Impervious surface area (ISA) has long been studied as an important input into moisture flux models. In general, ISA impedes groundwater recharge, increases stormflow/flood frequency, and alters in-stream and riparian habitats. Urban area is recognized as one of the richest ISA environment. Urban ISA mapping assists flood prevention and urban planning. Hyperspectral imagery (HI), for its ability to detect subtle spectral signature, becomes an ideal candidate in urban ISA mapping. To map ISA from HI involves endmember (EM) selection. The high degree of spatial and spectral heterogeneity of urban environment puts great difficulty in this task: a compromise point is needed between the automatic degree and the good representativeness of the method. The study tested one manual and two semi-automatic EM selection strategies. The manual and the first semi-automatic methods have been widely used in EM selection. The second semi-automatic EM selection method is rather new and has been only proposed for moderate spatial resolution satellite. The manual method visually selected the EM candidates from eight landcover types in the original image. The first semi-automatic method chose the EM candidates using a threshold over the pixel purity index (PPI) map. The second semi-automatic method used the triangle shape of the HI scatter plot in the n-Dimension visualizer to identify the V-I-S (vegetation-impervious surface-soil) EM candidates: the pixels locate at the triangle points. The initial EM candidates from the three methods were further refined by three indexes (EM average RMSE, minimum average spectral angle, and count based EM selection) and generated three spectral libraries, which were used to classify the test image. Spectral angle mapper was applied. The accuracy reports for the classification results were generated. The overall accuracy are 85% for the manual method, 81% for the PPI method, and 87% for the V-I-S method. The V-I-S EM selection method performs best in

Comparison of lithological mapping results from airborne hyperspectral VNIR-SWIR, LWIR and combined data

NASA Astrophysics Data System (ADS)

Feng, Jilu; Rogge, Derek; Rivard, Benoit

2018-02-01

This study investigates using the Airborne Hyperspectral Imaging Systems (AISA) visible and short-wave infrared (SWIR) and Spatially Enhanced Broadband Array Spectrograph System (SEBASS) longwave infrared (LWIR) (2 and 4 m spatial resolution, respectively) imagery independently and in combination to produce detailed lithologic maps in a subarctic region (Cape Smith Belt, Nunavik, Canada) where regionally metamorphosed lower greenschist mafic, ultramafic and sedimentary rocks are exposed in the presence of lichen coatings. We make use of continuous wavelet analysis (CWA) to improve the radiometric quality of the imagery through the minimization of random noise and the enhancement of spectral features, the minimization of residual errors in the ISAC radiometric correction and target temperature estimation in the case of the LWIR data, the minimization of line to line residual calibration effects that lead to inconsistencies in data mosaics, and the reduction in variability of the spectral continuum introduced by variable illumination and topography. The use of CWA also provides a platform to directly combine the wavelet scale spectral profiles of the SWIR and LWIR after applying a scalar correction factor to the LWIR such that the dynamic range of two data sets have equal weight. This is possible using CWA as the datasets are normalized to a zero mean allowing spectra from different spectral regions to be adjoined. Lithologic maps are generated using an iterative spectral unmixing approach with image spectral endmembers extracted from the SWIR and LWIR imagery based on locations defined from previous work of the study area and field mapping information. Unmixing results of the independent SWIR and LWIR data, and the combined data show clear benefits to using the CWA combined imagery. The analysis showed SWIR and LWIR imagery highlight similar regions and spatial distributions for the three ultramafic units (dunite, peridotite, pyroxenite). However, significant

Detection of Verticillium wilt of olive trees and downy mildew of opium poppy using hyperspectral and thermal UAV imagery

NASA Astrophysics Data System (ADS)

Calderón Madrid, Rocío; Navas Cortés, Juan Antonio; Montes Borrego, Miguel; Landa del Castillo, Blanca Beatriz; Lucena León, Carlos; Jesús Zarco Tejada, Pablo

2014-05-01

The present study explored the use of high-resolution thermal, multispectral and hyperspectral imagery as indicators of the infections caused by Verticillium wilt (VW) in olive trees and downy mildew (DM) in opium poppy fields. VW, caused by the soil-borne fungus Verticillium dahliae, and DM, caused by the biotrophic obligate oomycete Peronospora arborescens, are the most economically limiting diseases of olive trees and opium poppy, respectively, worldwide. V. dahliae infects the plant by the roots and colonizes its vascular system, blocking water flow and eventually inducing water stress. P. arborescens colonizes the mesophyll, appearing the first symptoms as small chlorotic leaf lesions, which can evolve to curled and thickened tissues and systemic infections that become deformed and necrotic as the disease develops. The work conducted to detect VW and DM infection consisted on the acquisition of time series of airborne thermal, multispectral and hyperspectral imagery using 2-m and 5-m wingspan electric Unmanned Aerial Vehicles (UAVs) in spring and summer of three consecutive years (2009 to 2011) for VW detection and on three dates in spring of 2009 for DM detection. Two 7-ha commercial olive orchards naturally infected with V. dahliae and two opium poppy field plots artificially infected by P. arborescens were flown. Concurrently to the airborne campaigns, olive orchards and opium poppy fields were assessed "in situ" to assess actual VW severity and DM incidence. Furthermore, field measurements were conducted at leaf and crown level. The field results related to VW detection showed a significant increase in crown temperature (Tc) minus air temperature (Ta) and a decrease in leaf stomatal conductance (G) as VW severity increased. This reduction in G was associated with a significant increase in the Photochemical Reflectance Index (PRI570) and a decrease in chlorophyll fluorescence. DM asymptomatic leaves showed significantly higher NDVI and lower green/red index

Active and passive multispectral scanner for earth resources applications: An advanced applications flight experiment

NASA Technical Reports Server (NTRS)

Hasell, P. G., Jr.; Peterson, L. M.; Thomson, F. J.; Work, E. A.; Kriegler, F. J.

1977-01-01

The development of an experimental airborne multispectral scanner to provide both active (laser illuminated) and passive (solar illuminated) data from a commonly registered surface scene is discussed. The system was constructed according to specifications derived in an initial programs design study. The system was installed in an aircraft and test flown to produce illustrative active and passive multi-spectral imagery. However, data was not collected nor analyzed for any specific application.

A report on the use of thermal scanner data in an operational program for monitoring apparent rooftop temperatures

NASA Technical Reports Server (NTRS)

Bjorklund, J.; Schmer, F. A.; Isakson, R. E.

1975-01-01

CENGAS, a division of Central Telephone and Utilities Corporation in cooperation with the Remote Sensing Institute, South Dakota State University, is using airborne thermal scanner data to monitor relative rooftop temperatures. Four Nebraska communities and one South Dakota community were surveyed by the Remote Sensing Institute for CENGAS. Thermal scanner data were converted to a film format and the resultant imagery has been successfully employed by CENGAS. The program places emphasis on heat losses resulting from inadequate home insulation, offers CENGAS customers the opportunity to observe a thermogram of their rooftop, and assists homeowners in evaluating insulation needs.

Michigan experimental multispectral mapping system: A description of the M7 airborne sensor and its performance

NASA Technical Reports Server (NTRS)

Hasell, P. G., Jr.

1974-01-01

The development and characteristics of a multispectral band scanner for an airborne mapping system are discussed. The sensor operates in the ultraviolet, visual, and infrared frequencies. Any twelve of the bands may be selected for simultaneous, optically registered recording on a 14-track analog tape recorder. Multispectral imagery recorded on magnetic tape in the aircraft can be laboratory reproduced on film strips for visual analysis or optionally machine processed in analog and/or digital computers before display. The airborne system performance is analyzed.

Miniaturized Airborne Imaging Central Server System

NASA Technical Reports Server (NTRS)

Sun, Xiuhong

2011-01-01

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and

Comparision of Bathymetry and Bottom Characteristics From Hyperspectral Remote Sensing Data and Shipborne Acoustic Measurements

NASA Astrophysics Data System (ADS)

McIntyre, M. L.; Naar, D. F.; Carder, K. L.; Howd, P. A.; Lewis, J. M.; Donahue, B. T.; Chen, F. R.

2002-12-01

There is growing interest in applying optical remote sensing techniques to shallow-water geological applications such as bathymetry and bottom characterization. Model inversions of hyperspectral remote-sensing reflectance imagery can provide estimates of bottom albedo and depth. This research was conducted in support of the HyCODE (Hyperspectral Coupled Ocean Dynamics Experiment) project in order to test optical sensor performance and the use of a hyperspectral remote-sensing reflectance algorithm for shallow waters in estimating bottom depths and reflectance. The objective of this project was to compare optically derived products of bottom depths and reflectance to shipborne acoustic measurements of bathymetry and backscatter. A set of three high-resolution, multibeam surveys within an 18 km by 1.5 km shore-perpendicular transect 5 km offshore of Sarasota, Florida were collected at water depths ranging from 8 m to 16 m. These products are compared to bottom depths derived from aircraft remote-sensing data collected with the AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) instrument data by means of a semi-analytical remote sensing reflectance model. The pixel size of the multibeam bathymetry and AVIRIS data are 0.25 m and 10 m, respectively. When viewed at full resolution, the multibeam bathymetry data show small-scale sedimentary bedforms (wavelength ~10m, amplitude ~1m) that are not observed in the lower resolution hyperspectral bathymetry. However, model-derived bottom depths agree well with a smoothed version of the multibeam bathymetry. Depths derived from shipborne hyperspectral measurements were accurate within 13%. In areas where diver observations confirmed biological growth and bioturbation, derived bottom depths were less accurate. Acoustic backscatter corresponds well with the aircraft hyperspectral imagery and in situ measurements of bottom reflectance. Acoustic backscatter was used to define the distribution of different bottom types

D Reconstruction from Uav-Based Hyperspectral Images

NASA Astrophysics Data System (ADS)

Liu, L.; Xu, L.; Peng, J.

2018-04-01

Reconstructing the 3D profile from a set of UAV-based images can obtain hyperspectral information, as well as the 3D coordinate of any point on the profile. Our images are captured from the Cubert UHD185 (UHD) hyperspectral camera, which is a new type of high-speed onboard imaging spectrometer. And it can get both hyperspectral image and panchromatic image simultaneously. The panchromatic image have a higher spatial resolution than hyperspectral image, but each hyperspectral image provides considerable information on the spatial spectral distribution of the object. Thus there is an opportunity to derive a high quality 3D point cloud from panchromatic image and considerable spectral information from hyperspectral image. The purpose of this paper is to introduce our processing chain that derives a database which can provide hyperspectral information and 3D position of each point. First, We adopt a free and open-source software, Visual SFM which is based on structure from motion (SFM) algorithm, to recover 3D point cloud from panchromatic image. And then get spectral information of each point from hyperspectral image by a self-developed program written in MATLAB. The production can be used to support further research and applications.

Airborne hyperspectral imaging for the detection of powdery mildew in wheat

NASA Astrophysics Data System (ADS)

Franke, Jonas; Mewes, Thorsten; Menz, Gunter

2008-08-01

Plant stresses, in particular fungal diseases, show a high variability in spatial and temporal dimension with respect to their impact on the host. Recent "Precision Agriculture"-techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stressdetection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is an accurate sensor-based detection of stress symptoms. The present study focuses on a remotely sensed detection of the fungal disease powdery mildew (Blumeria graminis) in wheat, Europe's main crop. In a field experiment, the potential of hyperspectral data for an early detection of stress symptoms was tested. A sophisticated endmember selection procedure was used and, additionally, a linear spectral mixture model was applied to a pixel spectrum with known characteristics, in order to derive an endmember representing 100% powdery mildew-infected wheat. Regression analyses of matched fraction estimates of this endmember and in-field-observed powdery mildew severities showed promising results (r=0.82 and r2=0.67).

Hyperspectral remote sensing and long term monitoring reveal watershed-estuary ecosystem interactions

NASA Astrophysics Data System (ADS)

Hestir, E. L.; Schoellhamer, D. H.; Santos, M. J.; Greenberg, J. A.; Morgan-King, T.; Khanna, S.; Ustin, S.

2016-02-01

Estuarine ecosystems and their biogeochemical processes are extremely vulnerable to climate and environmental changes, and are threatened by sea level rise and upstream activities such as land use/land cover and hydrological changes. Despite the recognized threat to estuaries, most aspects of how change will affect estuaries are not well understood due to the poorly resolved understanding of the complex physical, chemical and biological processes and their interactions in estuarine systems. Remote sensing technologies such as high spectral resolution optical systems enable measurements of key environmental parameters needed to establish baseline conditions and improve modeling efforts. The San Francisco Bay-Delta is a highly modified estuary system in a state of ecological crisis due to the numerous threats to its sustainability. In this study, we used a combination of hyperspectral remote sensing and long-term in situ monitoring records to investigate how water clarity has been responding to extreme climatic events, anthropogenic watershed disturbances, and submerged aquatic vegetation (SAV) invasions. From the long-term turbidity monitoring record, we found that water clarity underwent significant increasing step changes associated with sediment depletion and El Nino-extreme run-off events. Hyperspectral remote sensing data revealed that invasive submerged aquatic pant species have facultative C3 and C4-like photosynthetic pathways that give them a competitive advantage under the changing water clarity conditions of the Bay-Delta system. We postulate that this adaptation facilitated the rapid expansion of SAV following the significant step changes in increasing water clarity caused by watershed disturbances and the 1982-1983 El Nino events. Using SAV maps from hyperspectral remote sensing, we estimate that SAV-water clarity feedbacks were responsible for 20-70% of the increasing water clarity trend in the Bay-Delta. Ongoing and future developments in airborne and

Comparison of Stem Map Developed from Crown Geometry Allometry Linked Census Data to Airborne and Terrestrial Lidar at Harvard Forest, MA

NASA Astrophysics Data System (ADS)

Sullivan, F.; Palace, M. W.; Ducey, M. J.; David, O.; Cook, B. D.; Lepine, L. C.

2014-12-01

Harvard Forest in Petersham, MA, USA is the location of one of the temperate forest plots established by the Center for Tropical Forest Science (CTFS) as a joint effort with Harvard Forest and the Smithsonian Institute's Forest Global Earth Observatory (ForestGEO) to characterize ecosystem processes and forest dynamics. Census of a 35 ha plot on Prospect Hill was completed during the winter of 2014 by researchers at Harvard Forest. Census data were collected according to CTFS protocol; measured variables included species, stem diameter, and relative X-Y locations. Airborne lidar data were collected over the censused plot using the high spatial resolution Goddard LiDAR, Hyperspectral, and Thermal sensor package (G-LiHT) during June 2012. As part of a separate study, 39 variable radius plots (VRPs) were randomly located and sampled within and throughout the Prospect Hill CTFS/ForestGEO plot during September and October 2013. On VRPs, biometric properties of trees were sampled, including species, stem diameter, total height, crown base height, crown radii, and relative location to plot centers using a 20 Basal Area Factor prism. In addition, a terrestrial-based lidar scanner was used to collect one lidar scan at plot center for 38 of the 39 VRPs. Leveraging allometric equations of crown geometry and tree height developed from 374 trees and 16 different species sampled on 39 VRPs, a 3-dimensional stem map will be created using the Harvard Forest ForestGEO Prospect Hill census. Vertical and horizontal structure of 3d field-based stem maps will be compared to terrestrial and airborne lidar scan data. Furthermore, to assess the quality of allometric equations, a 2d canopy height raster of the field-based stem map will be compared to a G-LiHT derived canopy height model for the 35 ha census plot. Our automated crown delineation methods will be applied to the 2d representation of the census stem map and the G-LiHT canopy height model. For future work related to this study

Concept of an advanced hyperspectral remote sensing system for pipeline monitoring

NASA Astrophysics Data System (ADS)

Keskin, Göksu; Teutsch, Caroline D.; Lenz, Andreas; Middelmann, Wolfgang

2015-10-01

Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.

Hyperspectral fundus imager

NASA Astrophysics Data System (ADS)

Truitt, Paul W.; Soliz, Peter; Meigs, Andrew D.; Otten, Leonard John, III

2000-11-01

A Fourier Transform hyperspectral imager was integrated onto a standard clinical fundus camera, a Zeiss FF3, for the purposes of spectrally characterizing normal anatomical and pathological features in the human ocular fundus. To develop this instrument an existing FDA approved retinal camera was selected to avoid the difficulties of obtaining new FDA approval. Because of this, several unusual design constraints were imposed on the optical configuration. Techniques to calibrate the sensor and to define where the hyperspectral pushbroom stripe was located on the retina were developed, including the manufacturing of an artificial eye with calibration features suitable for a spectral imager. In this implementation the Fourier transform hyperspectral imager can collect over a hundred 86 cm-1 spectrally resolved bands with 12 micro meter/pixel spatial resolution within the 1050 nm to 450 nm band. This equates to 2 nm to 8 nm spectral resolution depending on the wavelength. For retinal observations the band of interest tends to lie between 475 nm and 790 nm. The instrument has been in use over the last year successfully collecting hyperspectral images of the optic disc, retinal vessels, choroidal vessels, retinal backgrounds, and macula diabetic macular edema, and lesions of age-related macular degeneration.

Construction of a small and lightweight hyperspectral imaging system

NASA Astrophysics Data System (ADS)

Vogel, Britta; Hünniger, Dirk; Bastian, Georg

2014-05-01

The analysis of the reflected sunlight offers great opportunity to gain information about the environment, including vegetation and soil. In the case of plants the wavelength ratio of the reflected light usually undergoes a change if the state of growth or state of health changes. So the measurement of the reflected light allows drawing conclusions about the state of, amongst others, vegetation. Using a hyperspectral imaging system for data acquisition leads to a large dataset, which can be evaluated with respect to several different questions to obtain various information by one measurement. Based on commercially available plain optical components we developed a small and lightweight hyperspectral imaging system within the INTERREG IV A-Project SMART INSPECTORS. The project SMART INSPECTORS [Smart Aerial Test Rigs with Infrared Spectrometers and Radar] deals with the fusion of airborne visible and infrared imaging remote sensing instruments and wireless sensor networks for precision agriculture and environmental research. A high performance camera was required in terms of good signal, good wavelength resolution and good spatial resolution, while severe constraints of size, proportions and mass had to be met due to the intended use on small unmanned aerial vehicles. The detector was chosen to operate without additional cooling. The refractive and focusing optical components were identified by supporting works with an optical raytracing software and a self-developed program. We present details of design and construction of our camera system, test results to confirm the optical simulation predictions as well as our first measurements.

Application of airborne laser scanner measurements of ocean roughness to the calibration and validation of a satellite bistatic radar experiment

NASA Astrophysics Data System (ADS)

Parrin, J.; Garrison, J. L.

2006-12-01

A high-resolution airborne laser scanner, from the National Center for Airborne Laser Mapping (NCALM) was used to profile the ocean surface in an attempt to experimentally measure the ocean height spectrum down to wavelengths as small as a few centimetres. In October of 2005, three data collections were scheduled, during overpasses of the UK-DMC satellite, off the coast of Virginia. UK-DMC carries an experimental bistatic radar receiver, which uses Global Navigation Satellite System (GNSS) signals as illumination sources. Most models for reflected GNSS signals relate the shape of the signal correlation waveforms to the ocean roughness, parameterized as a probability distribution (PDF) of surface slopes. This statistical description of the ocean surface must first be filtered to wavelengths greater than some fraction of the GNSS wavelength of 19 cm. Past experimental campaigns have used more common in-situ measurements, such as wind speed, for comparison with GNSS waveforms. These types of measurements will require the assumption of some empirical model for the ocean height spectrum, allowing the computation of the filtered slope statistics. Proposed applications of reflected GNSS signals include the correction of ocean roughness effects in passive microwave radiometry. To evaluate the feasibility of GNSS reflections for this measurement, it is important to make a more direct measurement of the ocean surface slope statistics, without the assumption of a spectrum model. In these experiments, a direct measurement of this spectrum was attempted, using the NCALM system. The laser scanner was operated on a low altitude (500 m) aircraft, at the highest sample rate (33KHz), generating ocean height measurements with an along-track separation of a few millimetres. The laser illuminates a spot on the ocean surface that is smaller than 10 cm, however, limiting the smallest resolvable wavelength to something on that order. Laser data were collected along multiple flight lines

Detecting leafy spurge in native grassland using hyperspectral image analysis

NASA Astrophysics Data System (ADS)

Kloppenburg, Catherine

Leafy spurge (Euphoria esula L.) is a perennial noxious weed that has been encroaches on the native grassland regions of North America resulting in biological and economic impacts. Leafy spurge growth is most prevalent along river banks and in pasture areas. Due to poor accessibility and the cost and labour associated with data collection, estimates of number and size of leafy spurge infestations is poor. Remote sensing has the ability to cover large areas, providing an alternate means to ground surveys and will allow for the capability to create an accurate baseline of infestations. Airborne hyperspectral data were collected over the two test sites selected on the Blood Reserve in Southern Alberta using a combined Airborne Imaging Spectrometer for different Applications (AISA) Eagle and Hawk sensor systems in July, 2010. This study used advanced analysis tools, including spectral mixture analysis, spectral angle mapper and mixture-tuned matched filter techniques to evaluate the ability to detect leafy spurge patches. The results show that patches of leafy spurge with flowering stem density >40 stems m-2 were identified with 85 % accuracy while identification of lower density stems were less accurate (10 - 40 %). The results are promising with respect to quantifying areas of significant leafy spurge infestation and targeting biological control and potential insect release sites.

Fire detection from hyperspectral data using neural network approach

NASA Astrophysics Data System (ADS)

Piscini, Alessandro; Amici, Stefania

2015-10-01

This study describes an application of artificial neural networks for the recognition of flaming areas using hyper- spectral remote sensed data. Satellite remote sensing is considered an effective and safe way to monitor active fires for environmental and people safeguarding. Neural networks are an effective and consolidated technique for the classification of satellite images. Moreover, once well trained, they prove to be very fast in the application stage for a rapid response. At flaming temperature, thanks to its low excitation energy (about 4.34 eV), potassium (K) ionize with a unique doublet emission features. This emission features can be detected remotely providing a detection map of active fire which allows in principle to separate flaming from smouldering areas of vegetation even in presence of smoke. For this study a normalised Advanced K Band Difference (AKBD) has been applied to airborne hyper spectral sensor covering a range of 400-970 nm with resolution 2.9 nm. A back propagation neural network was used for the recognition of active fires affecting the hyperspectral image. The network was trained using all channels of sensor as inputs, and the corresponding AKBD indexes as target output. In order to evaluate its generalization capabilities, the neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase. The validation results for the independent data-sets had an overall accuracy round 100% for both image and a few commission errors (0.1%), therefore demonstrating the feasibility of estimating the presence of active fires using a neural network approach. Although the validation of the neural network classifier had a few commission errors, the producer accuracies were lower due to the presence of omission errors. Image analysis revealed that those false negatives lie in "smoky" portion fire fronts, and due to the low intensity of the signal. The proposed method can be considered

Current instrument status of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

NASA Technical Reports Server (NTRS)

Eastwood, Michael L.; Sarture, Charles M.; Chrien, Thomas G.; Green, Robert O.; Porter, Wallace M.

1991-01-01

An upgraded version of AVIRIS, an airborne imaging spectrometer based on a whiskbroom-type scanner coupled via optical fibers to four dispersive spectrometers, that has been in operation since 1987 is described. Emphasis is placed on specific AVIRIS subsystems including foreoptics, fiber optics, and an in-flight reference source; spectrometers and detector dewars; a scan drive mechanism; a signal chain; digital electronics; a tape recorder; calibration systems; and ground support requirements.

Can the normalized soil moisture index improve the prediction of soil organic carbon based on hyperspectral remote sensing data?

NASA Astrophysics Data System (ADS)

van Wesemael, Bas; Nocita, Marco

2016-04-01

One of the problems for mapping of soil organic carbon (SOC) at large-scale based on visible - near and short wave infrared (VIS-NIR-SWIR) remote sensing techniques is the spatial variation of topsoil moisture when the images are collected. Soil moisture is certainly an aspect causing biased SOC estimations, due to the problems in discriminating reflectance differences due to either variations in organic matter or soil moisture, or their combination. In addition, the difficult validation procedures make the accurate estimation of soil moisture from optical airborne a major challenge. After all, the first millimeters of the soil surface reflect the signal to the airborne sensor and show a large spatial, vertical and temporal variation in soil moisture. Hence, the difficulty of assessing the soil moisture of this thin layer at the same moment of the flight. The creation of a soil moisture proxy, directly retrievable from the hyperspectral data is a priority to improve the large-scale prediction of SOC. This paper aims to verify if the application of the normalized soil moisture index (NSMI) to Airborne Prima Experiment (APEX) hyperspectral images could improve the prediction of SOC. The study area was located in the loam region of Wallonia, Belgium. About 40 samples were collected from bare fields covered by the flight lines, and analyzed in the laboratory. Soil spectra, corresponding to the sample locations, were extracted from the images. Once the NSMI was calculated for the bare fields' pixels, spatial patterns, presumably related to within field soil moisture variations, were revealed. SOC prediction models, built using raw and pre-treated spectra, were generated from either the full dataset (general model), or pixels belonging to one of the two classes of NSMI values (NSMI models). The best result, with a RMSE after validation of 1.24 g C kg-1, was achieved with a NSMI model, compared to the best general model, characterized by a RMSE of 2.11 g C kg-1. These

Nonnegative Matrix Factorization for Efficient Hyperspectral Image Projection

NASA Technical Reports Server (NTRS)

Iacchetta, Alexander S.; Fienup, James R.; Leisawitz, David T.; Bolcar, Matthew R.

2015-01-01

Hyperspectral imaging for remote sensing has prompted development of hyperspectral image projectors that can be used to characterize hyperspectral imaging cameras and techniques in the lab. One such emerging astronomical hyperspectral imaging technique is wide-field double-Fourier interferometry. NASA's current, state-of-the-art, Wide-field Imaging Interferometry Testbed (WIIT) uses a Calibrated Hyperspectral Image Projector (CHIP) to generate test scenes and provide a more complete understanding of wide-field double-Fourier interferometry. Given enough time, the CHIP is capable of projecting scenes with astronomically realistic spatial and spectral complexity. However, this would require a very lengthy data collection process. For accurate but time-efficient projection of complicated hyperspectral images with the CHIP, the field must be decomposed both spectrally and spatially in a way that provides a favorable trade-off between accurately projecting the hyperspectral image and the time required for data collection. We apply nonnegative matrix factorization (NMF) to decompose hyperspectral astronomical datacubes into eigenspectra and eigenimages that allow time-efficient projection with the CHIP. Included is a brief analysis of NMF parameters that affect accuracy, including the number of eigenspectra and eigenimages used to approximate the hyperspectral image to be projected. For the chosen field, the normalized mean squared synthesis error is under 0.01 with just 8 eigenspectra. NMF of hyperspectral astronomical fields better utilizes the CHIP's capabilities, providing time-efficient and accurate representations of astronomical scenes to be imaged with the WIIT.

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations.

PubMed

Johansen, Richard; Beck, Richard; Nowosad, Jakub; Nietch, Christopher; Xu, Min; Shu, Song; Yang, Bo; Liu, Hongxing; Emery, Erich; Reif, Molly; Harwood, Joseph; Young, Jade; Macke, Dana; Martin, Mark; Stillings, Garrett; Stumpf, Richard; Su, Haibin

2018-06-01

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km 2 ) in Southwest Ohio and Taylorsville Lake (11.88 km 2 ) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earth's orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r 2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2

Active Volcano Monitoring using a Space-based Hyperspectral Imager

NASA Astrophysics Data System (ADS)

Cipar, J. J.; Dunn, R.; Cooley, T.

2010-12-01

Active volcanoes occur on every continent, often in close proximity to heavily populated areas. While ground-based studies are essential for scientific research and disaster mitigation, remote sensing from space can provide rapid and continuous monitoring of active and potentially active volcanoes [Ramsey and Flynn, 2004]. In this paper, we report on hyperspectral measurements of Kilauea volcano, Hawaii. Hyperspectral images obtained by the US Air Force TacSat-3/ARTEMIS sensor [Lockwood et al, 2006] are used to obtain estimates of the surface temperatures for the volcano. ARTEMIS measures surface-reflected light in the visible, near-infrared, and short-wave infrared bands (VNIR-SWIR). The SWIR bands are known to be sensitive to thermal radiation [Green, 1996]. For example, images from the NASA Hyperion hyperspectral sensor have shown the extent of wildfires and active volcanoes [Young, 2009]. We employ the methodology described by Dennison et al, (2006) to obtain an estimate of the temperature of the active region of Kilauea. Both day and night-time images were used in the analysis. To improve the estimate, we aggregated neighboring pixels. The active rim of the lava lake is clearly discernable in the temperature image, with a measured temperature exceeding 1100o C. The temperature decreases markedly on the exterior of the summit crater. While a long-wave infrared (LWIR) sensor would be ideal for volcano monitoring, we have shown that the thermal state of an active volcano can be monitored using the SWIR channels of a reflective hyperspectral imager. References: Dennison, Philip E., Kraivut Charoensiri, Dar A. Roberts, Seth H. Peterson, and Robert O. Green (2006). Wildfire temperature and land cover modeling using hyperspectral data, Remote Sens. Environ., vol. 100, pp. 212-222. Green, R. O. (1996). Estimation of biomass fire temperature and areal extent from calibrated AVIRIS spectra, in Summaries of the 6th Annual JPL Airborne Earth Science Workshop, Pasadena, CA

Hyperspectral remote sensing of plant pigments.

PubMed

Blackburn, George Alan

2007-01-01

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.

MEMS FPI-based smartphone hyperspectral imager

NASA Astrophysics Data System (ADS)

Rissanen, Anna; Saari, Heikki; Rainio, Kari; Stuns, Ingmar; Viherkanto, Kai; Holmlund, Christer; Näkki, Ismo; Ojanen, Harri

2016-05-01

This paper demonstrates a mobile phone- compatible hyperspectral imager based on a tunable MEMS Fabry-Perot interferometer. The realized iPhone 5s hyperspectral imager (HSI) demonstrator utilizes MEMS FPI tunable filter for visible-range, which consist of atomic layer deposited (ALD) Al2O3/TiO2-thin film Bragg reflectors. Characterization results for the mobile phone hyperspectral imager utilizing MEMS FPI chip optimized for 500 nm is presented; the operation range is λ = 450 - 550 nm with FWHM between 8 - 15 nm. Also a configuration of two cascaded FPIs (λ = 500 nm and λ = 650 nm) combined with an RGB colour camera is presented. With this tandem configuration, the overall wavelength tuning range of MEMS hyperspectral imagers can be extended to cover a larger range than with a single FPI chip. The potential applications of mobile hyperspectral imagers in the vis-NIR range include authentication, counterfeit detection and potential health/wellness and food sensing applications.

Analysis of remote sensing data collected for detection and mapping of oil spills: Reduction and analysis of multi-sensor airborne data of the NASA Wallops oil spill exercise of November 1978

NASA Technical Reports Server (NTRS)

1982-01-01

Airborne, remotely sensed data of the NASA Wallops controlled oil spill were corrected, reduced and analysed. Sensor performance comparisons were made by registering data sets from different sensors, which were near-coincident in time and location. Multispectral scanner images were, in turn, overlayed with profiles of correlation between airborne and laboratory-acquired fluorosensor spectra of oil; oil-thickness contours derived (by NASA) from a scanning fluorosensor and also from a two-channel scanning microwave radiometer; and synthetic aperture radar X-HH images. Microwave scatterometer data were correlated with dual-channel (UV and TIR) line scanner images of the oil slick.

Retrieving Land Surface Temperature and Emissivity from Multispectral and Hyperspectral Thermal Infrared Instruments

NASA Astrophysics Data System (ADS)

Hook, Simon; Hulley, Glynn; Nicholson, Kerry

2017-04-01

Land Surface Temperature and Emissivity (LST&E) data are critical variables for studying a variety of Earth surface processes and surface-atmosphere interactions such as evapotranspiration, surface energy balance and water vapor retrievals. LST&E have been identified as an important Earth System Data Record (ESDR) by NASA and many other international organizations Accurate knowledge of the LST&E is a key requirement for many energy balance models to estimate important surface biophysical variables such as evapotranspiration and plant-available soil moisture. LST&E products are currently generated from sensors in low earth orbit (LEO) such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites as well as from sensors in geostationary Earth orbit (GEO) such as the Geostationary Operational Environmental Satellites (GOES) and airborne sensors such as the Hyperspectral Thermal Emission Spectrometer (HyTES). LST&E products are generated with varying accuracies depending on the input data, including ancillary data such as atmospheric water vapor, as well as algorithmic approaches. NASA has identified the need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. We will discuss the different approaches that can be used to retrieve surface temperature and emissivity from multispectral and hyperspectral thermal infrared sensors using examples from a variety of different sensors such as those mentioned, and planned new sensors like the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Hyperspectral Infrared Imager (HyspIRI). We will also discuss a project underway at NASA to develop a single unified product from some the individual sensor products and assess the errors associated with the product.

Multimodal hyperspectral optical microscopy

DOE PAGES

Novikova, Irina V.; Smallwood, Chuck R.; Gong, Yu; ...

2017-09-02

We describe a unique and convenient approach to multimodal hyperspectral optical microscopy, herein achieved by coupling a portable and transferable hyperspectral imager to various optical microscopes. The experimental and data analysis schemes involved in recording spectrally and spatially resolved fluorescence, dark field, and optical absorption micrographs are illustrated through prototypical measurements targeting selected model systems. Namely, hyperspectral fluorescence micrographs of isolated fluorescent beads are employed to ensure spectral calibration of our detector and to gauge the attainable spatial resolution of our measurements; the recorded images are diffraction-limited. Moreover, spatially over-sampled absorption spectroscopy of a single lipid (18:1 Liss Rhod PE)more » layer reveals that optical densities on the order of 10-3 may be resolved by spatially averaging the recorded optical signatures. We also briefly illustrate two applications of our setup in the general areas of plasmonics and cell biology. Most notably, we deploy hyperspectral optical absorption microscopy to identify and image algal pigments within a single live Tisochrysis lutea cell. Overall, this work paves the way for multimodal multidimensional spectral imaging measurements spanning the realms of several scientific disciples.« less

Multimodal hyperspectral optical microscopy

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

Novikova, Irina V.; Smallwood, Chuck R.; Gong, Yu

We describe a unique and convenient approach to multimodal hyperspectral optical microscopy, herein achieved by coupling a portable and transferable hyperspectral imager to various optical microscopes. The experimental and data analysis schemes involved in recording spectrally and spatially resolved fluorescence, dark field, and optical absorption micrographs are illustrated through prototypical measurements targeting selected model systems. Namely, hyperspectral fluorescence micrographs of isolated fluorescent beads are employed to ensure spectral calibration of our detector and to gauge the attainable spatial resolution of our measurements; the recorded images are diffraction-limited. Moreover, spatially over-sampled absorption spectroscopy of a single lipid (18:1 Liss Rhod PE)more » layer reveals that optical densities on the order of 10-3 may be resolved by spatially averaging the recorded optical signatures. We also briefly illustrate two applications of our setup in the general areas of plasmonics and cell biology. Most notably, we deploy hyperspectral optical absorption microscopy to identify and image algal pigments within a single live Tisochrysis lutea cell. Overall, this work paves the way for multimodal multidimensional spectral imaging measurements spanning the realms of several scientific disciples.« less

MEDUSA: an airborne multispectral oil spill detection and characterization system

NASA Astrophysics Data System (ADS)

Wagner, Peter; Hengstermann, Theo; Zielinski, Oliver

2000-12-01

MEDUSA is a sensor network, consisting of and effectively combining a variety of different remote sensing instruments. Installed in 1998 it is operationally used in a maritime surveillance aircraft maintained by the German Ministry of Transport, Building and Housing. On one hand routine oil pollution monitoring with remote sensing equipment like Side Looking Airborne Radar (SLAR), Infrared/Ultraviolet Line Scanner (IR/UV line scanner), Microwave Radiometer (MWR), Imaging Airborne Laserfluorosensor (IALFS) and Forward Looking Infrared (FLIR) requires a complex network and communication structure to be operated by a single operator. On the other hand the operation of such a variety of sensors on board of one aircraft provides an excellent opportunity to establish new concepts of integrated sensor fusion and data evaluation. In this work a general survey of the German surveillance aircraft instrumentation is given and major features of the sensor package as well as advantages of the design and architecture are presented. Results from routine operation over North and Baltic Sea are shown to illustrate the successful application of MEDUSA in maritime patrol of oil slicks and polluters. Recently the combination of the different sensor results towards one multispectral information has met with increasing interest. Thus new application fields and parameter sets could be derived, like oceanography or river flood management. The basic concepts and first results in the fusion of sensoric information will conclude the paper.

Portable Hyperspectral Imaging Broadens Sensing Horizons

NASA Technical Reports Server (NTRS)

2007-01-01

Broadband multispectral imaging can be very helpful in showing differences in energy being radiated and is often employed by NASA satellites to monitor temperature and climate changes. In addition, hyperspectral imaging is ideal for advanced laboratory uses, biomedical imaging, forensics, counter-terrorism, skin health, food safety, and Earth imaging. Lextel Intelligence Systems, LLC, of Jackson, Mississippi purchased Photon Industries Inc., a spinoff company of NASA's Stennis Space Center and the Institute for Technology Development dedicated to developing new hyperspectral imaging technologies. Lextel has added new features to and expanded the applicability of the hyperspectral imaging systems. It has made advances in the size, usability, and cost of the instruments. The company now offers a suite of turnkey hyperspectral imaging systems based on the original NASA groundwork. It currently has four lines of hyperspectral imaging products: the EagleEye VNIR 100E, the EagleEye SWIR 100E, the EagleEye SWIR 200E, and the EagleEye UV 100E. These Lextel instruments are used worldwide for a wide variety of applications including medical, military, forensics, and food safety.

A new hyperspectral image compression paradigm based on fusion

NASA Astrophysics Data System (ADS)

Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto

2016-10-01

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

Holographic Airborne Rotating Lidar Instrument Experiment (HARLIE)

NASA Technical Reports Server (NTRS)

Schwemmer, Geary K.

1998-01-01

Scanning holographic lidar receivers are currently in use in two operational lidar systems, PHASERS (Prototype Holographic Atmospheric Scanner for Environmental Remote Sensing) and now HARLIE (Holographic Airborne Rotating Lidar Instrument Experiment). These systems are based on volume phase holograms made in dichromated gelatin (DCG) sandwiched between 2 layers of high quality float glass. They have demonstrated the practical application of this technology to compact scanning lidar systems at 532 and 1064 nm wavelengths, the ability to withstand moderately high laser power and energy loading, sufficient optical quality for most direct detection systems, overall efficiencies rivaling conventional receivers, and the stability to last several years under typical lidar system environments. Their size and weight are approximately half of similar performing scanning systems using reflective optics. The cost of holographic systems will eventually be lower than the reflective optical systems depending on their degree of commercialization. There are a number of applications that require or can greatly benefit from a scanning capability. Several of these are airborne systems, which either use focal plane scanning, as in the Laser Vegetation Imaging System or use primary aperture scanning, as in the Airborne Oceanographic Lidar or the Large Aperture Scanning Airborne Lidar. The latter class requires a large clear aperture opening or window in the aircraft. This type of system can greatly benefit from the use of scanning transmission holograms of the HARLIE type because the clear aperture required is only about 25% larger than the collecting aperture as opposed to 200-300% larger for scan angles of 45 degrees off nadir.

Mapping of hydrothermally altered rocks using airborne multispectral scanner data, Marysvale, Utah, mining district

USGS Publications Warehouse

Podwysocki, M.H.; Segal, D.B.; Jones, O.D.

1983-01-01

Multispectral data covering an area near Marysvale, Utah, collected with the airborne National Aeronautics and Space Administration (NASA) 24-channel Bendix multispectral scanner, were analyzed to detect areas of hydrothermally altered, potentially mineralized rocks. Spectral bands were selected for analysis that approximate those of the Landsat 4 Thematic Mapper and which are diagnostic of the presence of hydrothermally derived products. Hydrothermally altered rocks, particularly volcanic rocks affected by solutions rich in sulfuric acid, are commonly characterized by concentrations of argillic minerals such as alunite and kaolinite. These minerals are important for identifying hydrothermally altered rocks in multispectral images because they have intense absorption bands centered near a wavelength of 2.2 ??m. Unaltered volcanic rocks commonly do not contain these minerals and hence do not have the absorption bands. A color-composite image was constructed using the following spectral band ratios: 1.6??m/2.2??m, 1.6??m/0.48??m, and 0.67??m/1.0??m. The particular bands were chosen to emphasize the spectral contrasts that exist for argillic versus non-argillic rocks, limonitic versus nonlimonitic rocks, and rocks versus vegetation, respectively. The color-ratio composite successfully distinguished most types of altered rocks from unaltered rocks. Some previously unrecognized areas of hydrothermal alteration were mapped. The altered rocks included those having high alunite and/or kaolinite content, siliceous rocks containing some kaolinite, and ash-fall tuffs containing zeolitic minerals. The color-ratio-composite image allowed further division of these rocks into limonitic and nonlimonitic phases. The image did not allow separation of highly siliceous or hematitically altered rocks containing no clays or alunite from unaltered rocks. A color-coded density slice image of the 1.6??m/2.2??m band ratio allowed further discrimination among the altered units. Areas

Clutter characterization within segmented hyperspectral imagery

NASA Astrophysics Data System (ADS)

Kacenjar, Steve T.; Hoffberg, Michael; North, Patrick

2007-10-01

Use of a Mean Class Propagation Model (MCPM) has been shown to be an effective approach in the expedient propagation of hyperspectral data scenes through the atmosphere. In this approach, real scene data are spatially subdivided into regions of common spectral properties. Each sub-region which we call a class possesses two important attributes (1) the mean spectral radiance and (2) the spectral covariance. The use of this attributes can significantly improve throughput performance of computing systems over conventional pixel-based methods. However, this approach assumes that background clutter can be approximated as having multivariate Gaussian distributions. Under such conditions, covariance propagations can be effectively performed from ground through the atmosphere. This paper explores this basic assumption using real-scene Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and examines how the partitioning of the scene into smaller and smaller segments influences local clutter characterization. It also presents a clutter characterization metric that helps explain the migration of the magnitude of statistical clutter from parent class to child sub-classes populations. It is shown that such a metric can be directly related to an approximate invariant between the parent class and its child classes.

Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

NASA Astrophysics Data System (ADS)

Marwaha, Richa; Kumar, Anil; Kumar, Arumugam Senthil

2015-01-01

Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

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

Leary, T.J.; Lamb, A.

The Department of Energy`s Office of Arms Control and Non-Proliferation (NN-20) has developed a suite of airborne remote sensing systems that simultaneously collect coincident data from a US Navy P-3 aircraft. The primary objective of the Airborne Multisensor Pod System (AMPS) Program is {open_quotes}to collect multisensor data that can be used for data research, both to reduce interpretation problems associated with data overload and to develop information products more complete than can be obtained from any single sensor.{close_quotes} The sensors are housed in wing-mounted pods and include: a Ku-Band Synthetic Aperture Radar; a CASI Hyperspectral Imager; a Daedalus 3600 Airbornemore » Multispectral Scanner; a Wild Heerbrugg RC-30 motion compensated large format camera; various high resolution, light intensified and thermal video cameras; and several experimental sensors (e.g. the Portable Hyperspectral Imager of Low-Light Spectroscopy (PHILLS)). Over the past year or so, the Coastal Marine Resource Assessment (CAMRA) group at the Florida Department of Environmental Protection`s Marine Research Institute (FMRI) has been working with the Department of Energy through the Naval Research Laboratory to develop applications and products from existing data. Considerable effort has been spent identifying image formats integration parameters. 2 refs., 3 figs., 2 tabs.« less

Hyperspectral Sensors Final Report CRADA No. TC02173.0

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

Priest, R. E.; Sauvageau, J. E.

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Science Applications International Corporation (SAIC), National Security Space Operations/SRBU, to develop longwave infrared (LWIR) hyperspectral imaging (HSI) sensors for airborne and potentially ground and space, platforms. LLNL has designed and developed LWIR HSI sensors since 1995. The current generation of these sensors has applications to users within the U.S. Department of Defense and the Intelligence Community. User needs are for multiple copies provided by commercial industry. To gain the most benefit from the U.S. Government’s prior investments inmore » LWIR HSI sensors developed at LLNL, transfer of technology and know-how from LLNL HSI experts to commercial industry was needed. The overarching purpose of the CRADA project was to facilitate the transfer of the necessary technology from LLNL to SAIC thereby allowing the U.S. Government to procure LWIR HSI sensors from this company.« less

Side scanner for supermarkets: a new scanner design standard

NASA Astrophysics Data System (ADS)

Cheng, Charles K.; Cheng, J. K.

1996-09-01

High speed UPC bar code has become a standard mode of data capture for supermarkets in the US, Europe, and Japan. The influence of the ergonomics community on the design of the scanner is evident. During the past decade the ergonomic issues of cashier in check-outs has led to occupational hand-wrist cumulative trauma disorders, in most cases causing carpal tunnel syndrome, a permanent hand injury. In this paper, the design of a side scanner to resolve the issues is discussed. The complex optical module and the sensor for aforesaid side scanner is described. The ergonomic advantages offer the old counter mounted vertical scanner has been experimentally proved by the industrial funded study at an independent university.

A Fast Smoothing Algorithm for Post-Processing of Surface Reflectance Spectra Retrieved from Airborne Imaging Spectrometer Data

PubMed Central

Gao, Bo-Cai; Liu, Ming

2013-01-01

Surface reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a fast smoothing technique for post-processing of retrieved surface reflectance spectra. In the present spectral smoothing technique, model-derived reflectance spectra are first fit using moving filters derived with a cubic spline smoothing algorithm. A common gain curve, which contains minor artifacts in the model-derived reflectance spectra, is then derived. This gain curve is finally applied to all of the reflectance spectra in a scene to obtain the spectrally smoothed surface reflectance spectra. Results from analysis of hyperspectral imaging data collected with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data are given. Comparisons between the smoothed spectra and those derived with the empirical line method are also presented. PMID:24129022

Wavelet compression techniques for hyperspectral data

NASA Technical Reports Server (NTRS)

Evans, Bruce; Ringer, Brian; Yeates, Mathew

1994-01-01

Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data, we are not aware of any published results for hyperspectral data. Three algorithms for hyperspectral data compression are compared. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet

Improving three-tier environmental assessment model by using a 3D scanning FLS-AM series hyperspectral lidar

NASA Astrophysics Data System (ADS)

Samberg, Andre; Babichenko, Sergei; Poryvkina, Larisa

2005-05-01

Delay between the time when natural disaster, for example, oil accident in coastal water, occurred and the time when environmental protection actions, for example, water and shoreline clean-up, started is of significant importance. Mostly remote sensing techniques are considered as (near) real-time and suitable for multiple tasks. These techniques in combination with rapid environmental assessment methodologies would form multi-tier environmental assessment model, which allows creating (near) real-time datasets and optimizing sampling scenarios. This paper presents the idea of three-tier environmental assessment model. Here all three tiers are briefly described to show the linkages between them, with a particular focus on the first tier. Furthermore, it is described how large-scale environmental assessment can be improved by using an airborne 3-D scanning FLS-AM series hyperspectral lidar. This new aircraft-based sensor is typically applied for oil mapping on sea/ground surface and extracting optical features of subjects. In general, a sampling network, which is based on three-tier environmental assessment model, can include ship(s) and aircraft(s). The airborne 3-D scanning FLS-AM series hyperspectral lidar helps to speed up the whole process of assessing of area of natural disaster significantly, because this is a real-time remote sensing mean. For instance, it can deliver such information as georeferenced oil spill position in WGS-84, the estimated size of the whole oil spill, and the estimated amount of oil in seawater or on ground. All information is produced in digital form and, thus, can be directly transferred into a customer"s GIS (Geographical Information System) system.

Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo G.; Fei, Baowei

2014-03-01

The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.

Validating Cryosat-2 elevation estimates with airborne laser scanner data for the Greenland ice sheet, Austfonna and Devon ice caps

NASA Astrophysics Data System (ADS)

Simonsen, Sebastian B.; Sandberg Sørensen, Louise; Nilsson, Johan; Helm, Veit; Langley, Kirsty A.; Forsberg, Rene; Hvidegaard, Sine M.; Skourup, Henriette

2015-04-01

The ESA CryoSat-2 satellite, launched in late 2010, carries a new type of radar altimeter especially designed for monitoring changes of sea and land ice. The radar signal might penetrate into the snow pack and the depth of the radar reflecting surface depends on the ratio between the surface and the volume backscatter, which is a function of several different properties such as snow density, crystal structure and surface roughness. In case of large volume scatter, the radar waveforms become broad and the determination of the range (surface elevation) becomes more difficult. Different algorithms (retrackers) are used for the range determination, and estimated surface penetration is highly dependent on the applied retracker. As part of the ESA-CryoVEx/CryoVal-Land Ice projects, DTU Space has gathered accurate airborne laser scanner elevation measurements. Sites on the Greenland ice sheet, Austfonna and Devon ice caps, has been surveyed repeatedly, aligned with Cryosat-2 ground tracks and surface experiments. Here, we utilize elevation estimates from available Cryosat-2 retrackers (ESA level-2 retracker, DTU retracker, etc.) and validate the elevation measurements against ESA-CryoVEx campaigns. A difference between laser and radar elevations is expected due to radar penetration issues, however an inter-comparison between retrackers will shed light on individual performances and biases. Additionally, the geo-location of the radar return will also be a determining factor for the precision. Ultimately, the use of multiple retrackers can provide information about subsurface conditions and utilize more of the waveform information than presently used in radar altimetry.

Multiview hyperspectral topography of tissue structural and functional characteristics

NASA Astrophysics Data System (ADS)

Zhang, Shiwu; Liu, Peng; Huang, Jiwei; Xu, Ronald

2012-12-01

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. However, many clinical imaging systems have limitations and fail to provide noninvasive, real time, and quantitative assessment of biological tissue in an operation room. To overcome these limitations, we developed and tested a multiview hyperspectral imaging system. The multiview hyperspectral imaging system integrated the multiview and the hyperspectral imaging techniques in a single portable unit. Four plane mirrors are cohered together as a multiview reflective mirror set with a rectangular cross section. The multiview reflective mirror set was placed between a hyperspectral camera and the measured biological tissue. For a single image acquisition task, a hyperspectral data cube with five views was obtained. The five-view hyperspectral image consisted of a main objective image and four reflective images. Three-dimensional topography of the scene was achieved by correlating the matching pixels between the objective image and the reflective images. Three-dimensional mapping of tissue oxygenation was achieved using a hyperspectral oxygenation algorithm. The multiview hyperspectral imaging technique is currently under quantitative validation in a wound model, a tissue-simulating blood phantom, and an in vivo biological tissue model. The preliminary results have demonstrated the technical feasibility of using multiview hyperspectral imaging for three-dimensional topography of tissue functional properties.

Multiview hyperspectral topography of tissue structural and functional characteristics

NASA Astrophysics Data System (ADS)

Liu, Peng; Huang, Jiwei; Zhang, Shiwu; Xu, Ronald X.

2016-01-01

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. We introduced and tested a multiview hyperspectral imaging technique for noninvasive topographic imaging of cutaneous wound oxygenation. The technique integrated a multiview module and a hyperspectral module in a single portable unit. Four plane mirrors were cohered to form a multiview reflective mirror set with a rectangular cross section. The mirror set was placed between a hyperspectral camera and the target biological tissue. For a single image acquisition task, a hyperspectral data cube with five views was obtained. The five-view hyperspectral image consisted of a main objective image and four reflective images. Three-dimensional (3-D) topography of the scene was achieved by correlating the matching pixels between the objective image and the reflective images. 3-D mapping of tissue oxygenation was achieved using a hyperspectral oxygenation algorithm. The multiview hyperspectral imaging technique was validated in a wound model, a tissue-simulating blood phantom, and in vivo biological tissue. The experimental results demonstrated the technical feasibility of using multiview hyperspectral imaging for 3-D topography of tissue functional properties.

Determination of forest fuels characteristics in mortality-affected Pinus forests using integrated hyperspectral and ALS data

NASA Astrophysics Data System (ADS)

Romero Ramirez, Francisco J.; Navarro-Cerrillo, Rafael Mª.; Varo-Martínez, Mª. Ángeles; Quero, Jose Luis; Doerr, Stefan; Hernández-Clemente, Rocío

2018-06-01

Widespread tree mortality caused by forest decline in recent decades has raised concern among forest managers about how to assess forest fuels in these conditions. To investigate this question, we developed and tested an objective, consistent approach to the characterization of canopy fuel metrics - such as fuel load (FL), live fuel moisture content (LFMC), and live-dead ratio (LDR) - by integrating airborne laser scanning (ALS) and hyperspectral data to produce more-accurate estimates at the stand level. Regression models were developed for Pinus sylvestris and P. nigra stands representative of pine plantations in southern Spain, using field data acquired for different spatial fuel types and distributions as well as high resolution airborne hyperspectral data (AHS) and ALS datasets. Strong relationships were found between ALS and FL using a density of 2 points m-2 (R2 = 0.64) and between LFMC and Temperature/NDVI index at a spatial resolution of 5 m (R2 = 0.91). The red edge normalized index provided the highest separability (Jeffries-Matusita distance = 1.83) between types of LDR. The plot-aggregate ALS and AHS metrics performed better at spatial resolutions of 5 m and 2 points m-2 than at other scales. Cartography of the estimations of FL, LFMC, and LDR made using the empirical models from the ALS and AHS data showed a mean FL value of 65.87 Mg ha-1, an average LFMC content of 57.51%, and 30.75% of the surface classified as dead fuel (≥60% defoliation). The results suggest that our remote sensing approach could improve the estimation of canopy fuels characteristics at higher spatial resolutions as well as estimations of fuel cartography, to assist the planning and management of fuel reduction treatments.

Hyperspectral remote sensing for terrestrial applications

USGS Publications Warehouse

Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Murali Krishna Gumma,; Venkateswarlu Dheeravath,

2015-01-01

Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum.” However, Jensen (2004) defines hyperspectral remote sensing as “The simultaneous acquisition of images in many relatively narrow, contiguous and/or non contiguous spectral bands throughout the ultraviolet, visible, and infrared portions of the electromagnetic spectrum.

Applications of multi-season hyperspectral remote sensing for acid mine water characterization and mapping of secondary iron minerals associated with acid mine drainage

NASA Astrophysics Data System (ADS)

Davies, Gwendolyn E.

Acid mine drainage (AMD) resulting from the oxidation of sulfides in mine waste is a major environmental issue facing the mining industry today. Open pit mines, tailings ponds, ore stockpiles, and waste rock dumps can all be significant sources of pollution, primarily heavy metals. These large mining-induced footprints are often located across vast geographic expanses and are difficult to access. With the continuing advancement of imaging satellites, remote sensing may provide a useful monitoring tool for pit lake water quality and the rapid assessment of abandoned mine sites. This study explored the applications of laboratory spectroscopy and multi-season hyperspectral remote sensing for environmental monitoring of mine waste environments. Laboratory spectral experiments were first performed on acid mine waters and synthetic ferric iron solutions to identify and isolate the unique spectral properties of mine waters. These spectral characterizations were then applied to airborne hyperspectral imagery for identification of poor water quality in AMD ponds at the Leviathan Mine Superfund site, CA. Finally, imagery varying in temporal and spatial resolutions were used to identify changes in mineralogy over weathering overburden piles and on dry AMD pond liner surfaces at the Leviathan Mine. Results show the utility of hyperspectral remote sensing for monitoring a diverse range of surfaces associated with AMD.

Aerosol Properties Derived from Airborne Sky Radiance and Direct Beam Measurements in Recent NASA and DoE Field Campaigns

NASA Technical Reports Server (NTRS)

Redemann, J.; Flynn, C. J.; Shinozuka, Y.; Russell, P. B.; Kacenelenbogen, M.; Segal-Rosenheimer, M.; Livingston, J. M.; Schmid, B.; Dunagan, S. E.; Johnson, R. R.;

Hyperspectral CMOS imager

NASA Astrophysics Data System (ADS)

Jerram, P. A.; Fryer, M.; Pratlong, J.; Pike, A.; Walker, A.; Dierickx, B.; Dupont, B.; Defernez, A.

2017-11-01

CCDs have been used for many years for Hyperspectral imaging missions and have been extremely successful. These include the Medium Resolution Imaging Spectrometer (MERIS) [1] on Envisat, the Compact High Resolution Imaging Spectrometer (CHRIS) on Proba and the Ozone Monitoring Instrument operating in the UV spectral region. ESA are also planning a number of further missions that are likely to use CCD technology (Sentinel 3, 4 and 5). However CMOS sensors have a number of advantages which means that they will probably be used for hyperspectral applications in the longer term. There are two main advantages with CMOS sensors: First a hyperspectral image consists of spectral lines with a large difference in intensity; in a frame transfer CCD the faint spectral lines have to be transferred through the part of the imager illuminated by intense lines. This can lead to cross-talk and whilst this problem can be reduced by the use of split frame transfer and faster line rates CMOS sensors do not require a frame transfer and hence inherently will not suffer from this problem. Second, with a CMOS sensor the intense spectral lines can be read multiple times within a frame to give a significant increase in dynamic range. We will describe the design, and initial test of a CMOS sensor for use in hyperspectral applications. This device has been designed to give as high a dynamic range as possible with minimum cross-talk. The sensor has been manufactured on high resistivity epitaxial silicon wafers and is be back-thinned and left relatively thick in order to obtain the maximum quantum efficiency across the entire spectral range

Thin-film tunable filters for hyperspectral fluorescence microscopy

PubMed Central

Favreau, Peter; Hernandez, Clarissa; Lindsey, Ashley Stringfellow; Alvarez, Diego F.; Rich, Thomas; Prabhat, Prashant

2013-01-01

Abstract. Hyperspectral imaging is a powerful tool that acquires data from many spectral bands, forming a contiguous spectrum. Hyperspectral imaging was originally developed for remote sensing applications; however, hyperspectral techniques have since been applied to biological fluorescence imaging applications, such as fluorescence microscopy and small animal fluorescence imaging. The spectral filtering method largely determines the sensitivity and specificity of any hyperspectral imaging system. There are several types of spectral filtering hardware available for microscopy systems, most commonly acousto-optic tunable filters (AOTFs) and liquid crystal tunable filters (LCTFs). These filtering technologies have advantages and disadvantages. Here, we present a novel tunable filter for hyperspectral imaging—the thin-film tunable filter (TFTF). The TFTF presents several advantages over AOTFs and LCTFs, most notably, a high percentage transmission and a high out-of-band optical density (OD). We present a comparison of a TFTF-based hyperspectral microscopy system and a commercially available AOTF-based system. We have characterized the light transmission, wavelength calibration, and OD of both systems, and have then evaluated the capability of each system for discriminating between green fluorescent protein and highly autofluorescent lung tissue. Our results suggest that TFTFs are an alternative approach for hyperspectral filtering that offers improved transmission and out-of-band blocking. These characteristics make TFTFs well suited for other biomedical imaging devices, such as ophthalmoscopes or endoscopes. PMID:24077519

51. View of upper radar scanner switch in radar scanner ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

51. View of upper radar scanner switch in radar scanner building 105 from upper catwalk level showing emanating waveguides from upper switch (upper one-fourth of photograph) and emanating waveguides from lower radar scanner switch in vertical runs. - Clear Air Force Station, Ballistic Missile Early Warning System Site II, One mile west of mile marker 293.5 on Parks Highway, 5 miles southwest of Anderson, Anderson, Denali Borough, AK

EAQUATE: An International Experiment for Hyper-Spectral Atmospheric Sounding Validation

NASA Technical Reports Server (NTRS)

Taylor, J. P.; Smith, W.; Cuomo, V.; Larar, A.; Zhou, D.; Serio, C.; Maestri, T.; Rizzi, R.; Newman, S.; Antonelli, P.;

Detection of Tephra Layers in Antarctic Sediment Cores with Hyperspectral Imaging

PubMed Central

Aymerich, Ismael F.; Oliva, Marc; Giralt, Santiago; Martín-Herrero, Julio

2016-01-01

Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras. PMID:26815202

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

PubMed

Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

2018-01-01

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

Hyperspectral Mineral Mapping in Support of Geothermal Exploration: Examples from Long Valley Caldera, CA and Dixie Valley, NV, USA

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

Martini, B; Silver, E; Pickles, W

2004-03-25

Growing interest and exploration dollars within the geothermal sector have paved the way for increasingly sophisticated suites of geophysical and geochemical tools and methodologies. The efforts to characterize and assess known geothermal fields and find new, previously unknown resources has been aided by the advent of higher spatial resolution airborne geophysics (e.g. aeromagnetics), development of new seismic processing techniques, and the genesis of modern multi-dimensional fluid flow and structural modeling algorithms, just to name a few. One of the newest techniques on the scene, is hyperspectral imaging. Really an optical analytical geochemical tool, hyperspectral imagers (or imaging spectrometers as theymore » are also called), are generally flown at medium to high altitudes aboard mid-sized aircraft and much in the same way more familiar geophysics are flown. The hyperspectral data records a continuous spatial record of the earth's surface, as well as measuring a continuous spectral record of reflected sunlight or emitted thermal radiation. This high fidelity, uninterrupted spatial and spectral record allows for accurate material distribution mapping and quantitative identification at the pixel to sub-pixel level. In volcanic/geothermal regions, this capability translates to synoptic, high spatial resolution, large-area mineral maps generated at time scales conducive to both the faster pace of the exploration and drilling managers, as well as to the slower pace of geologists and other researchers trying to understand the geothermal system over the long run.« less

Hyperspectral Mineral Mapping in Support of Geothermal Exploration: Examples from Long Valley Caldera, CA and Dixie Valley, NV, USA

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

Pickles, W L; Martini, B A; Silver, E A

2004-03-03

Growing interest and exploration dollars within the geothermal sector have paved the way for increasingly sophisticated suites of geophysical and geochemical tools and methodologies. The efforts to characterize and assess known geothermal fields and find new, previously unknown resources has been aided by the advent of higher spatial resolution airborne geophysics (e.g. aeromagnetics), development of new seismic processing techniques, and the genesis of modern multi-dimensional fluid flow and structural modeling algorithms, just to name a few. One of the newest techniques on the scene, is hyperspectral imaging. Really an optical analytical geochemical tool, hyperspectral imagers (or imaging spectrometers as theymore » are also called), are generally flown at medium to high altitudes aboard mid-sized aircraft and much in the same way more familiar geophysics are flown. The hyperspectral data records a continuous spatial record of the earth's surface, as well as measuring a continuous spectral record of reflected sunlight or emitted thermal radiation. This high fidelity, uninterrupted spatial and spectral record allows for accurate material distribution mapping and quantitative identification at the pixel to sub-pixel level. In volcanic/geothermal regions, this capability translates to synoptic, high spatial resolution, large-area mineral maps generated at time scales conducive to both the faster pace of the exploration and drilling managers, as well as to the slower pace of geologists and other researchers trying to understand the geothermal system over the long run.« less

Hyperspectral imaging for food processing automation

NASA Astrophysics Data System (ADS)

Park, Bosoon; Lawrence, Kurt C.; Windham, William R.; Smith, Doug P.; Feldner, Peggy W.

2002-11-01

This paper presents the research results that demonstrates hyperspectral imaging could be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses, and potential application for real-time, on-line processing of poultry for automatic safety inspection. The hyperspectral imaging system included a line scan camera with prism-grating-prism spectrograph, fiber optic line lighting, motorized lens control, and hyperspectral image processing software. Hyperspectral image processing algorithms, specifically band ratio of dual-wavelength (565/517) images and thresholding were effective on the identification of fecal and ingesta contamination of poultry carcasses. A multispectral imaging system including a common aperture camera with three optical trim filters (515.4 nm with 8.6- nm FWHM), 566.4 nm with 8.8-nm FWHM, and 631 nm with 10.2-nm FWHM), which were selected and validated by a hyperspectral imaging system, was developed for a real-time, on-line application. A total image processing time required to perform the current multispectral images captured by a common aperture camera was approximately 251 msec or 3.99 frames/sec. A preliminary test shows that the accuracy of real-time multispectral imaging system to detect feces and ingesta on corn/soybean fed poultry carcasses was 96%. However, many false positive spots that cause system errors were also detected.

Super-resolution reconstruction of hyperspectral images.

PubMed

Akgun, Toygar; Altunbasak, Yucel; Mersereau, Russell M

2005-11-01

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying super-resolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super-resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions.

Feasibility Study of Radiometry for Airborne Detection of Aviation Hazards

NASA Technical Reports Server (NTRS)

Gimmestad, Gary G.; Papanicolopoulos, Chris D.; Richards, Mark A.; Sherman, Donald L.; West, Leanne L.; Johnson, James W. (Technical Monitor)

2001-01-01

Radiometric sensors for aviation hazards have the potential for widespread and inexpensive deployment on aircraft. This report contains discussions of three aviation hazards - icing, turbulence, and volcanic ash - as well as candidate radiometric detection techniques for each hazard. Dual-polarization microwave radiometry is the only viable radiometric technique for detection of icing conditions, but more research will be required to assess its usefulness to the aviation community. Passive infrared techniques are being developed for detection of turbulence and volcanic ash by researchers in this country and also in Australia. Further investigation of the infrared airborne radiometric hazard detection approaches will also be required in order to develop reliable detection/discrimination techniques. This report includes a description of a commercial hyperspectral imager for investigating the infrared detection techniques for turbulence and volcanic ash.

Investigation of Greenhouse Gas Emissions by Surface, Airborne, and Satellite on Local to Continental-Scale

NASA Astrophysics Data System (ADS)

Leifer, I.; Tratt, D. M.; Egland, E. T.; Gerilowski, K.; Vigil, S. A.; Buchwitz, M.; Krings, T.; Bovensmann, H.; Krautwurst, S.; Burrows, J. P.

2013-12-01

In situ meteorological observations, including 10-m winds (U), in conjunction with greenhouse gas (GHG - methane, carbon dioxide, water vapor) measurements by continuous wave Cavity Enhanced Absorption Spectroscopy (CEAS) were conducted onboard two specialized platforms: MACLab (Mobile Atmospheric Composition Laboratory in a RV) and AMOG Surveyor (AutoMObile Greenhouse gas) - a converted commuter automobile. AMOG Surveyor data were collected for numerous southern California sources including megacity, geology, fossil fuel industrial, animal husbandry, and landfill operations. MACLab investigated similar sources along with wetlands on a transcontinental scale from California to Florida to Nebraska covering more than 15,000 km. Custom software allowing real-time, multi-parameter data visualization (GHGs, water vapor, temperature, U, etc.) improved plume characterization and was applied to large urban area and regional-scale sources. The capabilities demonstrated permit calculation of source emission strength, as well as enable documenting microclimate variability. GHG transect data were compared with airborne HyperSpectral Imaging data to understand temporal and spatial variability and to ground-truth emission strength derived from airborne imagery. These data also were used to validate satellite GHG products from SCIAMACHY (2003-2005) and GOSAT (2009-2013) that are currently being analyzed to identify significant decadal-scale changes in North American GHG emission patterns resulting from changes in anthropogenic and natural sources. These studies lay the foundation for the joint ESA/NASA COMEX campaign that will map GHG plumes by remote sensing and in situ measurements for a range of strong sources to derive emission strength through inverse plume modeling. COMEX is in support of the future GHG monitoring satellites, such as CarbonSat and HyspIRI. GHG transect data were compared with airborne HyperSpectral Imaging data to understand temporal and spatial variability

Using hyperspectral imaging technology to identify diseased tomato leaves

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Zhao, Xueguan; Meng, Zhijun; Zou, Wei

2016-11-01

In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea.

PubMed

Angelliaume, Sébastien; Ceamanos, Xavier; Viallefont-Robinet, Françoise; Baqué, Rémi; Déliot, Philippe; Miegebielle, Véronique

2017-08-02

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface.

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region.

NASA Astrophysics Data System (ADS)

Frankenberg, C.

2016-12-01

Methane (CH4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ˜ 2 kg/h to 5 kg/h through ˜ 5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, natural seeps and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. We will summarize the campaign results and provide an overview of how airborne remote sensing can be used to detect and infer methane fluxes over widespread geographic areas and how new instrumentation could be used to perform similar observations from space.

Hyperspectral remote sensing of coral reefs: Deriving bathymetry, aquatic optical properties and a benthic spectral unmixing classification using AVIRIS data in the Hawaiian Islands

NASA Astrophysics Data System (ADS)

Goodman, James Ansell

My research focuses on the development and application of hyperspectral remote sensing as a valuable component in the assessment and management of coral ecosystems. Remote sensing provides an important quantitative ability to investigate the spatial dynamics of coral health and evaluate the impacts of local, regional and global change on this important natural resource. Furthermore, advances in detector capabilities and analysis methods, particularly with respect to hyperspectral remote sensing, are also increasing the accuracy and level of effectiveness of the resulting data products. Using imagery of Kaneohe Bay and French Frigate Shoals in the Hawaiian Islands, acquired in 2000 by NASA's Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), I developed, applied and evaluated algorithms for analyzing coral reefs using hyperspectral remote sensing data. Research included developing methods for acquiring in situ underwater reflectance, collecting spectral measurements of the dominant bottom components in Kaneohe Bay, applying atmospheric correction and sunglint removal algorithms, employing a semianalytical optimization model to derive bathymetry and aquatic optical properties, and developing a linear unmixing approach for deriving bottom composition. Additionally, algorithm development focused on using fundamental scientific principles to facilitate the portability of methods to diverse geographic locations and across variable environmental conditions. Assessments of this methodology compared favorably with available field measurements and habitat information, and the overall analysis demonstrated the capacity to derive information on water properties, bathymetry and habitat composition. Thus, results illustrated a successful approach for extracting environmental information and habitat composition from a coral reef environment using hyperspectral remote sensing.

Hyperspectral data discrimination methods

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

2000-12-01

Hyperspectral data provides spectral response information that provides detailed chemical, moisture, and other description of constituent parts of an item. These new sensor data are useful in USDA product inspection. However, such data introduce problems such as the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). Several two-step methods are compared to a new and preferable single-step spectral decomposition algorithm. Initial results on hyperspectral data for good/bad almonds and for good/bad (aflatoxin infested) corn kernels are presented. The hyperspectral application addressed differs greatly from prior USDA work (PLS) in which the level of a specific channel constituent in food was estimated. A validation set (separate from the test set) is used in selecting algorithm parameters. Threshold parameters are varied to select the best Pc operating point. Initial results show that nonlinear features yield improved performance.

Pathway to future sustainable land imaging: the compact hyperspectral prism spectrometer

NASA Astrophysics Data System (ADS)

Kampe, Thomas U.; Good, William S.

2017-09-01

NASA's Sustainable Land Imaging (SLI) program, managed through the Earth Science Technology Office, aims to develop technologies that will provide future Landsat-like measurements. SLI aims to develop a new generation of smaller, more capable, less costly payloads that meet or exceed current imaging capabilities. One projects funded by this program is Ball's Compact Hyperspectral Prism Spectrometer (CHPS), a visible-to-shortwave imaging spectrometer that provides legacy Landsat data products as well as hyperspectral coverage suitable for a broad range of land science products. CHPS exhibits extremely low straylight and accommodates full aperture, full optical path calibration needed to ensure the high radiometric accuracy demanded by SLI measurement objectives. Low polarization sensitivity in visible to near-infrared bands facilitates coastal water science as first demonstrated by the exceptional performance of the Operational Land Imager. Our goal is to mature CHPS imaging spectrometer technology for infusion into the SLI program. Our effort builds on technology development initiated by Ball IRAD investment and includes laboratory and airborne demonstration, data distribution to science collaborators, and maturation of technology for spaceborne demonstration. CHPS is a three year program with expected exiting technology readiness of TRL-6. The 2013 NRC report Landsat and Beyond: Sustaining and Enhancing the Nations Land Imaging Program recommended that the nation should "maintain a sustained, space-based, land-imaging program, while ensuring the continuity of 42-years of multispectral information." We are confident that CHPS provides a path to achieve this goal while enabling new science measurements and significantly reducing the cost, size, and volume of the VSWIR instrument.

Hyperspectral imaging for nondestructive evaluation of tomatoes

USDA-ARS?s Scientific Manuscript database

Machine vision methods for quality and defect evaluation of tomatoes have been studied for online sorting and robotic harvesting applications. We investigated the use of a hyperspectral imaging system for quality evaluation and defect detection for tomatoes. Hyperspectral reflectance images were a...

Simple models for complex natural surfaces - A strategy for the hyperspectral era of remote sensing

NASA Technical Reports Server (NTRS)

Adams, John B.; Smith, Milton O.; Gillespie, Alan R.

1989-01-01

A two-step strategy for analyzing multispectral images is described. In the first step, the analyst decomposes the signal from each pixel (as expressed by the radiance or reflectance values in each channel) into components that are contributed by spectrally distinct materials on the ground, and those that are due to atmospheric effects, instrumental effects, and other factors, such as illumination. In the second step, the isolated signals from the materials on the ground are selectively edited, and recombined to form various unit maps that are interpretable within the framework of field units. The approach has been tested on multispectral images of a variety of natural land surfaces ranging from hyperarid deserts to tropical rain forests. Data were analyzed from Landsat MSS (multispectral scanner) and TM (Thematic Mapper), the airborne NS001 TM simulator, Viking Lander and Orbiter, AIS, and AVRIS (Airborne Visible and Infrared Imaging Spectrometer).

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

Space-multiplexed optical scanner.

PubMed

Riza, Nabeel A; Yaqoob, Zahid

2004-05-01

A low-loss two-dimensional optical beam scanner that is capable of delivering large (e.g., > 10 degrees) angular scans along the elevation as well as the azimuthal direction is presented. The proposed scanner is based on a space-switched parallel-serial architecture that employs a coarse-scanner module and a fine-scanner module that produce an ultrahigh scan space-fill factor, e.g., 900 x 900 distinguishable beams in a 10 degrees (elevation) x 10 degrees (azimuth) scan space. The experimentally demonstrated one-dimensional version of the proposed scanner has a supercontinuous scan, 100 distinguishable beam spots in a 2.29 degrees total scan range, and 1.5-dB optical insertion loss.

Estimation of tissue optical parameters with hyperspectral imaging and spectral unmixing

NASA Astrophysics Data System (ADS)

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo G.; Fei, Baowei

2015-03-01

Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.

Comparing methods for analysis of biomedical hyperspectral image data

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter F.; Annamdevula, Naga S.; Rich, Thomas C.

2017-02-01

Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if" scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.

Spatial-scanning hyperspectral imaging probe for bio-imaging applications

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2016-03-01

The three common methods to perform hyperspectral imaging are the spatial-scanning, spectral-scanning, and snapshot methods. However, only the spectral-scanning and snapshot methods have been configured to a hyperspectral imaging probe as of today. This paper presents a spatial-scanning (pushbroom) hyperspectral imaging probe, which is realized by integrating a pushbroom hyperspectral imager with an imaging probe. The proposed hyperspectral imaging probe can also function as an endoscopic probe by integrating a custom fabricated image fiber bundle unit. The imaging probe is configured by incorporating a gradient-index lens at the end face of an image fiber bundle that consists of about 50 000 individual fiberlets. The necessary simulations, methodology, and detailed instrumentation aspects that are carried out are explained followed by assessing the developed probe's performance. Resolution test targets such as United States Air Force chart as well as bio-samples such as chicken breast tissue with blood clot are used as test samples for resolution analysis and for performance validation. This system is built on a pushbroom hyperspectral imaging system with a video camera and has the advantage of acquiring information from a large number of spectral bands with selectable region of interest. The advantages of this spatial-scanning hyperspectral imaging probe can be extended to test samples or tissues residing in regions that are difficult to access with potential diagnostic bio-imaging applications.

Self-adaptive road tracking in hyperspectral data for C-IED

NASA Astrophysics Data System (ADS)

Schilling, Hendrik; Gross, Wolfgang; Middelmann, Wolfgang

2012-09-01

For Counter Improvised Explosive Devices purposes, main routes including their vicinity are surveyed. In future military operations, small hyperspectral sensors will be used for ground covering reconnaissance, complementing images from infrared and high resolution sensors. They will be mounted on unmanned airborne vehicles and are used for on-line monitoring of convoy routes. Depending of the proximity to the road, different regions can be defined for threat assessment. Automatic road tracking can help choosing the correct areas of interest. Often, the exact discrimination between road and surroundings fails in conventional methods due to low contrast in pan-chromatic images at the road boundaries or occlusions. In this contribution, a novel real-time lock-on road tracking algorithm is introduced. It uses hyperspectral data and is specifically designed to address the afore- mentioned deficiencies of conventional methods. Local features are calculated from the high-resolution spectral signatures. They describe the similarity to the actual road cover and to either roadside. Classification is per- formed to discriminate the signatures. To improve robustness against variations in road cover, the classification results are used to progressively adapt the road and roadside classes. Occlusions are treated by predicting the course of the road and comparing the signatures in the target area to previously determined road cover signa- tures. The algorithm can be easily extended to show regions of varying threat, depending on the distance to the road. Thus, complex anomaly detectors and classification algorithms can be applied to a reduced data set. First experiments were performed for AISA Eagle II (400nm - 970nm) and AISA Hawk (970nm - 2450nm) data

Parallel hyperspectral image reconstruction using random projections

NASA Astrophysics Data System (ADS)

Sevilla, Jorge; Martín, Gabriel; Nascimento, José M. P.

2016-10-01

Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA). Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NVIDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.

VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

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

Serrato, M.; Jungho, I.; Jensen, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using threemore » different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.« less

Geographical classification of apple based on hyperspectral imaging

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

2013-05-01

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

Information Extraction in Tomb Pit Using Hyperspectral Data

NASA Astrophysics Data System (ADS)

Yang, X.; Hou, M.; Lyu, S.; Ma, S.; Gao, Z.; Bai, S.; Gu, M.; Liu, Y.

2018-04-01

Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

Removing cosmic spikes using a hyperspectral upper-bound spectrum method

DOE PAGES

Anthony, Stephen Michael; Timlin, Jerilyn A.

2016-11-04

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in amore » hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. As a result, a comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.« less

Removing cosmic spikes using a hyperspectral upper-bound spectrum method

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

Anthony, Stephen Michael; Timlin, Jerilyn A.

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in amore » hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. As a result, a comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.« less

Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method.

PubMed

Anthony, Stephen M; Timlin, Jerilyn A

2017-03-01

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

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

Scanner imaging systems, aircraft

NASA Technical Reports Server (NTRS)

Ungar, S. G.

1982-01-01

The causes and effects of distortion in aircraft scanner data are reviewed and an approach to reduce distortions by modelling the effect of aircraft motion on the scanner scene is discussed. With the advent of advanced satellite borne scanner systems, the geometric and radiometric correction of aircraft scanner data has become increasingly important. Corrections are needed to reliably simulate observations obtained by such systems for purposes of evaluation. It is found that if sufficient navigational information is available, aircraft scanner coordinates may be related very precisely to planimetric ground coordinates. However, the potential for a multivalue remapping transformation (i.e., scan lines crossing each other), adds an inherent uncertainty, to any radiometric resampling scheme, which is dependent on the precise geometry of the scan and ground pattern.

Mapping Soil Organic Matter with Hyperspectral Imaging

NASA Astrophysics Data System (ADS)

Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

2014-05-01

Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

Retrieval Lesson Learned from NAST-I Hyperspectral Data

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Smith, William L.; Liu, Xu; Larar, Allen M.; Mango, Stephen A.

2007-01-01

The retrieval lesson learned is important to many current and future hyperspectral remote sensors. Validated retrieval algorithms demonstrate the advancement of hyperspectral remote sensing capabilities to be achieved with current and future satellite instruments.

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.

Evaluation of a commercial flatbed document scanner and radiographic film scanner for radiochromic EBT film dosimetry

PubMed Central

Parker, Brent C.; Neck, Daniel W.; Henkelmann, Greg; Rosen, Isaac I.

2010-01-01

The purpose of this study was to quantify the performance and assess the utility of two different types of scanners for radiochromic EBT film dosimetry: a commercial flatbed document scanner and a widely used radiographic film scanner. We evaluated the Epson Perfection V700 Photo flatbed scanner and the Vidar VXR Dosimetry Pro Advantage scanner as measurement devices for radiochromic EBT film. Measurements were made of scan orientation effects, response uniformity, and scanner noise. Scanners were tested using films irradiated with eight separate 3×3 cm2 fields to doses ranging from 0.115–5.119 Gy. ImageJ and RIT software was used for analyzing the Epson and Vidar scans, respectively. For repeated scans of a single film, the measurements in each dose region were reproducible to within ±0.3% standard deviation (SD) with both scanners. Film‐to‐film variations for corresponding doses were measured to be within ±0.4% SD for both Epson scanner and Vidar scanners. Overall, the Epson scanner showed a 10% smaller range of pixel value compared to the Vidar scanner. Scanner noise was small: ±0.3% SD for the Epson and ±0.2% for the Vidar. Overall measurement uniformity for blank film in both systems was better than ±0.2%, provided that the leading and trailing 2 cm film edges were neglected in the Vidar system. In this region artifacts are attributed to the film rollers. Neither system demonstrated a clear measurement advantage. The Epson scanner is a relatively inexpensive method for analyzing radiochromic film, but there is a lack of commercially available software. For a clinic already using a Vidar scanner, applying it to radiochromic film is attractive because commercial software is available. However, care must be taken to avoid using the leading and trailing film edges. PACS number: 87.55.Qr

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

NASA Astrophysics Data System (ADS)

Liu, Haijian; Wu, Changshan

2018-06-01

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

Research on hyperspectral dynamic scene and image sequence simulation

NASA Astrophysics Data System (ADS)

Sun, Dandan; Liu, Fang; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

2016-10-01

This paper presents a simulation method of hyperspectral dynamic scene and image sequence for hyperspectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyperspectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyperspectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyperspectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyperspectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyperspectral images are consistent with the theoretical analysis results.

Research on hyperspectral dynamic scene and image sequence simulation

NASA Astrophysics Data System (ADS)

Sun, Dandan; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

2016-10-01

This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

Forensics for flatbed scanners

NASA Astrophysics Data System (ADS)

Gloe, Thomas; Franz, Elke; Winkler, Antje

2007-02-01

Within this article, we investigate possibilities for identifying the origin of images acquired with flatbed scanners. A current method for the identification of digital cameras takes advantage of image sensor noise, strictly speaking, the spatial noise. Since flatbed scanners and digital cameras use similar technologies, the utilization of image sensor noise for identifying the origin of scanned images seems to be possible. As characterization of flatbed scanner noise, we considered array reference patterns and sensor line reference patterns. However, there are particularities of flatbed scanners which we expect to influence the identification. This was confirmed by extensive tests: Identification was possible to a certain degree, but less reliable than digital camera identification. In additional tests, we simulated the influence of flatfielding and down scaling as examples for such particularities of flatbed scanners on digital camera identification. One can conclude from the results achieved so far that identifying flatbed scanners is possible. However, since the analyzed methods are not able to determine the image origin in all cases, further investigations are necessary.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea

PubMed Central

Angelliaume, Sébastien; Ceamanos, Xavier; Viallefont-Robinet, Françoise; Baqué, Rémi; Déliot, Philippe

2017-01-01

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface

A survey of landmine detection using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo

2017-02-01

Hyperspectral imaging is a trending technique in remote sensing that finds its application in many different areas, such as agriculture, mapping, target detection, food quality monitoring, etc. This technique gives the ability to remotely identify the composition of each pixel of the image. Therefore, it is a natural candidate for the purpose of landmine detection, thanks to its inherent safety and fast response time. In this paper, we will present the results of several studies that employed hyperspectral imaging for the purpose of landmine detection, discussing the different signal processing techniques used in this framework for hyperspectral image processing and target detection. Our purpose is to highlight the progresses attained in the detection of landmines using hyperspectral imaging and to identify possible perspectives for future work, in order to achieve a better detection in real-time operation mode.

Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

PubMed Central

Cavalli, Rosa Maria; Fusilli, Lorenzo; Pascucci, Simone; Pignatti, Stefano; Santini, Federico

2008-01-01

This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials. PMID:27879879

Multi- and hyperspectral geologic remote sensing: A review

NASA Astrophysics Data System (ADS)

van der Meer, Freek D.; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Hecker, Chris A.; Bakker, Wim H.; Noomen, Marleen F.; van der Meijde, Mark; Carranza, E. John M.; Smeth, J. Boudewijn de; Woldai, Tsehaie

2012-02-01

Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly

Hyperspectral range imaging for transportation systems evaluation

NASA Astrophysics Data System (ADS)

Bridgelall, Raj; Rafert, J. B.; Atwood, Don; Tolliver, Denver D.

2016-04-01

Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and space-borne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.

The Ring-Barking Experiment: Analysis of Forest Vitality Using Multi-Temporal Hyperspectral Data

NASA Astrophysics Data System (ADS)

Reichmuth, Anne; Bachmann, Martin; Heiden, Uta; Pinnel, Nicole; Holzwarth, Stefanie; Muller, Andreas; Henning, Lea; Einzmann, Kathrin; Immitzer, Markus; Seitz, Rudolf

2016-08-01

Through new operational optical spaceborne sensors (En- MAP and Sentinel-2) the impact analysis of climate change on forest ecosystems will be fostered. This analysis examines the potential of high spectral, spatial and temporal resolution data for detecting forest vegetation parameters, in particular Chlorophyll and Canopy Water content. The study site is a temperate spruce forest in Germany where in 2013 several trees were Ring-barked for a controlled die-off. During this experiment Ring- barked and Control trees were observed. Twelve airborne hyperspectral HySpex VNIR (Visible/Near Infrared) and SWIR (Shortwave Infrared) data with 1m spatial and 416 bands spectral resolution were acquired during the vegetation periods of 2013 and 2014. Additional laboratory spectral measurements of collected needle samples from Ring-barked and Control trees are available for needle level analysis. Index analysis of the laboratory measurements and image data are presented in this study.

Investigation of Tree Spectral Reflectance Characteristics Using a Mobile Terrestrial Line Spectrometer and Laser Scanner

PubMed Central

Lin, Yi; Puttonen, Eetu; Hyyppä, Juha

2013-01-01

In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt. PMID:23877127

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

NASA Technical Reports Server (NTRS)

Cetin, Haluk

1999-01-01

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and

Seagrass biomass and productivity in the Florida Keys, USA: ground-level and airborne measurements

NASA Astrophysics Data System (ADS)

Yarbro, L.; Carlson, P. R., Jr.; McHan, C.; Carlson, D. F.; Hu, C.; Danielson, T.; Durnan, B.; English, D. C.; Muller-Karger, F. E.; Yates, K. K.; Herwitz, S.; Merrill, J.; Mewes, T.

2013-12-01

Seagrass communities serve as essential habitat for fish and shellfish, and recent research indicates that they can play a significant role in reducing ocean acidification. As part of a collaborative project funded by the NASA ROSES program and administered by the NASA UAV Collaborative, we collected hyperspectral imagery of seagrass beds and measured productivity of Thalassia testudinum at Sugarloaf Key, Florida, in May 2012, October 2012, and May 2013. Our primary goal was to evaluate the utility of hyperspectral sensors, in general, and UAV platforms, in specific, to measure seagrass health and productivity. Airborne measurements using the AISA Eagle hyperspectral imaging system were carried out simultaneously with ground measurements of Thalassia fluorescence, oxygen metabolism, growth, and biomass, as well as remote sensing reflectance and several in situ optical properties. Water depths at the study site ranged from less than 1 m to 5 m. Phytoplankton chlorophyll-a concentrations (0.09-0.72 ug l-1), ag(440) (0-0.02 m-1), and turbidity (0.12-4.1 ntu) were relatively low for all three deployments, facilitating the collection of excellent imagery and application of water-column radiative-transfer corrections. Aboveground Thalassia and macroalgal biomass, at 18 sites in the study area, ranged from 210 to 690 and 11 to 590 gDW m-2, respectively. One-sided green leaf area index of Thalassia ranged from 0.7 to 3.0. Preliminary findings show that the sensitivity of relationships between seagrass productivity and biomass parameters and remotely-sensed habitat spectra is reduced with increasing water depth and, even in shallow water, is complicated by epiphytic algae and sediment coverage of leaf surfaces.

Naval EarthMap Observer (NEMO) Hyperspectral Remote Sensing Program

DTIC Science & Technology

2000-10-01

The NEMO hyperspectral remote sensing program will provide unclassified, space-based hyperspectral passive imagery at moderate resolution that offers substantial potential for direct use by Naval forces and the Civil Sector.

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

SSUSI-Lite: a far-ultraviolet hyper-spectral imager for space weather remote sensing

NASA Astrophysics Data System (ADS)

Ogorzalek, Bernard; Osterman, Steven; Carlsson, Uno; Grey, Matthew; Hicks, John; Hourani, Ramsey; Kerem, Samuel; Marcotte, Kathryn; Parker, Charles; Paxton, Larry J.

2015-09-01

SSUSI-Lite is a far-ultraviolet (115-180nm) hyperspectral imager for monitoring space weather. The SSUSI and GUVI sensors, its predecessors, have demonstrated their value as space weather monitors. SSUSI-Lite is a refresh of the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) design that has flown on the Defense Meteorological Satellite Program (DMSP) spacecraft F16 through F19. The refresh updates the 25-year-old design and insures that the next generation of SSUSI/GUVI sensors can be accommodated on any number of potential platforms. SSUSI-Lite maintains the same optical layout as SSUSI, includes updates to key functional elements, and reduces the sensor volume, mass, and power requirements. SSUSI-Lite contains an improved scanner design that results in precise mirror pointing and allows for variable scan profiles. The detector electronics have been redesigned to employ all digital pulse processing. The largest decrease in volume, mass, and power has been obtained by consolidating all control and power electronics into one data processing unit.

Camouflage target reconnaissance based on hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Hua, Wenshen; Guo, Tong; Liu, Xun

2015-08-01

Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

Hyperspectral imaging fluorescence excitation scanning for colon cancer detection

PubMed Central

Leavesley, Silas J.; Walters, Mikayla; Lopez, Carmen; Baker, Thomas; Favreau, Peter F.; Rich, Thomas C.; Rider, Paul F.; Boudreaux, Carole W.

2016-01-01

Abstract. Optical spectroscopy and hyperspectral imaging have shown the potential to discriminate between cancerous and noncancerous tissue with high sensitivity and specificity. However, to date, these techniques have not been effectively translated to real-time endoscope platforms. Hyperspectral imaging of the fluorescence excitation spectrum represents new technology that may be well suited for endoscopic implementation. However, the feasibility of detecting differences between normal and cancerous mucosa using fluorescence excitation-scanning hyperspectral imaging has not been evaluated. The goal of this study was to evaluate the initial feasibility of using fluorescence excitation-scanning hyperspectral imaging for measuring changes in fluorescence excitation spectrum concurrent with colonic adenocarcinoma using a small pre-pilot-scale sample size. Ex vivo analysis was performed using resected pairs of colorectal adenocarcinoma and normal mucosa. Adenocarcinoma was confirmed by histologic evaluation of hematoxylin and eosin (H&E) permanent sections. Specimens were imaged using a custom hyperspectral imaging fluorescence excitation-scanning microscope system. Results demonstrated consistent spectral differences between normal and cancerous tissues over the fluorescence excitation range of 390 to 450 nm that could be the basis for wavelength-dependent detection of colorectal cancers. Hence, excitation-scanning hyperspectral imaging may offer an alternative approach for discriminating adenocarcinoma from surrounding normal colonic mucosa, but further studies will be required to evaluate the accuracy of this approach using a larger patient cohort. PMID:27792808

Hyperspectral imaging fluorescence excitation scanning for colon cancer detection

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Walters, Mikayla; Lopez, Carmen; Baker, Thomas; Favreau, Peter F.; Rich, Thomas C.; Rider, Paul F.; Boudreaux, Carole W.

2016-10-01

Optical spectroscopy and hyperspectral imaging have shown the potential to discriminate between cancerous and noncancerous tissue with high sensitivity and specificity. However, to date, these techniques have not been effectively translated to real-time endoscope platforms. Hyperspectral imaging of the fluorescence excitation spectrum represents new technology that may be well suited for endoscopic implementation. However, the feasibility of detecting differences between normal and cancerous mucosa using fluorescence excitation-scanning hyperspectral imaging has not been evaluated. The goal of this study was to evaluate the initial feasibility of using fluorescence excitation-scanning hyperspectral imaging for measuring changes in fluorescence excitation spectrum concurrent with colonic adenocarcinoma using a small pre-pilot-scale sample size. Ex vivo analysis was performed using resected pairs of colorectal adenocarcinoma and normal mucosa. Adenocarcinoma was confirmed by histologic evaluation of hematoxylin and eosin (H&E) permanent sections. Specimens were imaged using a custom hyperspectral imaging fluorescence excitation-scanning microscope system. Results demonstrated consistent spectral differences between normal and cancerous tissues over the fluorescence excitation range of 390 to 450 nm that could be the basis for wavelength-dependent detection of colorectal cancers. Hence, excitation-scanning hyperspectral imaging may offer an alternative approach for discriminating adenocarcinoma from surrounding normal colonic mucosa, but further studies will be required to evaluate the accuracy of this approach using a larger patient cohort.

High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier

NASA Astrophysics Data System (ADS)

Mirik, Mustafa; Ansley, R. James; Steddom, Karl; Rush, Charles M.; Michels, Gerald J.; Workneh, Fekede; Cui, Song; Elliott, Norman C.

2014-01-01

The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA, Diuraphis noxia) is an insect pest that causes damage to wheat (Triticum aestivum L.). We proposed that concentrated RWA feeding areas, referred to as "hot spots," could be identified and isolated from uninfested areas within a field for site specific aphid management using remotely sensed data. Our objectives were to (1) investigate the reflectance characteristics of infested and uninfested wheat by RWA and (2) evaluate utility of airborne hyperspectral imagery with 1-m spatial resolution for detecting, quantifying, and mapping RWA infested areas in commercial winter wheat fields using the constrained energy minimization classifier. Percent surface reflectance from uninfested wheat was lower in the visible and higher in the near infrared portions of the spectrum when compared with RWA-infested wheat. The overall classification accuracies of >89% for damage detection were achieved. These results indicate that hyperspectral imagery can be effectively used for accurate detection and quantification of RWA infestation in wheat for site-specific aphid management.

Sparsely-sampled hyperspectral stimulated Raman scattering microscopy: a theoretical investigation

NASA Astrophysics Data System (ADS)

Lin, Haonan; Liao, Chien-Sheng; Wang, Pu; Huang, Kai-Chih; Bouman, Charles A.; Kong, Nan; Cheng, Ji-Xin

2017-02-01

A hyperspectral image corresponds to a data cube with two spatial dimensions and one spectral dimension. Through linear un-mixing, hyperspectral images can be decomposed into spectral signatures of pure components as well as their concentration maps. Due to this distinct advantage on component identification, hyperspectral imaging becomes a rapidly emerging platform for engineering better medicine and expediting scientific discovery. Among various hyperspectral imaging techniques, hyperspectral stimulated Raman scattering (HSRS) microscopy acquires data in a pixel-by-pixel scanning manner. Nevertheless, current image acquisition speed for HSRS is insufficient to capture the dynamics of freely moving subjects. Instead of reducing the pixel dwell time to achieve speed-up, which would inevitably decrease signal-to-noise ratio (SNR), we propose to reduce the total number of sampled pixels. Location of sampled pixels are carefully engineered with triangular wave Lissajous trajectory. Followed by a model-based image in-painting algorithm, the complete data is recovered for linear unmixing. Simulation results show that by careful selection of trajectory, a fill rate as low as 10% is sufficient to generate accurate linear unmixing results. The proposed framework applies to any hyperspectral beam-scanning imaging platform which demands high acquisition speed.

Utility of hyperspectral imagers in the mining industry: Italy's gypsum reserves

NASA Astrophysics Data System (ADS)

Wilson, Janette H.; Greenberger, Rebecca N.

2014-05-01

The mining industry is plagued with socioeconomic and safety roadblocks with not many solutions in the midst of a demanding market. As more and more geologic research using hyperspectral technology has been performed, along with an affordable price point for commercial use of hyperspectral technology, the benefits of hyperspectral imaging to the mining industry has become apparent. This study identifies the key areas of use for hyperspectral imaging in the mining industry through a case study of gypsum mine samples obtained from a mine in central Tuscany.

Hyperspectral Image Classification via Kernel Sparse Representation

DTIC Science & Technology

2013-01-01

classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model , where...joint sparsity model , where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training...hyperspectral imagery, joint spar- sity model , kernel methods, sparse representation. I. INTRODUCTION HYPERSPECTRAL imaging sensors capture images

Uncooled long-wave infrared hyperspectral imaging

NASA Technical Reports Server (NTRS)

Lucey, Paul G. (Inventor)

2006-01-01

A long-wave infrared hyperspectral sensor device employs a combination of an interferometer with an uncooled microbolometer array camera to produce hyperspectral images without the use of bulky, power-hungry motorized components, making it suitable for UAV vehicles, small mobile platforms, or in extraterrestrial environments. The sensor device can provide signal-to-noise ratios near 200 for ambient temperature scenes with 33 wavenumber resolution at a frame rate of 50 Hz, with higher results indicated by ongoing component improvements.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

PubMed

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Recent Advances in Techniques for Hyperspectral Image Processing

NASA Technical Reports Server (NTRS)

Plaza, Antonio; Benediktsson, Jon Atli; Boardman, Joseph W.; Brazile, Jason; Bruzzone, Lorenzo; Camps-Valls, Gustavo; Chanussot, Jocelyn; Fauvel, Mathieu; Gamba, Paolo; Gualtieri, Anthony;

A multisensor system for airborne surveillance of oil pollution

NASA Technical Reports Server (NTRS)

Edgerton, A. T.; Ketchal, R.; Catoe, C.

1973-01-01

The U.S. Coast Guard is developing a prototype airborne oil surveillance system for use in its Marine Environmental Protection Program. The prototype system utilizes an X-band side-looking radar, a 37-GHz imaging microwave radiometer, a multichannel line scanner, and a multispectral low light level system. The system is geared to detecting and mapping oil spills and potential pollution violators anywhere within a 25 nmi range of the aircraft flight track under all but extreme weather conditions. The system provides for false target discrimination and maximum identification of spilled materials. The system also provides an automated detection alarm, as well as a color display to achieve maximum coupling between the sensor data and the equipment operator.

Complete-arch accuracy of intraoral scanners.

PubMed

Treesh, Joshua C; Liacouras, Peter C; Taft, Robert M; Brooks, Daniel I; Raiciulescu, Sorana; Ellert, Daniel O; Grant, Gerald T; Ye, Ling

2018-04-30

Intraoral scanners have shown varied results in complete-arch applications. The purpose of this in vitro study was to evaluate the complete-arch accuracy of 4 intraoral scanners based on trueness and precision measurements compared with a known reference (trueness) and with each other (precision). Four intraoral scanners were evaluated: CEREC Bluecam, CEREC Omnicam, TRIOS Color, and Carestream CS 3500. A complete-arch reference cast was created and printed using a 3-dimensional dental cast printer with photopolymer resin. The reference cast was digitized using a laboratory-based white light 3-dimensional scanner. The printed reference cast was scanned 10 times with each intraoral scanner. The digital standard tessellation language (STL) files from each scanner were then registered to the reference file and compared with differences in trueness and precision using a 3-dimensional modeling software. Additionally, scanning time was recorded for each scan performed. The Wilcoxon signed rank, Kruskal-Wallis, and Dunn tests were used to detect differences for trueness, precision, and scanning time (α=.05). Carestream CS 3500 had the lowest overall trueness and precision compared with Bluecam and TRIOS Color. The fourth scanner, Omnicam, had intermediate trueness and precision. All of the scanners tended to underestimate the size of the reference file, with exception of the Carestream CS 3500, which was more variable. Based on visual inspection of the color rendering of signed differences, the greatest amount of error tended to be in the posterior aspects of the arch, with local errors exceeding 100 μm for all scans. The single capture scanner Carestream CS 3500 had the overall longest scan times and was significantly slower than the continuous capture scanners TRIOS Color and Omnicam. Significant differences in both trueness and precision were found among the scanners. Scan times of the continuous capture scanners were faster than the single capture scanners

Hyperspectral Observations of Land Surfaces Using Ground-based, Airborne, and Satellite Sensors

NASA Astrophysics Data System (ADS)

Knuteson, R. O.; Best, F. A.; Revercomb, H. E.; Tobin, D. C.

2006-12-01

The University of Wisconsin-Madison Space Science and Engineering Center (UW-SSEC) has helped pioneer the use of high spectral resolution infrared spectrometers for application to atmospheric and surface remote sensing. This paper is focused on observations of land surface infrared emission from high spectral resolution measurements collected over the past 15 years using airborne, ground-based, and satellite platforms. The earliest data was collected by the High-resolution Interferometer Sounder (HIS), an instrument designed in the 1980s for operation on the NASA ER-2 high altitude aircraft. The HIS was replaced in the late 1990s by the Scanning-HIS instrument which has flown on the NASA ER-2, WB-57, DC-8, and Scaled Composites Proteus aircraft and continues to support field campaigns, such as those for EOS Terra, Aqua, and Aura validation. Since 1995 the UW-SSEC has fielded a ground-based Atmospheric Emitted Radiance Interferometer (AERI) in a research vehicle (the AERIBAGO) which has allowed for direct field measurements of land surface emission from a height of about 16 ft above the ground. Several ground-based and aircraft campaigns were conducted to survey the region surrounding the ARM Southern Great Plains site in north central Oklahoma. The ground- based AERIBAGO has also participated in surface emissivity campaigns in the Western U.S.. Since 2002, the NASA Atmospheric InfraRed Sounder (AIRS) has provided similar measurements from the Aqua platform in an afternoon sun-synchronous polar orbit. Ground-based and airborne observations are being used to validate the land surface products derived from the AIRS observations. These cal/val activities are in preparation for similar measurements anticipated from the operational Cross-track InfraRed Sounder (CrIS) on the NPOESS Preparatory Platform (NPP), expected to be launched in 2008. Moreover, high spectral infrared observations will soon be made by the Infrared Atmospheric Sounder Interferometer (IASI) on the

Novel hyperspectral prediction method and apparatus

NASA Astrophysics Data System (ADS)

Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

2009-05-01

Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Airborne Remote Earth Sensing (ARES) Program: an operational airborne MWIR imaging spectrometer and applications

NASA Astrophysics Data System (ADS)

Bishop, Kevin D.; Diestel, Michael J.

1996-11-01

Since 1993, the Airborne Remote Earth Sensing (ARES) Program has collected a wide variety of mid-wave infrared hyperspectral data on an interesting assortment of atmospheric, geologic, urban and chemical emission/absorption features. Flown in NASA's high altitude WB-57F aircraft, the ARES sensor is a 75 channel cryo-cooled prism spectrometer covering the 2 - 6 micrometers spectral region, and is capable of up or down-looking measurements over a wide range of collection geometries. Sensor characteristics, pointing capabilities, and overall performance are discussed. Highlights from some of the recent data collections, such as the 1993 and 95 thermal mapping of the active lava flow areas from the Kilauea volcano; the 1993 collection of the direct solar specular reflection off high altitude (ice) cloud layers over West Texas; upper atmospheric H2O vapor sounding using the 6 micrometers solar absorption spectra; Sulfur Dioxide detection from a coal burning power plant in Page, AZ (SO2 in emission) and from the Pu'u O'o vent of the Kilauea volcano (SO2 in absorption); and MWIR imagery from various terrestrial and urban background scenes, including West Los Angeles, and the Capitol area of Washington, D.C. Supporting spectral analysis and radiometric modeling are presented.

Snapshot hyperspectral retinal imaging using compact spectral resolving detector array.

PubMed

Li, Hao; Liu, Wenzhong; Dong, Biqin; Kaluzny, Joel V; Fawzi, Amani A; Zhang, Hao F

2017-06-01

Hyperspectral retinal imaging captures the light spectrum from each imaging pixel. It provides spectrally encoded retinal physiological and morphological information, which could potentially benefit diagnosis and therapeutic monitoring of retinal diseases. The key challenges in hyperspectral retinal imaging are how to achieve snapshot imaging to avoid motions between the images from multiple spectral bands, and how to design a compact snapshot imager suitable for clinical use. Here, we developed a compact, snapshot hyperspectral fundus camera for rodents using a novel spectral resolving detector array (SRDA), on which a thin-film Fabry-Perot cavity filter was monolithically fabricated on each imaging pixel. We achieved hyperspectral retinal imaging with 16 wavelength bands (460 to 630 nm) at 20 fps. We also demonstrated false-color vessel contrast enhancement and retinal oxygen saturation (sO 2 ) measurement through spectral analysis. This work could potentially bring hyperspectral retinal imaging from bench to bedside. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

3D Spatial and Spectral Fusion of Terrestrial Hyperspectral Imagery and Lidar for Hyperspectral Image Shadow Restoration Applied to a Geologic Outcrop

NASA Astrophysics Data System (ADS)

Hartzell, P. J.; Glennie, C. L.; Hauser, D. L.; Okyay, U.; Khan, S.; Finnegan, D. C.

2016-12-01

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from an exclusively airborne technique to terrestrial modalities. This enables high resolution 3D spatial and spectral quantification of vertical geologic structures for applications such as virtual 3D rock outcrop models for hydrocarbon reservoir analog analysis and mineral quantification in open pit mining environments. In contrast to airborne observation geometry, the vertical surfaces observed by horizontal-viewing terrestrial HSI sensors are prone to extensive topography-induced solar shadowing, which leads to reduced pixel classification accuracy or outright removal of shadowed pixels from analysis tasks. Using a precisely calibrated and registered offset cylindrical linear array camera model, we demonstrate the use of 3D lidar data for sub-pixel HSI shadow detection and the restoration of the shadowed pixel spectra via empirical methods that utilize illuminated and shadowed pixels of similar material composition. We further introduce a new HSI shadow restoration technique that leverages collocated backscattered lidar intensity, which is resistant to solar conditions, obtained by projecting the 3D lidar points through the HSI camera model into HSI pixel space. Using ratios derived from the overlapping lidar laser and HSI wavelengths, restored shadow pixel spectra are approximated using a simple scale factor. Simulations of multiple lidar wavelengths, i.e., multi-spectral lidar, indicate the potential for robust HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance is quantified through HSI pixel classification consistency between full sun and partial sun exposures of a single geologic outcrop.

A Simple X-Y Scanner.

ERIC Educational Resources Information Center

Halse, M. R.; Hudson, W. J.

1986-01-01

Describes an X-Y scanner used to create acoustic holograms. Scanner is computer controlled and can be adapted to digitize pictures. Scanner geometry is discussed. An appendix gives equipment details. The control program in ATOM BASIC and 6502 machine code is available from the authors. (JM)

New Application of Hyperspectral Imaging for Bacterial Cell Classification

USDA-ARS?s Scientific Manuscript database

Hyperspectral microscopy has shown potential as a method for rapid detection of foodborne pathogenic bacteria with spectral characteristics from bacterial cells. Hyperspectral microscope images (HMIs) are collected from broiler chicken isolates of Salmonella serotypes Enteritidis, Typhimurium, Infa...

Post-processing for improving hyperspectral anomaly detection accuracy

NASA Astrophysics Data System (ADS)

Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

2015-10-01

Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

NASA Astrophysics Data System (ADS)

Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

2014-03-01

This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.

[Application of hyper-spectral remote sensing technology in environmental protection].

PubMed

Zhao, Shao-Hua; Zhang, Feng; Wang, Qiao; Yao, Yun-Jun; Wang, Zhong-Ting; You, Dai-An

2013-12-01

Hyper-spectral remote sensing (RS) technology has been widely used in environmental protection. The present work introduces its recent application in the RS monitoring of pollution gas, green-house gas, algal bloom, water quality of catch water environment, safety of drinking water sources, biodiversity, vegetation classification, soil pollution, and so on. Finally, issues such as scarce hyper-spectral satellites, the limits of data processing and information extract are related. Some proposals are also presented, including developing subsequent satellites of HJ-1 satellite with differential optical absorption spectroscopy, greenhouse gas spectroscopy and hyper-spectral imager, strengthening the study of hyper-spectral data processing and information extraction, and promoting the construction of environmental application system.

Technology Development for a Hyperspectral Microwave Atmospheric Sounder (HyMAS)

NASA Technical Reports Server (NTRS)

Blackwell, W.; Galbraith, C.; Hilliard, L.; Racette, P.; Thompson, E.

2014-01-01

The Hyperspectral Microwave Atmospheric Sounder (HyMAS) is being developed at Lincoln Laboratories and accommodated by the Goddard Space Flight Center for a flight opportunity on a NASA research aircraft. The term hyperspectral microwave is used to indicate an all-weather sounding instrument that performs equivalent to hyperspectral infrared sounders in clear air with vertical resolution of approximately 1 km. Deploying the HyMAS equipped scanhead with the existing Conical Scanning Microwave Imaging Radiometer (CoSMIR) shortens the path to a flight demonstration. Hyperspectral microwave is achieved through the use of independent RF antennas that sample the volume of the Earths atmosphere through various levels of frequencies, thereby producing a set of dense, spaced vertical weighting functions.

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Geometrical calibration of an AOTF hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Špiclin, Žiga; Katrašnik, Jaka; Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2010-02-01

Optical aberrations present an important problem in optical measurements. Geometrical calibration of an imaging system is therefore of the utmost importance for achieving accurate optical measurements. In hyper-spectral imaging systems, the problem of optical aberrations is even more pronounced because optical aberrations are wavelength dependent. Geometrical calibration must therefore be performed over the entire spectral range of the hyper-spectral imaging system, which is usually far greater than that of the visible light spectrum. This problem is especially adverse in AOTF (Acousto- Optic Tunable Filter) hyper-spectral imaging systems, as the diffraction of light in AOTF filters is dependent on both wavelength and angle of incidence. Geometrical calibration of hyper-spectral imaging system was performed by stable caliber of known dimensions, which was imaged at different wavelengths over the entire spectral range. The acquired images were then automatically registered to the caliber model by both parametric and nonparametric transformation based on B-splines and by minimizing normalized correlation coefficient. The calibration method was tested on an AOTF hyper-spectral imaging system in the near infrared spectral range. The results indicated substantial wavelength dependent optical aberration that is especially pronounced in the spectral range closer to the infrared part of the spectrum. The calibration method was able to accurately characterize the aberrations and produce transformations for efficient sub-pixel geometrical calibration over the entire spectral range, finally yielding better spatial resolution of hyperspectral imaging system.

Assessing the Ability of Vegetation Indices to Identify Shallow Subsurface Water Flow Pathways from Hyperspectral Imagery Using Machine Learning: Methodology

NASA Astrophysics Data System (ADS)

Byers, J. M.; Doctor, K.

2017-12-01

A common application of the satellite and airborne acquired hyperspectral imagery in the visible and NIR spectrum is the assessment of vegetation. Various absorption features of plants related to both water and chlorophyll content can be used to measure the vigor and access to underlying water sources of the vegetation. The typical strategy is to form hand-crafted features from the hyperspectral data cube by selecting two wavelengths to form difference or ratio images in the pixel space. The new image attempts to provide greater contrast for some feature of the vegetation. The Normalized Difference Vegetation Index (NDVI) is a widely used example formed from the ratio of differences and sums at two different wavelengths. There are dozens of these indices that are ostensibly formed using insights about the underlying physics of the spectral absorption with claims to efficacy in representing various properties of vegetation. In the language of machine learning these vegetation indices are features that can be used as a useful data representation within an algorithm. In this work we use a powerful approach from machine learning, probabilistic graphical models (PGM), to balance the competing needs of using existing hydrological classifications of terrain while finding statistically reliable features within hyperspectral data for identifying the generative process of the data. The algorithm in its simplest form is called a Naïve Bayes (NB) classifier and can be constructed in a data-driven estimation procedure of the conditional probability distributions that form the PGM. The Naïve Bayes model assumes that all vegetation indices (VI) are independent of one another given the hydrological class label. We seek to test its validity in a pilot study of detecting subsurface water flow pathways from VI. A more sophisticated PGM will also be explored called a tree-augmented NB that accounts for the probabilistic dependence between VI features. This methodology provides a

ICER-3D Hyperspectral Image Compression Software

NASA Technical Reports Server (NTRS)

Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh

2010-01-01

Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received

Quantum computation in the analysis of hyperspectral data

NASA Astrophysics Data System (ADS)

Gomez, Richard B.; Ghoshal, Debabrata; Jayanna, Anil

2004-08-01

Recent research on the topic of quantum computation provides us with some quantum algorithms with higher efficiency and speedup compared to their classical counterparts. In this paper, it is our intent to provide the results of our investigation of several applications of such quantum algorithms - especially the Grover's Search algorithm - in the analysis of Hyperspectral Data. We found many parallels with Grover's method in existing data processing work that make use of classical spectral matching algorithms. Our efforts also included the study of several methods dealing with hyperspectral image analysis work where classical computation methods involving large data sets could be replaced with quantum computation methods. The crux of the problem in computation involving a hyperspectral image data cube is to convert the large amount of data in high dimensional space to real information. Currently, using the classical model, different time consuming methods and steps are necessary to analyze these data including: Animation, Minimum Noise Fraction Transform, Pixel Purity Index algorithm, N-dimensional scatter plot, Identification of Endmember spectra - are such steps. If a quantum model of computation involving hyperspectral image data can be developed and formalized - it is highly likely that information retrieval from hyperspectral image data cubes would be a much easier process and the final information content would be much more meaningful and timely. In this case, dimensionality would not be a curse, but a blessing.

Airborne mapping of chemical plumes in the aftermath of Hurricanes Katrina and Rita

NASA Astrophysics Data System (ADS)

Lewis, Paul E.; Thomas, Mark J.; Kroutil, Robert T.; Combs, Roger; Cummings, Alan S.; Miller, Dave; Curry, Tim; Shen, Sylvia S.

2006-05-01

Infrared airborne spectral measurements were collected over the Gulf Coast area during the aftermath of Hurricanes Katrina and Rita. These measurements allowed surveillance for potentially hazardous chemical vapor releases from industrial facilities caused by storm damage. Data was collected with a mid-longwave infrared multispectral imager and a hyperspectral Fourier transform infrared spectrometer operating in a low altitude aircraft. Signal processing allowed detection and identification of targeted spectral signatures in the presence of interferents, atmospheric contributions, and thermal clutter. Results confirmed the presence of a number of chemical vapors. All detection results were immediately passed along to emergency first responders on the ground. The chemical identification, location, and vapor species concentration information were used by the emergency response ground teams for identification of critical plume releases and subsequent mitigation.

Airborne surveys in the Arctic and Antarctic for geophysics, sea-ice thickness, and CryoSat validation

NASA Astrophysics Data System (ADS)

Forsberg, R.; Olesen, A. V.; Hvidegaard, S.; Skourup, H.

2010-12-01

Airborne laser and radar measurements over the Greenland ice sheet, Svalbard, and adjacent parts of the Arctic Ocean have been carried out by DTU-Space in a number of recent Danish/Greenlandic and European project campaigns, with the purpose to monitor ice sheet and sea-ice changes, support of Greenland societal needs (oil exploration and hydropower), and support of CryoSat pre-launch calibration and validation campaigns. The Arctic campaigns have been done using a Twin-Otter aircraft, carrying laser scanners and various radars. Since 2009 a new program of long-range gravity and magnetic surveys have been initiated using a Basler DC3 aircraft for large-scale surveys in the Arctic Ocean and Antarctica, with the 2010 cooperative Danish-Argentinean-Chilean-US ICEGRAV survey of the Antarctic Peninsula additionally including a UTIG 60 MHz ice-penetrating radar. In the paper we outline the recent and upcoming airborne survey activities, outline the usefulness of the airborne data for satellite validation (CryoSat and GOCE), and give examples of measurements and comparisons to satellite and in-situ data.

Analysis of multispectral and hyperspectral longwave infrared (LWIR) data for geologic mapping

NASA Astrophysics Data System (ADS)

Kruse, Fred A.; McDowell, Meryl

2015-05-01

Multispectral MODIS/ASTER Airborne Simulator (MASTER) data and Hyperspectral Thermal Emission Spectrometer (HyTES) data covering the 8 - 12 μm spectral range (longwave infrared or LWIR) were analyzed for an area near Mountain Pass, California. Decorrelation stretched images were initially used to highlight spectral differences between geologic materials. Both datasets were atmospherically corrected using the ISAC method, and the Normalized Emissivity approach was used to separate temperature and emissivity. The MASTER data had 10 LWIR spectral bands and approximately 35-meter spatial resolution and covered a larger area than the HyTES data, which were collected with 256 narrow (approximately 17nm-wide) spectral bands at approximately 2.3-meter spatial resolution. Spectra for key spatially-coherent, spectrally-determined geologic units for overlap areas were overlain and visually compared to determine similarities and differences. Endmember spectra were extracted from both datasets using n-dimensional scatterplotting and compared to emissivity spectral libraries for identification. Endmember distributions and abundances were then mapped using Mixture-Tuned Matched Filtering (MTMF), a partial unmixing approach. Multispectral results demonstrate separation of silica-rich vs non-silicate materials, with distinct mapping of carbonate areas and general correspondence to the regional geology. Hyperspectral results illustrate refined mapping of silicates with distinction between similar units based on the position, character, and shape of high resolution emission minima near 9 μm. Calcite and dolomite were separated, identified, and mapped using HyTES based on a shift of the main carbonate emissivity minimum from approximately 11.3 to 11.2 μm respectively. Both datasets demonstrate the utility of LWIR spectral remote sensing for geologic mapping.

Onboard Classification of Hyperspectral Data on the Earth Observing One Mission

NASA Technical Reports Server (NTRS)

Chien, Steve; Tran, Daniel; Schaffer, Steve; Rabideau, Gregg; Davies, Ashley Gerard; Doggett, Thomas; Greeley, Ronald; Ip, Felipe; Baker, Victor; Doubleday, Joshua;

Advanced pushbroom hyperspectral LWIR imagers

NASA Astrophysics Data System (ADS)

Holma, Hannu; Hyvärinen, Timo; Lehtomaa, Jarmo; Karjalainen, Harri; Jaskari, Risto

2009-05-01

Performance studies and instrument designs for hyperspectral pushbroom imagers in thermal wavelength region are introduced. The studies involve imaging systems based on both MCT and microbolometer detector. All the systems employ pushbroom imaging spectrograph with transmission grating and on-axis optics. The aim of the work was to design high performance instruments with good image quality and compact size for various application requirements. A big challenge in realizing these goals without considerable cooling of the whole instrument is to control the instrument radiation from all the surfaces of the instrument itself. This challenge is even bigger in hyperspectral instruments, where the optical power from the target is spread spectrally over tens of pixels, but the instrument radiation is not dispersed. Without any suppression, the instrument radiation can overwhelm the radiation from the target by 1000 times. In the first imager design, BMC-technique (background monitoring on-chip), background suppression and temperature stabilization have been combined with cryo-cooled MCT-detector. The performance of a very compact hyperspectral imager with 84 spectral bands and 384 spatial samples has been studied and NESR of 18 mW/(m2srμm) at 10 μm wavelength for 300 K target has been achieved. This leads to SNR of 580. These results are based on a simulation model. The second version of the imager with an uncooled microbolometer detector and optics in ambient temperature aims at imaging targets at higher temperatures or with illumination. Heater rods with ellipsoidal reflectors can be used to illuminate the swath line of the hyperspectral imager on a target or sample, like drill core in mineralogical analysis. Performance characteristics for microbolometer version have been experimentally verified.

Coastal Seabed Mapping with Hyperspectral and Lidar data

NASA Astrophysics Data System (ADS)

Taramelli, A.; Valentini, E.; Filipponi, F.; Cappucci, S.

2017-12-01

A synoptic view of the coastal seascape and its dynamics needs a quantitative ability to dissect different components over the complexity of the seafloor where a mixture of geo - biological facies determines geomorphological features and their coverage. The present study uses an analytical approach that takes advantage of a multidimensional model to integrate different data sources from airborne Hyperspectral and LiDAR remote sensing and in situ measurements to detect antropogenic features and ecological `tipping points' in coastal seafloors. The proposed approach has the ability to generate coastal seabed maps using: 1) a multidimensional dataset to account for radiometric and morphological properties of waters and the seafloor; 2) a field spectral library to assimilate the high environmental variability into the multidimensional model; 3) a final classification scheme to represent the spatial gradients in the seafloor. The spatial pattern of the response to anthropogenic forcing may be indistinguishable from patterns of natural variability. It is argued that this novel approach to define tipping points following anthropogenic impacts could be most valuable in the management of natural resources and the economic development of coastal areas worldwide. Examples are reported from different sites of the Mediterranean Sea, both from Marine Protected and un-Protected Areas.

Medical hyperspectral imaging: a review

PubMed Central

Lu, Guolan; Fei, Baowei

2014-01-01

Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941

Detection of cracks on tomatoes using hyperspectral near-infrared reflectance imaging system

USDA-ARS?s Scientific Manuscript database

The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detection of cuticle cracks on tomatoes. A hyperspectral near-infrared reflectance imaging system in the region of 1000-1700 nm was used to obtain hyperspectral reflectance ima...

Absorption Spectrum of Phytoplankton Pigments Derived from Hyperspectral Remote-Sensing Reflectance

DTIC Science & Technology

2004-01-01

For a data set collected around Baja California with chlorophyll-a concentration ((chl-a)) ranging from 0.16 to 11.3 mg/cubic meter, hyperspectral absorption spectra of phytoplankton pigments were independently inverted from hyperspectral remote - sensing reflectance using a newly...potential of using hyperspectral remote sensing to retrieve both chlorophyll-a and other accessory pigments. (7 figures, 47 refs.)

[Lithology feature extraction of CASI hyperspectral data based on fractal signal algorithm].

PubMed

Tang, Chao; Chen, Jian-Ping; Cui, Jing; Wen, Bo-Tao

2014-05-01

Hyperspectral data is characterized by combination of image and spectrum and large data volume dimension reduction is the main research direction. Band selection and feature extraction is the primary method used for this objective. In the present article, the authors tested methods applied for the lithology feature extraction from hyperspectral data. Based on the self-similarity of hyperspectral data, the authors explored the application of fractal algorithm to lithology feature extraction from CASI hyperspectral data. The "carpet method" was corrected and then applied to calculate the fractal value of every pixel in the hyperspectral data. The results show that fractal information highlights the exposed bedrock lithology better than the original hyperspectral data The fractal signal and characterized scale are influenced by the spectral curve shape, the initial scale selection and iteration step. At present, research on the fractal signal of spectral curve is rare, implying the necessity of further quantitative analysis and investigation of its physical implications.

Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ochilov, S.; Alam, M. S.; Bal, A.

2006-05-01

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

Dental caries imaging using hyperspectral stimulated Raman scattering microscopy

NASA Astrophysics Data System (ADS)

Wang, Zi; Zheng, Wei; Jian, Lin; Huang, Zhiwei

2016-03-01

We report the development of a polarization-resolved hyperspectral stimulated Raman scattering (SRS) imaging technique based on a picosecond (ps) laser-pumped optical parametric oscillator system for label-free imaging of dental caries. In our imaging system, hyperspectral SRS images (512×512 pixels) in both fingerprint region (800-1800 cm-1) and high-wavenumber region (2800-3600 cm-1) are acquired in minutes by scanning the wavelength of OPO output, which is a thousand times faster than conventional confocal micro Raman imaging. SRS spectra variations from normal enamel to caries obtained from the hyperspectral SRS images show the loss of phosphate and carbonate in the carious region. While polarization-resolved SRS images at 959 cm-1 demonstrate that the caries has higher depolarization ratio. Our results demonstrate that the polarization resolved-hyperspectral SRS imaging technique developed allows for rapid identification of the biochemical and structural changes of dental caries.

Hyperspectral signature analysis of skin parameters

NASA Astrophysics Data System (ADS)

Vyas, Saurabh; Banerjee, Amit; Garza, Luis; Kang, Sewon; Burlina, Philippe

2013-02-01

The temporal analysis of changes in biological skin parameters, including melanosome concentration, collagen concentration and blood oxygenation, may serve as a valuable tool in diagnosing the progression of malignant skin cancers and in understanding the pathophysiology of cancerous tumors. Quantitative knowledge of these parameters can also be useful in applications such as wound assessment, and point-of-care diagnostics, amongst others. We propose an approach to estimate in vivo skin parameters using a forward computational model based on Kubelka-Munk theory and the Fresnel Equations. We use this model to map the skin parameters to their corresponding hyperspectral signature. We then use machine learning based regression to develop an inverse map from hyperspectral signatures to skin parameters. In particular, we employ support vector machine based regression to estimate the in vivo skin parameters given their corresponding hyperspectral signature. We build on our work from SPIE 2012, and validate our methodology on an in vivo dataset. This dataset consists of 241 signatures collected from in vivo hyperspectral imaging of patients of both genders and Caucasian, Asian and African American ethnicities. In addition, we also extend our methodology past the visible region and through the short-wave infrared region of the electromagnetic spectrum. We find promising results when comparing the estimated skin parameters to the ground truth, demonstrating good agreement with well-established physiological precepts. This methodology can have potential use in non-invasive skin anomaly detection and for developing minimally invasive pre-screening tools.

Spectral Reconstruction for Obtaining Virtual Hyperspectral Images

NASA Astrophysics Data System (ADS)

Perez, G. J. P.; Castro, E. C.

2016-12-01

Hyperspectral sensors demonstrated its capabalities in identifying materials and detecting processes in a satellite scene. However, availability of hyperspectral images are limited due to the high development cost of these sensors. Currently, most of the readily available data are from multi-spectral instruments. Spectral reconstruction is an alternative method to address the need for hyperspectral information. The spectral reconstruction technique has been shown to provide a quick and accurate detection of defects in an integrated circuit, recovers damaged parts of frescoes, and it also aids in converting a microscope into an imaging spectrometer. By using several spectral bands together with a spectral library, a spectrum acquired by a sensor can be expressed as a linear superposition of elementary signals. In this study, spectral reconstruction is used to estimate the spectra of different surfaces imaged by Landsat 8. Four atmospherically corrected surface reflectance from three visible bands (499 nm, 585 nm, 670 nm) and one near-infrared band (872 nm) of Landsat 8, and a spectral library of ground elements acquired from the United States Geological Survey (USGS) are used. The spectral library is limited to 420-1020 nm spectral range, and is interpolated at one nanometer resolution. Singular Value Decomposition (SVD) is used to calculate the basis spectra, which are then applied to reconstruct the spectrum. The spectral reconstruction is applied for test cases within the library consisting of vegetation communities. This technique was successful in reconstructing a hyperspectral signal with error of less than 12% for most of the test cases. Hence, this study demonstrated the potential of simulating information at any desired wavelength, creating a virtual hyperspectral sensor without the need for additional satellite bands.

The potential of high resolution airborne laser scanning for deriving geometric properties of single trees

NASA Astrophysics Data System (ADS)

Morsdorf, F.; Meier, E.; Koetz, B.; Nüesch, D.; Itten, K.; Allgöwer, B.

2003-04-01

The potential of airborne laserscanning for mapping forest stands has been intensively evaluated in the past few years. Algorithms deriving structural forest parameters in a stand-wise manner from laser data have been successfully implemented by a number of researchers. However, with very high point density laser (>20 points/m^2) data we pursue the approach of deriving these parameters on a single-tree basis. We explore the potential of delineating single trees from laser scanner raw data (x,y,z- triples) and validate this approach with a dataset of more than 2000 georeferenced trees, including tree height and crown diameter, gathered on a long term forest monitoring site by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). The accuracy of the laser scanner is evaluated trough 6 reference targets, being 3x3 m^2 in size and horizontally plain, for validating both the horizontal and vertical accuracy of the laser scanner by matching of triangular irregular networks (TINs). Single trees are segmented by a clustering analysis in all three coordinate dimensions and their geometric properties can then be derived directly from the tree cluster.

Two-dimensional airborne ultrasound real-time linear array scanner--applied to screening for scoliosis.

PubMed

Mauritzson, L; Ilver, J; Benoni, G; Lindström, K; Willner, S

1991-01-01

Diagnostic ultrasound is an established, noninvasive and harmless method for imaging the shape and appearance of organs and other tissues inside the body, and it has been used in many clinical applications for more than three decades. We have now applied some of this well-known technique together with the use of airborne ultrasound in medical applications, to build an equipment for anthropometrical investigation outside the body, e.g., measuring and registration of the shape and form of the human back. This is mostly done for screening purposes of young people in an attempt to find patients developing scoliosis, and in order to circumvent some of the disadvantages with the traditional screening method in this field of medical application.

In vivo and in vitro hyperspectral imaging of cervical neoplasia

NASA Astrophysics Data System (ADS)

Wang, Chaojian; Zheng, Wenli; Bu, Yanggao; Chang, Shufang; Tong, Qingping; Zhang, Shiwu; Xu, Ronald X.

2014-02-01

Cervical cancer is a prevalent disease in many developing countries. Colposcopy is the most common approach for screening cervical intraepithelial neoplasia (CIN). However, its clinical efficacy heavily relies on the examiner's experience. Spectroscopy is a potentially effective method for noninvasive diagnosis of cervical neoplasia. In this paper, we introduce a hyperspectral imaging technique for noninvasive detection and quantitative analysis of cervical neoplasia. A hyperspectral camera is used to collect the reflectance images of the entire cervix under xenon lamp illumination, followed by standard colposcopy examination and cervical tissue biopsy at both normal and abnormal sites in different quadrants. The collected reflectance data are calibrated and the hyperspectral signals are extracted. Further spectral analysis and image processing works are carried out to classify tissue into different types based on the spectral characteristics at different stages of cervical intraepithelial neoplasia. The hyperspectral camera is also coupled with a lab microscope to acquire the hyperspectral transmittance images of the pathological slides. The in vivo and the in vitro imaging results are compared with clinical findings to assess the accuracy and efficacy of the method.

Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety

PubMed Central

Huang, Hui; Liu, Li; Ngadi, Michael O.

2014-01-01

Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed. PMID:24759119

Remote Sensing of Wind Fields and Aerosol Distribution with Airborne Scanning Doppler Lidar

NASA Technical Reports Server (NTRS)

Rothermel, Jeffry; Cutten, Dean R.; Johnson, Steven C.; Jazembski, Maurice; Arnold, James E. (Technical Monitor)

2001-01-01

The coherent Doppler laser radar (lidar), when operated from an airborne platform, is a unique tool for the study of atmospheric and surface processes and features. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are typically at a disadvantage. The atmospheric lidar remote sensing groups of several US institutions, led by Marshall Space Flight Center, have developed an airborne coherent Doppler lidar capable of mapping the wind field and aerosol structure in three dimensions. The instrument consists of an eye-safe approx. 1 Joule/pulse lidar transceiver, telescope, scanner, inertial measurement unit, and flight computer system to orchestrate all subsystem functions and tasks. The scanner is capable of directing the expanded lidar beam in a variety of ways, in order to extract vertically-resolved wind fields. Horizontal resolution is approx. 1 km; vertical resolution is even finer. Winds are obtained by measuring backscattered, Doppler-shifted laser radiation from naturally-occurring aerosol particles (of order 1 micron diameter). Measurement coverage depends on aerosol spatial distribution and composition. Velocity accuracy has been verified to be approx. 1 meter per second. A variety of applications have been demonstrated during the three flight campaigns conducted during 1995-1998. Examples will be shown during the presentation. In 1995, boundary layer winds over the ocean were mapped with unprecedented resolution. In 1996, unique measurements were made of. flow over the complex terrain of the Aleutian Islands; interaction of the marine boundary layer jet with the California coastal mountain range; a weak dry line in Texas - New Mexico; the angular dependence of sea surface scattering; and in-flight radiometric calibration using the surface of White Sands National Monument. In 1998, the first measurements of eyewall and boundary layer winds within a

Imaging of blood cells based on snapshot Hyper-Spectral Imaging systems

NASA Astrophysics Data System (ADS)

Robison, Christopher J.; Kolanko, Christopher; Bourlai, Thirimachos; Dawson, Jeremy M.

2015-05-01

Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering coregistered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera; attached to a microscope at varying objective powers and illumination intensity. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyper-spectral data cube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting.

Hyperspectral remote sensing image retrieval system using spectral and texture features.

PubMed

Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

2017-06-01

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

Airborne observed solar elevation and row direction effects on the near-IR/red ratio of cotton

NASA Technical Reports Server (NTRS)

Millard, J. P.; Jackson, R. D.; Goettelman, R. C.; Leroy, M. J. (Principal Investigator)

1981-01-01

An airborne multispectral scanner was used to obtain data over two adjacent cotton fields having rows perpendicular to one another, at three times of day (different solar elevations), and on two dates (different plant size). The near IR/red ratios were displayed in image form, so that within-field variations and differences between fields could be easily assessed. The ratio varied with changing Sun elevation for north-south oriented rows, but no variation was detected for east-west oriented rows.

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

PubMed

Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung

2018-02-01

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

Chromotomosynthesis for high speed hyperspectral imagery

NASA Astrophysics Data System (ADS)

Bostick, Randall L.; Perram, Glen P.

2012-09-01

A rotating direct vision prism, chromotomosynthetic imaging (CTI) system operating in the visible creates hyperspectral imagery by collecting a set of 2D images with each spectrally projected at a different rotation angle of the prism. Mathematical reconstruction techniques that have been well tested in the field of medical physics are used to reconstruct the data to produce the 3D hyperspectral image. The instrument operates with a 100 mm focusing lens in the spectral range of 400-900 nm with a field of view of 71.6 mrad and angular resolution of 0.8-1.6 μrad. The spectral resolution is 0.6 nm at the shortest wavelengths, degrading to over 10 nm at the longest wavelengths. Measurements using a pointlike target show that performance is limited by chromatic aberration. The accuracy and utility of the instrument is assessed by comparing the CTI results to spatial data collected by a wideband image and hyperspectral data collected using a liquid crystal tunable filter (LCTF). The wide-band spatial content of the scene reconstructed from the CTI data is of same or better quality as a single frame collected by the undispersed imaging system with projections taken at every 1°. Performance is dependent on the number of projections used, with projections at 5° producing adequate results in terms of target characterization. The data collected by the CTI system can provide spatial information of equal quality as a comparable imaging system, provide high-frame rate slitless 1-D spectra, and generate 3-D hyperspectral imagery which can be exploited to provide the same results as a traditional multi-band spectral imaging system. While this prototype does not operate at high speeds, components exist which will allow for CTI systems to generate hyperspectral video imagery at rates greater than 100 Hz. The instrument has considerable potential for characterizing bomb detonations, muzzle flashes, and other battlefield combustion events.

Practical issues of hyperspectral imaging analysis of solid dosage forms.

PubMed

Amigo, José Manuel

2010-09-01

Hyperspectral imaging techniques have widely demonstrated their usefulness in different areas of interest in pharmaceutical research during the last decade. In particular, middle infrared, near infrared, and Raman methods have gained special relevance. This rapid increase has been promoted by the capability of hyperspectral techniques to provide robust and reliable chemical and spatial information on the distribution of components in pharmaceutical solid dosage forms. Furthermore, the valuable combination of hyperspectral imaging devices with adequate data processing techniques offers the perfect landscape for developing new methods for scanning and analyzing surfaces. Nevertheless, the instrumentation and subsequent data analysis are not exempt from issues that must be thoughtfully considered. This paper describes and discusses the main advantages and drawbacks of the measurements and data analysis of hyperspectral imaging techniques in the development of solid dosage forms.

Hyperspectral stimulated emission depletion microscopy and methods of use thereof

DOEpatents

Timlin, Jerilyn A; Aaron, Jesse S

2014-04-01

A hyperspectral stimulated emission depletion ("STED") microscope system for high-resolution imaging of samples labeled with multiple fluorophores (e.g., two to ten fluorophores). The hyperspectral STED microscope includes a light source, optical systems configured for generating an excitation light beam and a depletion light beam, optical systems configured for focusing the excitation and depletion light beams on a sample, and systems for collecting and processing data generated by interaction of the excitation and depletion light beams with the sample. Hyperspectral STED data may be analyzed using multivariate curve resolution analysis techniques to deconvolute emission from the multiple fluorophores. The hyperspectral STED microscope described herein can be used for multi-color, subdiffraction imaging of samples (e.g., materials and biological materials) and for analyzing a tissue by Forster Resonance Energy Transfer ("FRET").

Mapping forest canopy fuels in Yellowstone National Park using lidar and hyperspectral data

NASA Astrophysics Data System (ADS)

Halligan, Kerry Quinn

The severity and size of wildland fires in the forested western U.S have increased in recent years despite improvements in fire suppression efficiency. This, along with increased density of homes in the wildland-urban interface, has resulted in high costs for fire management and increased risks to human health, safety and property. Crown fires, in comparison to surface fires, pose an especially high risk due to their intensity and high rate of spread. Crown fire models require a range of quantitative fuel parameters which can be difficult and costly to obtain, but advances in lidar and hyperspectral sensor technologies hold promise for delivering these inputs. Further research is needed, however, to assess the strengths and limitations of these technologies and the most appropriate analysis methodologies for estimating crown fuel parameters from these data. This dissertation focuses on retrieving critical crown fuel parameters, including canopy height, canopy bulk density and proportion of dead canopy fuel, from airborne lidar and hyperspectral data. Remote sensing data were used in conjunction with detailed field data on forest parameters and surface reflectance measurements. A new method was developed for retrieving Digital Surface Model (DSM) and Digital Canopy Models (DCM) from first return lidar data. Validation data on individual tree heights demonstrated the high accuracy (r2 0.95) of the DCMs developed via this new algorithm. Lidar-derived DCMs were used to estimate critical crown fire parameters including available canopy fuel, canopy height and canopy bulk density with linear regression model r2 values ranging from 0.75 to 0.85. Hyperspectral data were used in conjunction with Spectral Mixture Analysis (SMA) to assess fuel quality in the form of live versus dead canopy proportions. Severity and stage of insect-caused forest mortality were estimated using the fractional abundance of green vegetation, non-photosynthetic vegetation and shade obtained from

Classification by diagnosing all absorption features (CDAF) for the most abundant minerals in airborne hyperspectral images

NASA Astrophysics Data System (ADS)

Mobasheri, Mohammad Reza; Ghamary-Asl, Mohsen

2011-12-01

Imaging through hyperspectral technology is a powerful tool that can be used to spectrally identify and spatially map materials based on their specific absorption characteristics in electromagnetic spectrum. A robust method called Tetracorder has shown its effectiveness at material identification and mapping, using a set of algorithms within an expert system decision-making framework. In this study, using some stages of Tetracorder, a technique called classification by diagnosing all absorption features (CDAF) is introduced. This technique enables one to assign a class to the most abundant mineral in each pixel with high accuracy. The technique is based on the derivation of information from reflectance spectra of the image. This can be done through extraction of spectral absorption features of any minerals from their respected laboratory-measured reflectance spectra, and comparing it with those extracted from the pixels in the image. The CDAF technique has been executed on the AVIRIS image where the results show an overall accuracy of better than 96%.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

Airborne measurements of multi-wavelength aerosol optical depth and cloud-transmitted radiances in the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES)

NASA Astrophysics Data System (ADS)

Shinozuka, Y.; Johnson, R. R.; LeBlanc, S. E.; Chang, C. S.; Redemann, J.

2016-12-01

We report on our recent airborne measurements of multi-wavelength aerosol optical depth and cloud-transmitted radiances over the North Atlantic. We ran the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) in November 2015 and the 14-channel Ames Airborne Tracking Sunphotometer (AATS-14) in May and June 2016, both aboard the NASA C-130 aircraft. These sunphotometers provide measurements of overlying cirrus and aerosol optical depths of up to about 0.5 and constrain ecosystem and aerosol retrievals from the accompanying nadir-viewing remote sensing instruments. In addition, 4STAR measures hyperspectral transmitted light, which enables the retrieval of cloud optical depth, effective radius, and thermodynamic phase from below cloud. Our measurements contribute to the science objectives of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES), an interdisciplinary investigation resolving key processes controlling marine ecosystems and aerosols that are essential to our understanding of Earth system function and future change.

Airborne electromagnetic bathymetry investigations in Port Lincoln, South Australia - comparison with an equivalent floating transient electromagnetic system

NASA Astrophysics Data System (ADS)

Vrbancich, Julian

2011-09-01

Helicopter time-domain airborne electromagnetic (AEM) methodology is being investigated as a reconnaissance technique for bathymetric mapping in shallow coastal waters, especially in areas affected by water turbidity where light detection and ranging (LIDAR) and hyperspectral techniques may be limited. Previous studies in Port Lincoln, South Australia, used a floating AEM time-domain system to provide an upper limit to the expected bathymetric accuracy based on current technology for AEM systems. The survey lines traced by the towed floating system were also flown with an airborne system using the same transmitter and receiver electronic instrumentation, on two separate occasions. On the second occasion, significant improvements had been made to the instrumentation to reduce the system self-response at early times. A comparison of the interpreted water depths obtained from the airborne and floating systems is presented, showing the degradation in bathymetric accuracy obtained from the airborne data. An empirical data correction method based on modelled and observed EM responses over deep seawater (i.e. a quasi half-space response) at varying survey altitudes, combined with known seawater conductivity measured during the survey, can lead to significant improvements in interpreted water depths and serves as a useful method for checking system calibration. Another empirical data correction method based on observed and modelled EM responses in shallow water was shown to lead to similar improvements in interpreted water depths; however, this procedure is notably inferior to the quasi half-space response because more parameters need to be assumed in order to compute the modelled EM response. A comparison between the results of the two airborne surveys in Port Lincoln shows that uncorrected data obtained from the second airborne survey gives good agreement with known water depths without the need to apply any empirical corrections to the data. This result significantly

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

Excitation-scanning hyperspectral imaging microscope

PubMed Central

Favreau, Peter F.; Hernandez, Clarissa; Heaster, Tiffany; Alvarez, Diego F.; Rich, Thomas C.; Prabhat, Prashant; Leavesley, Silas J.

2014-01-01

Abstract. Hyperspectral imaging is a versatile tool that has recently been applied to a variety of biomedical applications, notably live-cell and whole-tissue signaling. Traditional hyperspectral imaging approaches filter the fluorescence emission over a broad wavelength range while exciting at a single band. However, these emission-scanning approaches have shown reduced sensitivity due to light attenuation from spectral filtering. Consequently, emission scanning has limited applicability for time-sensitive studies and photosensitive applications. In this work, we have developed an excitation-scanning hyperspectral imaging microscope that overcomes these limitations by providing high transmission with short acquisition times. This is achieved by filtering the fluorescence excitation rather than the emission. We tested the efficacy of the excitation-scanning microscope in a side-by-side comparison with emission scanning for detection of green fluorescent protein (GFP)-expressing endothelial cells in highly autofluorescent lung tissue. Excitation scanning provided higher signal-to-noise characteristics, as well as shorter acquisition times (300  ms/wavelength band with excitation scanning versus 3  s/wavelength band with emission scanning). Excitation scanning also provided higher delineation of nuclear and cell borders, and increased identification of GFP regions in highly autofluorescent tissue. These results demonstrate excitation scanning has utility in a wide range of time-dependent and photosensitive applications. PMID:24727909

Excitation-scanning hyperspectral imaging microscope.

PubMed

Favreau, Peter F; Hernandez, Clarissa; Heaster, Tiffany; Alvarez, Diego F; Rich, Thomas C; Prabhat, Prashant; Leavesley, Silas J

2014-04-01

Hyperspectral imaging is a versatile tool that has recently been applied to a variety of biomedical applications, notably live-cell and whole-tissue signaling. Traditional hyperspectral imaging approaches filter the fluorescence emission over a broad wavelength range while exciting at a single band. However, these emission-scanning approaches have shown reduced sensitivity due to light attenuation from spectral filtering. Consequently, emission scanning has limited applicability for time-sensitive studies and photosensitive applications. In this work, we have developed an excitation-scanning hyperspectral imaging microscope that overcomes these limitations by providing high transmission with short acquisition times. This is achieved by filtering the fluorescence excitation rather than the emission. We tested the efficacy of the excitation-scanning microscope in a side-by-side comparison with emission scanning for detection of green fluorescent protein (GFP)-expressing endothelial cells in highly autofluorescent lung tissue. Excitation scanning provided higher signal-to-noise characteristics, as well as shorter acquisition times (300  ms/wavelength band with excitation scanning versus 3  s/wavelength band with emission scanning). Excitation scanning also provided higher delineation of nuclear and cell borders, and increased identification of GFP regions in highly autofluorescent tissue. These results demonstrate excitation scanning has utility in a wide range of time-dependent and photosensitive applications.

Biologically-inspired data decorrelation for hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Picon, Artzai; Ghita, Ovidiu; Rodriguez-Vaamonde, Sergio; Iriondo, Pedro Ma; Whelan, Paul F.

2011-12-01

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Mineral Mapping with AVIRIS and EO-1 Hyperion

NASA Technical Reports Server (NTRS)

Kruse, Fred A.

2004-01-01

Imaging Spectrometry data or Hyperspectral Imagery (HSI) acquired using airborne systems have been used in the geologic community since the early 1980 s and represent a mature technology (Goetz et al., 1985; Kruse et al., 1999). The solar spectral range, 0.4 to 2.5 m, provides abundant information about many important Earth-surface minerals (Clark et al., 1990). In particular, the 2.0 to 2.5 m (SWIR) spectral range covers spectral features of hydroxyl-bearing minerals, sulfates, and carbonates common to many geologic units and hydrothermal alteration assemblages. Previous research has proven the ability of airborne and spaceborne hyperspectral systems to uniquely identify and map these and other minerals, even in sub-pixel abundances (Kruse and Lefkoff, 1993; Boardman and Kruse, 1994; Boardman et al., 1995; Kruse, et al., 1999). This paper describes a case history for a site in northern Death Valley, California and Nevada along with selected SNR calculations/results for other sites around the world. Various hyperspectral mineral mapping results for this site have previously been presented and published (Kruse, 1988; Kruse et al., 1993, 1999, 2001, 2002, 2003), however, this paper presents a condensed summary of key details for hyperspectral data from 2000 and 2001 and the results of accuracy assessment for satellite hyperspectral data compared to airborne hyperspectral data used as ground truth.

Spatial Mutual Information Based Hyperspectral Band Selection for Classification

PubMed Central

2015-01-01

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

Excitation-scanning hyperspectral imaging system for microscopic and endoscopic applications

NASA Astrophysics Data System (ADS)

Mayes, Sam A.; Leavesley, Silas J.; Rich, Thomas C.

2016-04-01

Current microscopic and endoscopic technologies for cancer screening utilize white-light illumination sources. Hyper-spectral imaging has been shown to improve sensitivity while retaining specificity when compared to white-light imaging in both microscopy and in vivo imaging. However, hyperspectral imaging methods have historically suffered from slow acquisition times due to the narrow bandwidth of spectral filters. Often minutes are required to gather a full image stack. We have developed a novel approach called excitation-scanning hyperspectral imaging that provides 2-3 orders of magnitude increased signal strength. This reduces acquisition times significantly, allowing for live video acquisition. Here, we describe a preliminary prototype excitation-scanning hyperspectral imaging system that can be coupled with endoscopes or microscopes for hyperspectral imaging of tissues and cells. Our system is comprised of three subsystems: illumination, transmission, and imaging. The illumination subsystem employs light-emitting diode arrays to illuminate at different wavelengths. The transmission subsystem utilizes a unique geometry of optics and a liquid light guide. Software controls allow us to interface with and control the subsystems and components. Digital and analog signals are used to coordinate wavelength intensity, cycling and camera triggering. Testing of the system shows it can cycle 16 wavelengths at as fast as 1 ms per cycle. Additionally, more than 18% of the light transmits through the system. Our setup should allow for hyperspectral imaging of tissue and cells in real time.

Analysis of hyperspectral scattering images using a moment method for apple firmness prediction

USDA-ARS?s Scientific Manuscript database

This article reports on using a moment method to extract features from the hyperspectral scattering profiles for apple fruit firmness prediction. Hyperspectral scattering images between 500 nm and 1000 nm were acquired online, using a hyperspectral scattering system, for ‘Golden Delicious’, ’Jonagol...

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

NASA Astrophysics Data System (ADS)

Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

2010-08-01

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

Biochip scanner device

DOEpatents

Perov, Alexander; Belgovskiy, Alexander I.; Mirzabekov, Andrei D.

2001-01-01

A biochip scanner device used to detect and acquire fluorescence signal data from biological microchips or biochips and method of use are provided. The biochip scanner device includes a laser for emitting a laser beam. A modulator, such as an optical chopper modulates the laser beam. A scanning head receives the modulated laser beam and a scanning mechanics coupled to the scanning head moves the scanning head relative to the biochip. An optical fiber delivers the modulated laser beam to the scanning head. The scanning head collects the fluorescence light from the biochip, launches it into the same optical fiber, which delivers the fluorescence into a photodetector, such as a photodiode. The biochip scanner device is used in a row scanning method to scan selected rows of the biochip with the laser beam size matching the size of the immobilization site.

Evaluation of the MODIS Albedo Product over a Heterogeneous Agricultural Area

NASA Technical Reports Server (NTRS)

Sobrino, Jose Antonio; Franch, B.; Oltra-Carrio, R.; Vermote, E. F.; Fedele, E.

2013-01-01

In this article, the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43) is evaluated over a heterogeneous agricultural area in the framework of the Earth Observation: Optical Data Calibration and Information Extraction (EODIX) project campaign, which was developed in Barrax (Spain) in June 2011. In this method, two models, the RossThick-LiSparse-Reciprocal (RTLSR) (which corresponds to the MODIS BRDF algorithm) and the RossThick-Maignan-LiSparse-Reciprocal (RTLSR-HS), were tested over airborne data by processing high-resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor. During the campaign, airborne images were retrieved with different view zenith angles along the principal and orthogonal planes. Comparing the results of applying the models to the airborne data with ground measurements, we obtained a root mean square error (RMSE) of 0.018 with both RTLSR and RTLSR-HS models. The evaluation of the MODIS BRDF/Albedo product (MCD43) was performed by comparing satellite images with AHS estimations. The results reported an RMSE of 0.04 with both models. Additionally, taking advantage of a homogeneous barley pixel, we compared in situ albedo data to satellite albedo data. In this case, the MODIS albedo estimation was (0.210 +/- 0.003), while the in situ measurement was (0.204 +/- 0.003). This result shows good agreement in regard to a homogeneous pixel.

A polarization sensitive hyperspectral imaging system for detection of differences in tissue properties

NASA Astrophysics Data System (ADS)

Peller, Joseph A.; Ceja, Nancy K.; Wawak, Amanda J.; Trammell, Susan R.

2018-02-01

Polarized light imaging and optical spectroscopy can be used to distinguish between healthy and diseased tissue. In this study, the design and testing of a single-pixel hyperspectral imaging system that uses differences in the polarization of light reflected from tissue to differentiate between healthy and thermally damaged tissue is discussed. Thermal lesions were created in porcine skin (n = 8) samples using an IR laser. The damaged regions were clearly visible in the polarized light hyperspectral images. Reflectance hyperspectral and white light imaging was also obtained for all tissue samples. Sizes of the thermally damaged regions as measured via polarized light hyperspectral imaging are compared to sizes of these regions as measured in the reflectance hyperspectral images and white light images. Good agreement between the sizes measured by all three imaging modalities was found. Hyperspectral polarized light imaging can differentiate between healthy and damaged tissue. Possible applications of this imaging system include determination of tumor margins during cancer surgery or pre-surgical biopsy.

Within-field and regional-scale accuracies of topsoil organic carbon content prediction from an airborne visible near-infrared hyperspectral image combined with synchronous field spectra for temperate croplands

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefevre, Josias; Chehdi, Kacem

2016-04-01

This study was carried out in the framework of the TOSCA-PLEIADES-CO of the French Space Agency and benefited data from the earlier PROSTOCK-Gessol3 project supported by the French Environment and Energy Management Agency (ADEME). It aimed at identifying the potential of airborne hyperspectral visible near-infrared AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with intensive annual crop cultivation and both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle images (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks. Tracks were atmospherically corrected then mosaicked at a 2 m-resolution using a set of 24 synchronous field spectra of bare soils, black and white targets and impervious surfaces. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then calculation and thresholding of NDVI from an atmospherically corrected SPOT4 image acquired the same day enabled to map agricultural fields with bare soil. A total of 101 sites, which were sampled either at the regional scale or within one field, were identified as bare by means of this map. Predictions were made from the mosaic AISA spectra which were related to SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples, considering those 75 sites outside cloud shadows only, and different sampling strategies for selecting calibration samples. Validation root-mean-square errors (RMSE) were comprised between 3.73 and 4.49 g. Kg-1 and were ~4 g. Kg-1 in median. The most performing models in terms of coefficient of determination (R²) and Residual Prediction Deviation (RPD) values were the

Recent micro-CT scanner developments at UGCT

NASA Astrophysics Data System (ADS)

Dierick, Manuel; Van Loo, Denis; Masschaele, Bert; Van den Bulcke, Jan; Van Acker, Joris; Cnudde, Veerle; Van Hoorebeke, Luc

2014-04-01

This paper describes two X-ray micro-CT scanners which were recently developed to extend the experimental possibilities of microtomography research at the Centre for X-ray Tomography (www.ugct.ugent.be) of the Ghent University (Belgium). The first scanner, called Nanowood, is a wide-range CT scanner with two X-ray sources (160 kVmax) and two detectors, resolving features down to 0.4 μm in small samples, but allowing samples up to 35 cm to be scanned. This is a sample size range of 3 orders of magnitude, making this scanner well suited for imaging multi-scale materials such as wood, stone, etc. Besides the traditional cone-beam acquisition, Nanowood supports helical acquisition, and it can generate images with significant phase-contrast contributions. The second scanner, known as the Environmental micro-CT scanner (EMCT), is a gantry based micro-CT scanner with variable magnification for scanning objects which are not easy to rotate in a standard micro-CT scanner, for example because they are physically connected to external experimental hardware such as sensor wiring, tubing or others. This scanner resolves 5 μm features, covers a field-of-view of about 12 cm wide with an 80 cm vertical travel range. Both scanners will be extensively described and characterized, and their potential will be demonstrated with some key application results.

Hyperspectral microscopy to identify foodborne bacteria with optimum lighting source

USDA-ARS?s Scientific Manuscript database

Hyperspectral microscopy is an emerging technology for rapid detection of foodborne pathogenic bacteria. Since scattering spectral signatures from hyperspectral microscopic images (HMI) vary with lighting sources, it is important to select optimal lights. The objective of this study is to compare t...

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

Snapshot Hyperspectral Volumetric Microscopy

NASA Astrophysics Data System (ADS)

Wu, Jiamin; Xiong, Bo; Lin, Xing; He, Jijun; Suo, Jinli; Dai, Qionghai

2016-04-01

The comprehensive analysis of biological specimens brings about the demand for capturing the spatial, temporal and spectral dimensions of visual information together. However, such high-dimensional video acquisition faces major challenges in developing large data throughput and effective multiplexing techniques. Here, we report the snapshot hyperspectral volumetric microscopy that computationally reconstructs hyperspectral profiles for high-resolution volumes of ~1000 μm × 1000 μm × 500 μm at video rate by a novel four-dimensional (4D) deconvolution algorithm. We validated the proposed approach with both numerical simulations for quantitative evaluation and various real experimental results on the prototype system. Different applications such as biological component analysis in bright field and spectral unmixing of multiple fluorescence are demonstrated. The experiments on moving fluorescent beads and GFP labelled drosophila larvae indicate the great potential of our method for observing multiple fluorescent markers in dynamic specimens.

System and method for progressive band selection for hyperspectral images

NASA Technical Reports Server (NTRS)

Fisher, Kevin (Inventor)

2013-01-01

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for progressive band selection for hyperspectral images. A system having module configured to control a processor to practice the method calculates a virtual dimensionality of a hyperspectral image having multiple bands to determine a quantity Q of how many bands are needed for a threshold level of information, ranks each band based on a statistical measure, selects Q bands from the multiple bands to generate a subset of bands based on the virtual dimensionality, and generates a reduced image based on the subset of bands. This approach can create reduced datasets of full hyperspectral images tailored for individual applications. The system uses a metric specific to a target application to rank the image bands, and then selects the most useful bands. The number of bands selected can be specified manually or calculated from the hyperspectral image's virtual dimensionality.

Physical Retrieval of Surface Emissivity Spectrum from Hyperspectral Infrared Radiances

NASA Technical Reports Server (NTRS)

Li, Jun; Weisz, Elisabeth; Zhou, Daniel K.

2007-01-01

Retrieval of temperature, moisture profiles and surface skin temperature from hyperspectral infrared (IR) radiances requires spectral information about the surface emissivity. Using constant or inaccurate surface emissivities typically results in large retrieval errors, particularly over semi-arid or arid areas where the variation in emissivity spectrum is large both spectrally and spatially. In this study, a physically based algorithm has been developed to retrieve a hyperspectral IR emissivity spectrum simultaneously with the temperature and moisture profiles, as well as the surface skin temperature. To make the solution stable and efficient, the hyperspectral emissivity spectrum is represented by eigenvectors, derived from the laboratory measured hyperspectral emissivity database, in the retrieval process. Experience with AIRS (Atmospheric InfraRed Sounder) radiances shows that a simultaneous retrieval of the emissivity spectrum and the sounding improves the surface skin temperature as well as temperature and moisture profiles, particularly in the near surface layer.

Statistical modeling of natural backgrounds in hyperspectral LWIR data

NASA Astrophysics Data System (ADS)

Truslow, Eric; Manolakis, Dimitris; Cooley, Thomas; Meola, Joseph

2016-09-01

Hyperspectral sensors operating in the long wave infrared (LWIR) have a wealth of applications including remote material identification and rare target detection. While statistical models for modeling surface reflectance in visible and near-infrared regimes have been well studied, models for the temperature and emissivity in the LWIR have not been rigorously investigated. In this paper, we investigate modeling hyperspectral LWIR data using a statistical mixture model for the emissivity and surface temperature. Statistical models for the surface parameters can be used to simulate surface radiances and at-sensor radiance which drives the variability of measured radiance and ultimately the performance of signal processing algorithms. Thus, having models that adequately capture data variation is extremely important for studying performance trades. The purpose of this paper is twofold. First, we study the validity of this model using real hyperspectral data, and compare the relative variability of hyperspectral data in the LWIR and visible and near-infrared (VNIR) regimes. Second, we illustrate how materials that are easily distinguished in the VNIR, may be difficult to separate when imaged in the LWIR.

The SPEX-airborne multi-angle spectropolarimeter on NASA's ER-2 research aircraft: capabilities, data processing and data products

NASA Astrophysics Data System (ADS)

Rietjens, J.; Smit, M.; Hasekamp, O. P.; Grim, M.; Eggens, M.; Eigenraam, A.; Keizer, G.; van Loon, D.; Talsma, J.; van der Vlugt, J.; Wolfs, R.; van Harten, G.; Rheingans, B. E.; Snik, F.; Keller, C. U.; Smit, H.

2016-12-01

A multi-angle spectropolarimeter payload, "SPEX-airborne" has been developed for observing and characterizing aerosols from NASA's high-altitude research aircraft ER-2. SPEX-airborne provides autonomously multi-angle snapshot measurements of spectral radiance and degree of linear polarization over a 7 degree swath in the visible part of the optical spectrum. The instrument is unique in the sense that it combines 30 highly accurate polarimetric measurements with hyperspectral radiance measurements at 2.5 nm resolution simultaneously at nine fixed viewing angles and that it offers the possibility to include polarimetric measurements in absorption bands at lower accuracy. This combination of measurements holds great potential for present and new retrieval algorithms to derive aerosol microphysical properties during airborne campaigns. The opto-mechanical subsystem of SPEX-airborne is based on the Spectropolarimeter for Planetary EXploration (SPEX) prototype, which has been developed over recent years by a consortium of Dutch institutes and industry. The polarimetry technique used is spectral polarization modulation, which has been proven to enable high accuracy polarimetric measurements. In laboratory conditions, the SPEX prototype has a demonstrated polarimetric accuracy of 0.002 in the degree of linear polarization. The SPEX prototype has been made fit for autonomous operation on NASA's ER-2 high altitude platform. In this presentation we will present the design and main subsystems of the payload, and address the operational modes. An outline of the data processing chain including calibration data will be given and the foreseen capability and performance will be discussed. We will discuss the quality of the polarimetric measurement in the lab and as recorded during the maiden flight in 2016 when SPEX-airborne was flying together with JPL's AirMSPI imaging polarimeter. Finally, we will give an outlook on the processing of the data of land and ocean scenes, and on the

Quantitative Hyperspectral Reflectance Imaging

PubMed Central

Klein, Marvin E.; Aalderink, Bernard J.; Padoan, Roberto; de Bruin, Gerrit; Steemers, Ted A.G.

2008-01-01

Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm (near-ultraviolet, visible and near-infrared). By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief (National Archives of The Netherlands) to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms. PMID:27873831

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region

PubMed Central

Thorpe, Andrew K.; Thompson, David R.; Hulley, Glynn; Kort, Eric Adam; Vance, Nick; Borchardt, Jakob; Krings, Thomas; Gerilowski, Konstantin; Sweeney, Colm; Conley, Stephen; Bue, Brian D.; Aubrey, Andrew D.; Hook, Simon; Green, Robert O.

2016-01-01

Methane (CH4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ∼ 2 kg/h to 5 kg/h through ∼ 5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign. PMID:27528660

Line-scan hyperspectral imaging techniques for food and agricultural applications

USDA-ARS?s Scientific Manuscript database

Hyperspectral imaging technologies in the food and agricultural area have been evolved rapidly during the past 15 years owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched and developed in diffe...

Integration of airborne optical and thermal imagery for archaeological subsurface structures detection: the Arpi case study (Italy)

NASA Astrophysics Data System (ADS)

Bassani, C.; Cavalli, R. M.; Fasulli, L.; Palombo, A.; Pascucci, S.; Santini, F.; Pignatti, S.

2009-04-01

The application of Remote Sensing data for detecting subsurface structures is becoming a remarkable tool for the archaeological observations to be combined with the near surface geophysics [1, 2]. As matter of fact, different satellite and airborne sensors have been used for archaeological applications, such as the identification of spectral anomalies (i.e. marks) related to the buried remnants within archaeological sites, and the management and protection of archaeological sites [3, 5]. The dominant factors that affect the spectral detectability of marks related to manmade archaeological structures are: (1) the spectral contrast between the target and background materials, (2) the proportion of the target on the surface (relative to the background), (3) the imaging system characteristics being used (i.e. bands, instrument noise and pixel size), and (4) the conditions under which the surface is being imaged (i.e. illumination and atmospheric conditions) [4]. In this context, just few airborne hyperspectral sensors were applied for cultural heritage studies, among them the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer), the CASI (Compact Airborne Spectrographic Imager), the HyMAP (Hyperspectral MAPping) and the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer). Therefore, the application of high spatial/spectral resolution imagery arise the question on which is the trade off between high spectral and spatial resolution imagery for archaeological applications and which spectral region is optimal for the detection of subsurface structures. This paper points out the most suitable spectral information useful to evaluate the image capability in terms of spectral anomaly detection of subsurface archaeological structures in different land cover contexts. In this study, we assess the capability of MIVIS and CASI reflectances and of ATM and MIVIS emissivities (Table 1) for subsurface archaeological prospection in different sites of the Arpi

Tunable thin-film optical filters for hyperspectral microscopy

NASA Astrophysics Data System (ADS)

Favreau, Peter F.; Rich, Thomas C.; Prabhat, Prashant; Leavesley, Silas J.

2013-02-01

Hyperspectral imaging was originally developed for use in remote sensing applications. More recently, it has been applied to biological imaging systems, such as fluorescence microscopes. The ability to distinguish molecules based on spectral differences has been especially advantageous for identifying fluorophores in highly autofluorescent tissues. A key component of hyperspectral imaging systems is wavelength filtering. Each filtering technology used for hyperspectral imaging has corresponding advantages and disadvantages. Recently, a new optical filtering technology has been developed that uses multi-layered thin-film optical filters that can be rotated, with respect to incident light, to control the center wavelength of the pass-band. Compared to the majority of tunable filter technologies, these filters have superior optical performance including greater than 90% transmission, steep spectral edges and high out-of-band blocking. Hence, tunable thin-film optical filters present optical characteristics that may make them well-suited for many biological spectral imaging applications. An array of tunable thin-film filters was implemented on an inverted fluorescence microscope (TE 2000, Nikon Instruments) to cover the full visible wavelength range. Images of a previously published model, GFP-expressing endothelial cells in the lung, were acquired using a charge-coupled device camera (Rolera EM-C2, Q-Imaging). This model sample presents fluorescently-labeled cells in a highly autofluorescent environment. Linear unmixing of hyperspectral images indicates that thin-film tunable filters provide equivalent spectral discrimination to our previous acousto-optic tunable filter-based approach, with increased signal-to-noise characteristics. Hence, tunable multi-layered thin film optical filters may provide greatly improved spectral filtering characteristics and therefore enable wider acceptance of hyperspectral widefield microscopy.

An integrated hyperspectral and SAR satellite constellation for environment monitoring

NASA Astrophysics Data System (ADS)

Wang, Jinnian; Ren, Fuhu; Xie, Chou; An, Jun; Tong, Zhanbo

2017-09-01

A fully-integrated, Hyperspectral optical and SAR (Synthetic Aperture Radar) constellation of small earth observation satellites will be deployed over multiple launches from last December to next five years. The Constellation is expected to comprise a minimum of 16 satellites (8 SAR and 8 optical ) flying in two orbital planes, with each plane consisting of four satellite pairs, equally-spaced around the orbit plane. Each pair of satellites will consist of a hyperspectral/mutispectral optical satellite and a high-resolution SAR satellite (X-band) flying in tandem. The constellation is expected to offer a number of innovative capabilities for environment monitoring. As a pre-launch experiment, two hyperspectral earth observation minisatellites, Spark 01 and 02 were launched as secondary payloads together with Tansat in December 2016 on a CZ-2D rocket. The satellites feature a wide-range hyperspectral imager. The ground resolution is 50 m, covering spectral range from visible to near infrared (420 nm - 1000 nm) and a swath width of 100km. The imager has an average spectral resolution of 5 nm with 148 channels, and a single satellite could obtain hyperspectral imagery with 2.5 million km2 per day, for global coverage every 16 days. This paper describes the potential applications of constellation image in environment monitoring.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

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

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

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

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Hybrid Dispersion Laser Scanner

PubMed Central

Goda, K.; Mahjoubfar, A.; Wang, C.; Fard, A.; Adam, J.; Gossett, D. R.; Ayazi, A.; Sollier, E.; Malik, O.; Chen, E.; Liu, Y.; Brown, R.; Sarkhosh, N.; Di Carlo, D.; Jalali, B.

2012-01-01

Laser scanning technology is one of the most integral parts of today's scientific research, manufacturing, defense, and biomedicine. In many applications, high-speed scanning capability is essential for scanning a large area in a short time and multi-dimensional sensing of moving objects and dynamical processes with fine temporal resolution. Unfortunately, conventional laser scanners are often too slow, resulting in limited precision and utility. Here we present a new type of laser scanner that offers ∼1,000 times higher scan rates than conventional state-of-the-art scanners. This method employs spatial dispersion of temporally stretched broadband optical pulses onto the target, enabling inertia-free laser scans at unprecedented scan rates of nearly 100 MHz at 800 nm. To show our scanner's broad utility, we use it to demonstrate unique and previously difficult-to-achieve capabilities in imaging, surface vibrometry, and flow cytometry at a record 2D raster scan rate of more than 100 kHz with 27,000 resolvable points. PMID:22685627

Hyperspectral imaging of bruised skin

NASA Astrophysics Data System (ADS)

Randeberg, Lise L.; Baarstad, Ivar; Løke, Trond; Kaspersen, Peter; Svaasand, Lars O.

2006-02-01

Bruises can be important evidence in legal medicine, for example in cases of child abuse. Optical techniques can be used to discriminate and quantify the chromophores present in bruised skin, and thereby aid dating of an injury. However, spectroscopic techniques provide only average chromophore concentrations for the sampled volume, and contain little information about the spatial chromophore distribution in the bruise. Hyperspectral imaging combines the power of imaging and spectroscopy, and can provide both spectroscopic and spatial information. In this study a hyperspectral imaging system developed by Norsk Elektro Optikk AS was used to measure the temporal development of bruised skin in a human volunteer. The bruises were inflicted by paintball bullets. The wavelength ranges used were 400 - 1000 nm (VNIR) and 900 - 1700 nm (SWIR), and the spectral sampling intervals were 3.7 and 5 nm, respectively. Preliminary results show good spatial discrimination of the bruised areas compared to normal skin. Development of a white spot can be seen in the central zone of the bruises. This central white zone was found to resemble the shape of the object hitting the skin, and is believed to develop in areas where the impact caused vessel damage. These results show that hyperspectral imaging is a promising technique to evaluate the temporal and spatial development of bruises on human skin.

High-resolution hyperspectral ground mapping for robotic vision

NASA Astrophysics Data System (ADS)

Neuhaus, Frank; Fuchs, Christian; Paulus, Dietrich

2018-04-01

Recently released hyperspectral cameras use large, mosaiced filter patterns to capture different ranges of the light's spectrum in each of the camera's pixels. Spectral information is sparse, as it is not fully available in each location. We propose an online method that avoids explicit demosaicing of camera images by fusing raw, unprocessed, hyperspectral camera frames inside an ego-centric ground surface map. It is represented as a multilayer heightmap data structure, whose geometry is estimated by combining a visual odometry system with either dense 3D reconstruction or 3D laser data. We use a publicly available dataset to show that our approach is capable of constructing an accurate hyperspectral representation of the surface surrounding the vehicle. We show that in many cases our approach increases spatial resolution over a demosaicing approach, while providing the same amount of spectral information.

Demystifying autofluorescence with excitation scanning hyperspectral imaging

NASA Astrophysics Data System (ADS)

Deal, Joshua; Harris, Bradley; Martin, Will; Lall, Malvika; Lopez, Carmen; Rider, Paul; Boudreaux, Carole; Rich, Thomas; Leavesley, Silas J.

2018-02-01

Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitationscanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states.

Fourth Airborne Geoscience Workshop

NASA Technical Reports Server (NTRS)

1991-01-01

The focus of the workshop was on how the airborne community can assist in achieving the goals of the Global Change Research Program. The many activities that employ airborne platforms and sensors were discussed: platforms and instrument development; airborne oceanography; lidar research; SAR measurements; Doppler radar; laser measurements; cloud physics; airborne experiments; airborne microwave measurements; and airborne data collection.

Radiation calibration for LWIR Hyperspectral Imager Spectrometer

NASA Astrophysics Data System (ADS)

Yang, Zhixiong; Yu, Chunchao; Zheng, Wei-jian; Lei, Zhenggang; Yan, Min; Yuan, Xiaochun; Zhang, Peizhong

2014-11-01

The radiometric calibration of LWIR Hyperspectral imager Spectrometer is presented. The lab has been developed to LWIR Interferometric Hyperspectral imager Spectrometer Prototype(CHIPED-I) to study Lab Radiation Calibration, Two-point linear calibration is carried out for the spectrometer by using blackbody respectively. Firstly, calibration measured relative intensity is converted to the absolute radiation lightness of the object. Then, radiation lightness of the object is is converted the brightness temperature spectrum by the method of brightness temperature. The result indicated †that this method of Radiation Calibration calibration was very good.

Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

NASA Astrophysics Data System (ADS)

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

Blind estimation of blur in hyperspectral images

NASA Astrophysics Data System (ADS)

Zhang, Mo; Vozel, Benoit; Chehdi, Kacem; Uss, Mykhail; Abramov, Sergey; Lukin, Vladimir

2017-10-01

Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present on the original image, blind restoration methods can only be considered. By blind, we mean absolutely no knowledge neither of the blur point spread function (PSF) nor the original latent channel and the noise level. In this study, we address the blind restoration of the degraded channels component-wise, according to a sequential scheme. For each degraded channel, the sequential scheme estimates the blur point spread function (PSF) in a first stage and deconvolves the degraded channel in a second and final stage by means of using the PSF previously estimated. We propose a new component-wise blind method for estimating effectively and accurately the blur point spread function. This method follows recent approaches suggesting the detection, selection and use of sufficiently salient edges in the current processed channel for supporting the regularized blur PSF estimation. Several modifications are beneficially introduced in our work. A new selection of salient edges through thresholding adequately the cumulative distribution of their corresponding gradient magnitudes is introduced. Besides, quasi-automatic and spatially adaptive tuning of the involved regularization parameters is considered. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from and four representative edge-sparsifying regularized methods of the literature already assessed in a previous work. Our attention is mainly paid to the objective analysis (via ݈l1-norm) of the blur PSF error estimation accuracy. The tests are performed on a synthetic hyperspectral image. This synthetic hyperspectral image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic spatial

Feasibility study for airborne fluorescence/reflectivity lidar bathymetry

NASA Astrophysics Data System (ADS)

Steinvall, Ove; Kautsky, Hans; Tulldahl, Michael; Wollner, Erika

2012-06-01

There is a demand from the authorities to have good maps of the coastal environment for their exploitation and preservation of the coastal areas. The goal for environmental mapping and monitoring is to differentiate between vegetation and non-vegetated bottoms and, if possible, to differentiate between species. Airborne lidar bathymetry is an interesting method for mapping shallow underwater habitats. In general, the maximum depth range for airborne laser exceeds the possible depth range for passive sensors. Today, operational lidar systems are able to capture the bottom (or vegetation) topography as well as estimations of the bottom reflectivity using e.g. reflected bottom pulse power. In this paper we study the possibilities and advantages for environmental mapping, if laser sensing would be further developed from single wavelength depth sounding systems to include multiple emission wavelengths and fluorescence receiver channels. Our results show that an airborne fluorescence lidar has several interesting features which might be useful in mapping underwater habitats. An example is the laser induced fluorescence giving rise to the emission spectrum which could be used for classification together with the elastic lidar signal. In the first part of our study, vegetation and substrate samples were collected and their spectral reflectance and fluorescence were subsequently measured in laboratory. A laser wavelength of 532 nm was used for excitation of the samples. The choice of 532 nm as excitation wavelength is motivated by the fact that this wavelength is commonly used in bathymetric laser scanners and that the excitation wavelengths are limited to the visual region as e.g. ultraviolet radiation is highly attenuated in water. The second part of our work consisted of theoretical performance calculations for a potential real system, and comparison of separability between species and substrate signatures using selected wavelength regions for fluorescence sensing.

Hyperspectral Shack–Hartmann test

PubMed Central

Birch, Gabriel C.; Descour, Michael R.; Tkaczyk, Tomasz S.

2011-01-01

A hyperspectral Shack–Hartmann test bed has been developed to characterize the performance of miniature optics across a wide spectral range, a necessary first step in developing broadband achromatized all-polymer endomicroscopes. The Shack–Hartmann test bed was used to measure the chromatic focal shift (CFS) of a glass singlet lens and a glass achromatic lens, i.e., lenses representing the extrema of CFS magnitude in polymer elements to be found in endomicroscope systems. The lenses were tested from 500 to 700 nm in 5 and 10 nm steps, respectively. In both cases, we found close agreement between test results obtained from a ZEMAX model of the test bed and test lens and those obtained by experiment (maximum error of 12 μm for the singlet lens and 5 μm for the achromatic triplet lens). Future applications of the hyperspectral Shack–Hartmann test include measurements of aberrations as a function of wavelength, characterization of manufactured plastic endomicroscope elements and systems, and reverse optimization. PMID:20885478

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Spectral-Spatial Classification of Hyperspectral Images Using Hierarchical Optimization

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two remote sensing hyperspectral images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques.

Soil Moisture Estimation Using Hyperspectral SWIR Imagery

NASA Astrophysics Data System (ADS)

Lewis, D.

2007-12-01

The U.S. Geological Survey (USGS) is engaged with the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) and the University of Georgia's National Environmentally Sound Production Agriculture Laboratory (NESPAL) both in Tifton, Georgia, USA, to develop transformations for medium and high resolution remotely sensed images to generate moisture indicators for soil. The Institute for Technology Development (ITD) is located at the Stennis Space Center in southern Mississippi and has developed hyperspectral sensor systems that, when mounted in aircraft, collect electromagnetic reflectance data of the terrain. The sensor suite consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near InfraRed (VNIR) and Short Wave InfraRed (SWIR). The USDA/ ARS' Southeast Watershed Research Laboratory has probes that measure and record soil moisture. Data taken from the ITD SWIR sensor and the USDA/ARS soil moisture meters were analyzed to study the informatics relationships between SWIR data and measured soil moisture. The geographic locations of 29 soil moisture meters provided by the USDA/ARS are in the vicinity of Tifton, Georgia. Using USGS Digital Ortho Quads (DOQ), flightlines were drawn over the 29 soil moisture meters. The SWIR sensor was installed into an aircraft. The coordinates for the flightlines were also loaded into the navigational system of the aircraft. This airborne platform was used to collect the data over these flightlines. In order to prepare the data set for analysis, standard preprocessing was performed. These standard processes included sensor calibration, spectral subsetting, and atmospheric calibration. All 60 bands of the SWIR data were collected for each line in the image data, 15 bands of which were stripped from the data set leaving 45 bands of information in the wavelength range of 906 to 1705 nanometers. All the image files were calibrated using the regression equations

[Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing].

PubMed

Wang, Li-Wen; Wei, Ya-Xing

2013-10-01

Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.

Portable biochip scanner device

DOEpatents

Perov, Alexander; Sharonov, Alexei; Mirzabekov, Andrei D.

2002-01-01

A portable biochip scanner device used to detect and acquire fluorescence signal data from biological microchips (biochips) is provided. The portable biochip scanner device employs a laser for emitting an excitation beam. An optical fiber delivers the laser beam to a portable biochip scanner. A lens collimates the laser beam, the collimated laser beam is deflected by a dichroic mirror and focused by an objective lens onto a biochip. The fluorescence light from the biochip is collected and collimated by the objective lens. The fluorescence light is delivered to a photomultiplier tube (PMT) via an emission filter and a focusing lens. The focusing lens focuses the fluorescence light into a pinhole. A signal output of the PMT is processed and displayed.

HICO and RAIDS Experiment Payload - Hyperspectral Imager for the Coastal Ocean

NASA Technical Reports Server (NTRS)

Corson, Mike

2009-01-01

HICO and RAIDS Experiment Payload - Hyperspectral Imager For The Coastal Ocean (HREP-HICO) will operate a visible and near-infrared (VNIR) Maritime Hyperspectral Imaging (MHSI) system, to detect, identify and quantify coastal geophysical features from the International Space Station.

Mapping the Wetland Vegetation Communities of the Australian Great Artesian Basin Springs Using SAM, Mtmf and Spectrally Segmented PCA Hyperspectral Analyses

NASA Astrophysics Data System (ADS)

White, D. C.; Lewis, M. M.

2012-07-01

The Australian Great Artesian Basin (GAB) supports a unique and diverse range of groundwater dependent wetland ecosystems termed GAB springs. In recent decades the ecological sustainability of the springs has become uncertain as demands on this iconic groundwater resource increase. The impacts of existing water extractions for mining and pastoral activities are unknown. This situation is compounded by the likelihood of future increasing demand for extractions. Hyperspectral remote sensing provides the necessary spectral and spatial detail to discriminate wetland vegetation communities. Therefore the objectives of this paper are to discriminate the spatial extent and distribution of key spring wetland vegetation communities associated with the GAB springs evaluating three hyperspectral techniques: Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF) and Spectrally Segmented PCA. In addition, to determine if the hyperspectral techniques developed can be applied at a number of sites representative of the range of spring formations and geomorphic settings and at two temporal intervals. Two epochs of HyMap airborne hyperspectral imagery were captured for this research in March 2009 and April 2011 at a number of sites representative of the floristic and geomorphic diversity of GAB spring groups/complexes within South Australia. Colour digital aerial photography at 30 cm GSD was acquired concurrently with the HyMap imagery. The image acquisition coincided with a field campaign of spectroradiometry measurements and a botanical survey. To identify key wavebands which have the greatest capability to discriminate vegetation communities of the GAB springs and surrounding area three hyperspectral data reduction techniques were employed: (i) Spectrally Segmented PCA (SSPCA); (ii) the Minimum Noise Transform (MNF); and (iii) the Pixel Purity Index (PPI). SSPCA was applied to NDVI-masked vegetation portions of the HyMap imagery with wavelength regions spectrally

Transferability of multi- and hyperspectral optical biocrust indices

NASA Astrophysics Data System (ADS)

Rodríguez-Caballero, E.; Escribano, P.; Olehowski, C.; Chamizo, S.; Hill, J.; Cantón, Y.; Weber, B.

2017-04-01

Biological soil crusts (biocrusts) are communities of cyanobacteria, algae, microfungi, lichens and bryophytes in varying proportions, which live within or immediately on top of the uppermost millimeters of the soil in arid and semiarid regions. As biocrusts are highly relevant for ecosystem processes like carbon, nitrogen, and water cycling, a correct characterization of their spatial distribution is required. Following this objective, considerable efforts have been devoted to the identification and mapping of biocrusts using remote sensing data, and several mapping indices have been developed. However, their transferability to different regions has only rarely been tested. In this study we investigated the transferability of two multispectral indices, i.e. the Crust Index (CI) and the Biological Soil Crust Index (BSCI), and two hyperspectral indices, i.e. the Continuum Removal Crust Identification Algorithm (CRCIA) and the Crust Development Index (CDI), in three sites dominated by biocrusts, but with differences in soil and vegetation composition. Whereas multispectral indices have been important and valuable tools for first approaches to map and classify biological soil crusts, hyperspectral data and indices developed for these allowed to classify biocrusts at much higher accuracy. While multispectral indices showed Kappa (κ) values below 0.6, hyperspectral indices obtained good classification accuracy (κ ∼ 0.8) in both the study area where they had been developed and in the newly tested region. These results highlight the capability of hyperspectral sensors to identify specific absorption features related to photosynthetic pigments as chlorophyll and carotenoids, but also the limitation of multispectral information to discriminate between areas dominated by biocrusts, vegetation or bare soil. Based on these results we conclude that remote sensing offers an important and valid tool to map biocrusts. However, the spectral similarity between the main surface

Hyperspectral surveying for mineral resources in Alaska