Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

PubMed Central

Zhou, Tao; Pan, Jianjun; Zhang, Peiyu; Wei, Shanbao; Han, Tao

2017-01-01

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data. PMID:28587066

Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Saur, Günter

2011-11-01

Spaceborne SAR imagery offers high capability for wide-ranging maritime surveillance especially in situations, where AIS (Automatic Identification System) data is not available. Therefore, maritime objects have to be detected and optional information such as size, orientation, or object/ship class is desired. In recent research work, we proposed a SAR processing chain consisting of pre-processing, detection, segmentation, and classification for single-polarimetric (HH) TerraSAR-X StripMap images to finally assign detection hypotheses to class "clutter", "non-ship", "unstructured ship", or "ship structure 1" (bulk carrier appearance) respectively "ship structure 2" (oil tanker appearance). In this work, we extend the existing processing chain and are now able to handle full-polarimetric (HH, HV, VH, VV) TerraSAR-X data. With the possibility of better noise suppression using the different polarizations, we slightly improve both the segmentation and the classification process. In several experiments we demonstrate the potential benefit for segmentation and classification. Precision of size and orientation estimation as well as correct classification rates are calculated individually for single- and quad-polarization and compared to each other.

Remote sensing of a dynamic sub-arctic peatland reservoir using optical and synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Larter, Jarod Lee

Stephens Lake, Manitoba is an example of a peatland reservoir that has undergone physical changes related to mineral erosion and peatland disintegration processes since its initial impoundment. In this thesis I focused on the processes of peatland upheaval, transport, and disintegration as the primary drivers of dynamic change within the reservoir. The changes related to these processes are most frequent after initial reservoir impoundment and decline over time. They continue to occur over 35 years after initial flooding. I developed a remote sensing approach that employs both optical and microwave sensors for discriminating land (Le. floating peatlands, forested land, and barren land) from open water within the reservoir. High spatial resolution visible and near-infrared (VNIR) optical data obtained from the QuickBird satellite, and synthetic aperture radar (SAR) microwave data obtained from the RADARSAT-1 satellite were implemented. The approach was facilitated with a Geographic Information System (GIS) based validation map for the extraction of optical and SAR pixel data. Each sensor's extracted data set was first analyzed separately using univariate and multivariate statistical methods to determine the discriminant ability of each sensor. The initial analyses were followed by an integrated sensor approach; the development of an image classification model; and a change detection analysis. Results showed excellent (> 95%) classification accuracy using QuickBird satellite image data. Discrimination and classification of studied land cover classes using SAR image texture data resulted in lower overall classification accuracies (˜ 60%). SAR data classification accuracy improved to > 90% when classifying only land and water, demonstrating SAR's utility as a land and water mapping tool. An integrated sensor data approach showed no considerable improvement over the use of optical satellite image data alone. An image classification model was developed that could be used to map both detailed land cover classes and the land and water interface within the reservoir. Change detection analysis over a seven year period indicated that physical changes related to mineral erosion, peatland upheaval, transport, and disintegration, and operational water level variation continue to take place in the reservoir some 35 years after initial flooding. This thesis demonstrates the ability of optical and SAR satellite image remote sensing data sets to be used in an operational context for the routine discrimination of the land and water boundaries within a dynamic peatland reservoir. Future monitoring programs would benefit most from a complementary image acquisition program in which SAR images, known for their acquisition reliability under cloud cover, are acquired along with optical images given their ability to discriminate land cover classes in greater detail.

SAR image classification based on CNN in real and simulation datasets

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

A learning tool for optical and microwave satellite image processing and analysis

NASA Astrophysics Data System (ADS)

Dashondhi, Gaurav K.; Mohanty, Jyotirmoy; Eeti, Laxmi N.; Bhattacharya, Avik; De, Shaunak; Buddhiraju, Krishna M.

2016-04-01

This paper presents a self-learning tool, which contains a number of virtual experiments for processing and analysis of Optical/Infrared and Synthetic Aperture Radar (SAR) images. The tool is named Virtual Satellite Image Processing and Analysis Lab (v-SIPLAB) Experiments that are included in Learning Tool are related to: Optical/Infrared - Image and Edge enhancement, smoothing, PCT, vegetation indices, Mathematical Morphology, Accuracy Assessment, Supervised/Unsupervised classification etc.; Basic SAR - Parameter extraction and range spectrum estimation, Range compression, Doppler centroid estimation, Azimuth reference function generation and compression, Multilooking, image enhancement, texture analysis, edge and detection. etc.; SAR Interferometry - BaseLine Calculation, Extraction of single look SAR images, Registration, Resampling, and Interferogram generation; SAR Polarimetry - Conversion of AirSAR or Radarsat data to S2/C3/T3 matrix, Speckle Filtering, Power/Intensity image generation, Decomposition of S2/C3/T3, Classification of S2/C3/T3 using Wishart Classifier [3]. A professional quality polarimetric SAR software can be found at [8], a part of whose functionality can be found in our system. The learning tool also contains other modules, besides executable software experiments, such as aim, theory, procedure, interpretation, quizzes, link to additional reading material and user feedback. Students can have understanding of Optical and SAR remotely sensed images through discussion of basic principles and supported by structured procedure for running and interpreting the experiments. Quizzes for self-assessment and a provision for online feedback are also being provided to make this Learning tool self-contained. One can download results after performing experiments.

Fusion method of SAR and optical images for urban object extraction

NASA Astrophysics Data System (ADS)

Jia, Yonghong; Blum, Rick S.; Li, Fangfang

2007-11-01

A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR texture is extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high pass details extracted from the available Pan image by means of the á trous wavelet decomposition. Then, high pass details modulated with the texture is applied to obtain the fusion product by HPFM (High pass Filter-based Modulation) fusion method. A set of image data including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the experiment. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, and the proposed approach is effective for image interpret and classification.

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Marine Targets Classification in PolInSAR Data

NASA Astrophysics Data System (ADS)

Chen, Peng; Yang, Jingsong; Ren, Lin

2014-11-01

In this paper, marine stationary targets and moving targets are studied by Pol-In-SAR data of Radarsat-2. A new method of stationary targets detection is proposed. The method get the correlation coefficient image of the In-SAR data, and using the histogram of correlation coefficient image. Then, A Constant False Alarm Rate (CFAR) algorithm and The Probabilistic Neural Network model are imported to detect stationary targets. To find the moving targets, Azimuth Ambiguity is show as an important feature. We use the length of azimuth ambiguity to get the target's moving direction and speed. Make further efforts, Targets classification is studied by rebuild the surface elevation of marine targets.

Marine Targets Classification in PolInSAR Data

NASA Astrophysics Data System (ADS)

Chen, Peng; Yang, Jingsong; Ren, Lin

2014-11-01

In this paper, marine stationary targets and moving targets are studied by Pol-In-SAR data of Radarsat-2. A new method of stationary targets detection is proposed. The method get the correlation coefficient image of the In-SAR data, and using the histogram of correlation coefficient image. Then , A Constant False Alarm Rate (CFAR) algorithm and The Probabilistic Neural Network model are imported to detect stationary targets. To find the moving targets, Azimuth Ambiguity is show as an important feature. We use the length of azimuth ambiguity to get the target's moving direction and speed. Make further efforts, Targets classification is studied by rebuild the surface elevation of marine targets.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

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

Koch, Mark William; Steinbach, Ryan Matthew; Moya, Mary M

2015-10-01

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithmsmore » using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.« less

Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

NASA Technical Reports Server (NTRS)

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

1998-01-01

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

Land cover classification accuracy from electro-optical, X, C, and L-band Synthetic Aperture Radar data fusion

NASA Astrophysics Data System (ADS)

Hammann, Mark Gregory

The fusion of electro-optical (EO) multi-spectral satellite imagery with Synthetic Aperture Radar (SAR) data was explored with the working hypothesis that the addition of multi-band SAR will increase the land-cover (LC) classification accuracy compared to EO alone. Three satellite sources for SAR imagery were used: X-band from TerraSAR-X, C-band from RADARSAT-2, and L-band from PALSAR. Images from the RapidEye satellites were the source of the EO imagery. Imagery from the GeoEye-1 and WorldView-2 satellites aided the selection of ground truth. Three study areas were chosen: Wad Medani, Sudan; Campinas, Brazil; and Fresno- Kings Counties, USA. EO imagery were radiometrically calibrated, atmospherically compensated, orthorectifed, co-registered, and clipped to a common area of interest (AOI). SAR imagery were radiometrically calibrated, and geometrically corrected for terrain and incidence angle by converting to ground range and Sigma Naught (?0). The original SAR HH data were included in the fused image stack after despeckling with a 3x3 Enhanced Lee filter. The variance and Gray-Level-Co-occurrence Matrix (GLCM) texture measures of contrast, entropy, and correlation were derived from the non-despeckled SAR HH bands. Data fusion was done with layer stacking and all data were resampled to a common spatial resolution. The Support Vector Machine (SVM) decision rule was used for the supervised classifications. Similar LC classes were identified and tested for each study area. For Wad Medani, nine classes were tested: low and medium intensity urban, sparse forest, water, barren ground, and four agriculture classes (fallow, bare agricultural ground, green crops, and orchards). For Campinas, Brazil, five generic classes were tested: urban, agriculture, forest, water, and barren ground. For the Fresno-Kings Counties location 11 classes were studied: three generic classes (urban, water, barren land), and eight specific crops. In all cases the addition of SAR to EO resulted in higher overall classification accuracies. In many cases using more than a single SAR band also improved the classification accuracy. There was no single best SAR band for all cases; for specific study areas or LC classes, different SAR bands were better. For Wad Medani, the overall accuracy increased nearly 25% over EO by using all three SAR bands and GLCM texture. For Campinas, the improvement over EO was 4.3%; the large areas of vegetation were classified by EO with good accuracy. At Fresno-Kings Counties, EO+SAR fusion improved the overall classification accuracy by 7%. For times or regions where EO is not available due to extended cloud cover, classification with SAR is often the only option; note that SAR alone typically results in lower classification accuracies than when using EO or EO-SAR fusion. Fusion of EO and SAR was especially important to improve the separability of orchards from other crops, and separating urban areas with buildings from bare soil; those classes are difficult to accurately separate with EO. The outcome of this dissertation contributes to the understanding of the benefits of combining data from EO imagery with different SAR bands and SAR derived texture data to identify different LC classes. In times of increased public and private budget constraints and industry consolidation, this dissertation provides insight as to which band packages could be most useful for increased accuracy in LC classification.

Scattering property based contextual PolSAR speckle filter

NASA Astrophysics Data System (ADS)

Mullissa, Adugna G.; Tolpekin, Valentyn; Stein, Alfred

2017-12-01

Reliability of the scattering model based polarimetric SAR (PolSAR) speckle filter depends upon the accurate decomposition and classification of the scattering mechanisms. This paper presents an improved scattering property based contextual speckle filter based upon an iterative classification of the scattering mechanisms. It applies a Cloude-Pottier eigenvalue-eigenvector decomposition and a fuzzy H/α classification to determine the scattering mechanisms on a pre-estimate of the coherency matrix. The H/α classification identifies pixels with homogeneous scattering properties. A coarse pixel selection rule groups pixels that are either single bounce, double bounce or volume scatterers. A fine pixel selection rule is applied to pixels within each canonical scattering mechanism. We filter the PolSAR data and depending on the type of image scene (urban or rural) use either the coarse or fine pixel selection rule. Iterative refinement of the Wishart H/α classification reduces the speckle in the PolSAR data. Effectiveness of this new filter is demonstrated by using both simulated and real PolSAR data. It is compared with the refined Lee filter, the scattering model based filter and the non-local means filter. The study concludes that the proposed filter compares favorably with other polarimetric speckle filters in preserving polarimetric information, point scatterers and subtle features in PolSAR data.

Marine Targets Detection in Pol-SAR Data

NASA Astrophysics Data System (ADS)

Chen, Peng; Yang, Jingsong

2016-08-01

In this poster, we present a new method of marine target detection in Pol-SAR data. One band SAR image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR) algorithm. But some false detection may happen, as the sidelobe of antenna, Azimuth ambiguity, strong speckle noise and so on in the single band SAR image. Pol-SAR image can get more information of targets. After decomposition and false color composite, the sidelobe of antenna and Azimuth ambiguity could be deleted. So, the method presented include three steps, decomposion, false color composite and supervised classification. The result of Radarsat-2 SAR image test indicates a good accuracy. The detection results are compared with Automatic Indentify Sistem (AIS) data, the accuracy of right detection is above 95% and false detection ratio is below 5%.

Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

NASA Astrophysics Data System (ADS)

Parekh, R. A.; Mehta, R. L.; Vyas, A.

2016-10-01

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

NASA Astrophysics Data System (ADS)

Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing

2018-03-01

In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

Arctic coastal polynya observations with ERS-1 SAR and DMSP SSM/I

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Onstott, R. G.

1993-01-01

Work to improve the characterization of the distribution of new and young sea ice types and open water amount within Arctic coastal polynyas through the combined use of ERS-1 SAR (Synthetic Aperture Radar) and DMSP SSM/I (Defense Meteorological Satellite Program Special Sensor Microwave/Imager) data is described. Two St. Lawrence Island polynya events are studied using low resolution, geocoded SAR images and coincident SSM/I data. The SAR images are analyzed in terms of polarization and spectral gradient ratios. Results of the combined analysis show that the SAR ice type classification is consistent with that from SSM/I and that the combined use of SAR and SSM/I can improve the characterization of thin ice better than either data set can do alone.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

PubMed

Guo, Hao; Wu, Danni; An, Jubai

2017-08-09

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

PubMed Central

An, Jubai

2017-01-01

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. PMID:28792477

Determination of Classification Accuracy for Land Use/cover Types Using Landsat-Tm Spot-Mss and Multipolarized and Multi-Channel Synthetic Aperture Radar

NASA Astrophysics Data System (ADS)

Dondurur, Mehmet

The primary objective of this study was to determine the degree to which modern SAR systems can be used to obtain information about the Earth's vegetative resources. Information obtainable from microwave synthetic aperture radar (SAR) data was compared with that obtainable from LANDSAT-TM and SPOT data. Three hypotheses were tested: (a) Classification of land cover/use from SAR data can be accomplished on a pixel-by-pixel basis with the same overall accuracy as from LANDSAT-TM and SPOT data. (b) Classification accuracy for individual land cover/use classes will differ between sensors. (c) Combining information derived from optical and SAR data into an integrated monitoring system will improve overall and individual land cover/use class accuracies. The study was conducted with three data sets for the Sleeping Bear Dunes test site in the northwestern part of Michigan's lower peninsula, including an October 1982 LANDSAT-TM scene, a June 1989 SPOT scene and C-, L- and P-Band radar data from the Jet Propulsion Laboratory AIRSAR. Reference data were derived from the Michigan Resource Information System (MIRIS) and available color infrared aerial photos. Classification and rectification of data sets were done using ERDAS Image Processing Programs. Classification algorithms included Maximum Likelihood, Mahalanobis Distance, Minimum Spectral Distance, ISODATA, Parallelepiped, and Sequential Cluster Analysis. Classified images were rectified as necessary so that all were at the same scale and oriented north-up. Results were analyzed with contingency tables and percent correctly classified (PCC) and Cohen's Kappa (CK) as accuracy indices using CSLANT and ImagePro programs developed for this study. Accuracy analyses were based upon a 1.4 by 6.5 km area with its long axis east-west. Reference data for this subscene total 55,770 15 by 15 m pixels with sixteen cover types, including seven level III forest classes, three level III urban classes, two level II range classes, two water classes, one wetland class and one agriculture class. An initial analysis was made without correcting the 1978 MIRIS reference data to the different dates of the TM, SPOT and SAR data sets. In this analysis, highest overall classification accuracy (PCC) was 87% with the TM data set, with both SPOT and C-Band SAR at 85%, a difference statistically significant at the 0.05 level. When the reference data were corrected for land cover change between 1978 and 1991, classification accuracy with the C-Band SAR data increased to 87%. Classification accuracy differed from sensor to sensor for individual land cover classes, Combining sensors into hypothetical multi-sensor systems resulted in higher accuracies than for any single sensor. Combining LANDSAT -TM and C-Band SAR yielded an overall classification accuracy (PCC) of 92%. The results of this study indicate that C-Band SAR data provide an acceptable substitute for LANDSAT-TM or SPOT data when land cover information is desired of areas where cloud cover obscures the terrain. Even better results can be obtained by integrating TM and C-Band SAR data into a multi-sensor system.

Preliminary results of the comparative study between EO-1/Hyperion and ALOS/PALSAR

NASA Astrophysics Data System (ADS)

Koizumi, E.; Furuta, R.; Yamamoto, A.

2011-12-01

[Introduction]Hyper-spectral remote sensing images have been used for land-cover classification due to their high spectral resolutions. Synthetic Aperture Radar (SAR) remote sensing data are also useful to probe surface condition because radar image reflects surface geometry, although there are not so many reports about the land-cover detection with combination use of both hyper-spectral data and SAR data. Among SAR sensors, L-band SAR is thought to be useful tool to find physical properties because its comparatively long wave length can through small objects on surface. We are comparing the result of land cover classification and/or physical values from hyper-spectral and L-band SAR data to find the relationship between these two quite different sensors and to confirm the possibility of the combined analysis of hyper-spectral and L-band SAR data, and in this presentation we will report the preliminary result of this study. There are only few sources of both hyper-spectral and L-band SAR data from the space in this time, however, several space organizations plan to launch new satellites on which hyper-spectral or L-band SAR equipments are mounted in next few years. So, the importance of the combined analysis will increase more than ever. [Target Area]We are performing and planning analyses on the following areas in this study. (a)South of Cairo, Nile river area, Egypt, for sand, sandstone, limestone, river, crops. (b)Mount Sakurajima, Japan, for igneous rock and other related geological property. [Methods and Results]EO-1 Hyperion data are analyzed in this study as hyper-spectral data. The Hyperion equipment has 242 channels but some of them include full noise or have no data. We selected channels for analysis by checking each channel, and select about 150 channels (depend on the area). Before analysis, the atmospheric correction of ATCOR-3 was applied for the selected channels. The corrected data were analyzed by unsupervised classification or principal component analysis (PCA). We also did the unsupervised classification with the several components from PCA. According to the analysis results, several classifications can be extracted for each category (vegetation, sand and rocks, and water). One of the interesting results is that there are a few classes for sand as those of other categories, and these classes seem to reflect artificial and natural surface changes that are some result of excavation or scratching. ALOS PALSAR data are analyzed as L-band SAR data. We selected the Dual Polarization data for each target area. The data were converted to backscattered images, and then calculated some image statistic values. The topographic information also calculates with SAR interferometry technique as reference. Comparing the Hyperion classification results with the result of the calculation of statistic values from PALSAR, there are some areas where relativities seem to be confirmed. To confirm the combined analysis between hyper-spectral and L-band SAR data to detect and classify the surface material, further studies are still required. We will continue to investigate more efficient analytic methods and to examine other functions like the adopted channels, the number of class in classification, the kind of statistic information, and so on, to refine the method.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

NASA Astrophysics Data System (ADS)

Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

2017-09-01

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed classification scheme is 94.91 %, while that with the conventional classification scheme is 93.70 %. Moreover, for multi-temporal UAVSAR data, the averaged overall classification accuracy with the proposed classification scheme is up to 97.08 %, which is much higher than the 87.79 % from the conventional classification scheme. Furthermore, for multitemporal PolSAR data, the proposed classification scheme can achieve better robustness. The comparison studies also clearly demonstrate that mining and utilization of hidden polarimetric features and information in the rotation domain can gain the added benefits for PolSAR land cover classification and provide a new vision for PolSAR image interpretation and application.

Generalized interpretation scheme for arbitrary HR InSAR image pairs

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten

2013-10-01

Land cover classification of remote sensing imagery is an important topic of research. For example, different applications require precise and fast information about the land cover of the imaged scenery (e.g., disaster management and change detection). Focusing on high resolution (HR) spaceborne remote sensing imagery, the user has the choice between passive and active sensor systems. Passive systems, such as multispectral sensors, have the disadvantage of being dependent from weather influences (fog, dust, clouds, etc.) and time of day, since they work in the visible part of the electromagnetic spectrum. Here, active systems like Synthetic Aperture Radar (SAR) provide improved capabilities. As an interactive method analyzing HR InSAR image pairs, the CovAmCohTM method was introduced in former studies. CovAmCoh represents the joint analysis of locality (coefficient of variation - Cov), backscatter (amplitude - Am) and temporal stability (coherence - Coh). It delivers information on physical backscatter characteristics of imaged scene objects or structures and provides the opportunity to detect different classes of land cover (e.g., urban, rural, infrastructure and activity areas). As example, railway tracks are easily distinguishable from other infrastructure due to their characteristic bluish coloring caused by the gravel between the sleepers. In consequence, imaged objects or structures have a characteristic appearance in CovAmCoh images which allows the development of classification rules. In this paper, a generalized interpretation scheme for arbitrary InSAR image pairs using the CovAmCoh method is proposed. This scheme bases on analyzing the information content of typical CovAmCoh imagery using the semisupervised k-means clustering. It is shown that eight classes model the main local information content of CovAmCoh images sufficiently and can be used as basis for a classification scheme.

Combating speckle in SAR images - Vector filtering and sequential classification based on a multiplicative noise model

NASA Technical Reports Server (NTRS)

Lin, Qian; Allebach, Jan P.

1990-01-01

An adaptive vector linear minimum mean-squared error (LMMSE) filter for multichannel images with multiplicative noise is presented. It is shown theoretically that the mean-squared error in the filter output is reduced by making use of the correlation between image bands. The vector and conventional scalar LMMSE filters are applied to a three-band SIR-B SAR, and their performance is compared. Based on a mutliplicative noise model, the per-pel maximum likelihood classifier was derived. The authors extend this to the design of sequential and robust classifiers. These classifiers are also applied to the three-band SIR-B SAR image.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Analysis of Radarsat-2 Full Polarimetric Data for Forest Mapping

NASA Astrophysics Data System (ADS)

Maghsoudi, Yasser

Forests are a major natural resource of the Earth and control a wide range of environmental processes. Forests comprise a major part of the planet's plant biodiversity and have an important role in the global hydrological and biochemical cycles. Among the numerous potential applications of remote sensing in forestry, forest mapping plays a vital role for characterization of the forest in terms of species. Particularly, in Canada where forests occupy 45% of the territory, representing more than 400 million hectares of the total Canadian continental area. In this thesis, the potential of polarimetric SAR (PolSAR) Radarsat-2 data for forest mapping is investigated. This thesis has two principle objectives. First is to propose algorithms for analyzing the PolSAR image data for forest mapping. There are a wide range of SAR parameters that can be derived from PolSAR data. In order to make full use of the discriminative power offered by all these parameters, two categories of methods are proposed. The methods are based on the concept of feature selection and classifier ensemble. First, a nonparametric definition of the evaluation function is proposed and hence the methods NFS and CBFS. Second, a fast wrapper algorithm is proposed for the evaluation function in feature selection and hence the methods FWFS and FWCBFS. Finally, to incorporate the neighboring pixels information in classification an extension of the FWCBFS method i.e. CCBFS is proposed. The second objective of this thesis is to provide a comparison between leaf-on (summer) and leaf-off (fall) season images for forest mapping. Two Radarsat-2 images acquired in fine quad-polarized mode were chosen for this study. The images were collected in leaf-on and leaf-off seasons. We also test the hypothesis whether combining the SAR parameters obtained from both images can provide better results than either individual datasets. The rationale for this combination is that every dataset has some parameters which may be useful for forest mapping. To assess the potential of the proposed methods their performance have been compared with each other and with the baseline classifiers. The baseline methods include the Wishart classifier, which is a commonly used classification method in PolSAR community, as well as an SVM classifier with the full set of parameters. Experimental results showed a better performance of the leaf-off image compared to that of leaf-on image for forest mapping. It is also shown that combining leaf-off parameters with leaf-on parameters can significantly improve the classification accuracy. Also, the classification results (in terms of the overall accuracy) compared to the baseline classifiers demonstrate the effectiveness of the proposed nonparametric scheme for forest mapping.

C- and L-band space-borne SAR incidence angle normalization for efficient Arctic sea ice monitoring

NASA Astrophysics Data System (ADS)

Mahmud, M. S.; Geldsetzer, T.; Howell, S.; Yackel, J.; Nandan, V.

2017-12-01

C-band Synthetic Aperture Radar (SAR) has been widely used effectively for operational sea ice monitoring, owing to its greater seperability between snow-covered first-year (FYI) and multi-year (MYI) ice types, during winter. However, during the melt season, C-band SAR backscatter contrast reduces between FYI and MYI. To overcome the limitations of C-band, several studies have recommended utlizing L-band SAR, as it has the potential to significantly improve sea ice classification. Given its longer wavelength, L-band can efficiently separate FYI and MYI types, especially during melt season. Therefore, the combination of C- and L-band SAR is an optimal solution for efficient seasonal sea ice monitoring. As SAR acquires images over a range of incidence angles from near-range to far-range, SAR backscatter varies substantially. To compensate this variation in SAR backscatter, incidence angle dependency of C- and L-band SAR backscatter for different FYI and MYI types is crucial to quantify, which is the objective of this study. Time-series SAR imagery from C-band RADARSAT-2 and L-band ALOS PALSAR during winter months of 2010 across 60 sites over the Canadian Arctic was acquired. Utilizing 15 images for each sites during February-March for both C- and L-band SAR, incidence angle dependency was calculated. Our study reveals that L- and C-band backscatter from FYI and MYI decreases with increasing incidence angle. The mean incidence angle dependency for FYI and MYI were estimated to be -0.21 dB/1° and -0.30 dB/1° respectively from L-band SAR, and -0.22 dB/1° and -0.16 dB/1° from C-band SAR, respectively. While the incidence angle dependency for FYI was found to be similar in both frequencies, it doubled in case of MYI from L-band, compared to C-band. After applying the incidence angle normalization method to both C- and L-band SAR images, preliminary results indicate improved sea ice type seperability between FYI and MYI types, with substantially lower number of mixed pixels; thereby offering more reliable sea ice classification accuracies. Research findings from this study can be utilized to improve seasonal sea ice classification with higher accuracy for operational Arctic sea ice monitoring, especially in regions like the Canadian Arctic, where MYI detection is crucial for safer ship navigations.

Using SAR Interferograms and Coherence Images for Object-Based Delineation of Unstable Slopes

NASA Astrophysics Data System (ADS)

Friedl, Barbara; Holbling, Daniel

2015-05-01

This study uses synthetic aperture radar (SAR) interferometric products for the semi-automated identification and delineation of unstable slopes and active landslides. Single-pair interferograms and coherence images are therefore segmented and classified in an object-based image analysis (OBIA) framework. The rule-based classification approach has been applied to landslide-prone areas located in Taiwan and Southern Germany. The semi-automatically obtained results were validated against landslide polygons derived from manual interpretation.

Synthetic aperture radar for a crop information system: A multipolarization and multitemporal approach

NASA Astrophysics Data System (ADS)

Ban, Yifang

Acquisition of timely information is a critical requirement for successful management of an agricultural monitoring system. Crop identification and crop-area estimation can be done fairly successfully using satellite sensors operating in the visible and near-infrared (VIR) regions of the spectrum. However, data collection can be unreliable due to problems of cloud cover at critical stages of the growing season. The all-weather capability of synthetic aperture radar (SAR) imagery acquired from satellites provides data over large areas whenever crop information is required. At the same time, SAR is sensitive to surface roughness and should be able to provide surface information such as tillage-system characteristics. With the launch of ERS-1, the first long-duration SAR system became available. The analysis of airborne multipolarization SAR data, multitemporal ERS-1 SAR data, and their combinations with VIR data, is necessary for the development of image-analysis methodologies that can be applied to RADARSAT data for extracting agricultural crop information. The overall objective of this research is to evaluate multipolarization airborne SAR data, multitemporal ERS-1 SAR data, and combinations of ERS-1 SAR and satellite VIR data for crop classification using non-conventional algorithms. The study area is situated in Norwich Township, an agricultural area in Oxford County, southern Ontario, Canada. It has been selected as one of the few representative agricultural 'supersites' across Canada at which the relationships between radar data and agriculture are being studied. The major field crops are corn, soybeans, winter wheat, oats, barley, alfalfa, hay, and pasture. Using airborne C-HH and C-HV SAR data, it was found that approaches using contextual information, texture information and per-field classification for improving agricultural crop classification proved to be effective, especially the per-field classification method. Results show that three of the four best per-field classification accuracies (\\ K=0.91) are achieved using combinations of C-HH and C-VV SAR data. This confirms the strong potential of multipolarization data for crop classification. The synergistic effects of multitemporal ERS-1 SAR and Landsat TM data are evaluated for crop classification using an artificial neural network (ANN) approach. The results show that the per-field approach using a feed-forward ANN significantly improves the overall classification accuracy of both single-date and multitemporal SAR data. Using the combination of TM3,4,5 and Aug. 5 SAR data, the best per-field ANN classification of 96.8% was achieved. It represents an 8.5% improvement over a single TM3,4,5 classification alone. Using multitemporal ERS-1 SAR data acquired during the 1992 and 1993 growing seasons, the radar backscatter characteristics of crops and their underlying soils are analyzed. The SAR temporal backscatter profiles were generated for each crop type and the earliest times of the year for differentiation of individual crop types were determined. Orbital (incidence-angle) effects were also observed on all crops. The average difference between the two orbits was about 3 dB. Thus attention should be given to the local incidence-angle effects when using ERS-1 SAR data, especially when comparing fields from different scenes or different areas within the same scene. Finally, early- and mid-season multitemporal SAR data for crop classification using sequential-masking techniques are evaluated, based on the temporal backscatter profiles. It was found that all crops studied could be identified by July 21.

Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

NASA Astrophysics Data System (ADS)

Bigdeli, Behnaz; Pahlavani, Parham

2017-01-01

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Feng; Dong, Junyu; Li, Bo; Xu, Qizhi; Xie, Cui

2016-10-01

Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Contextual descriptors and neural networks for scene analysis in VHR SAR images

NASA Astrophysics Data System (ADS)

Del Frate, Fabio; Picchiani, Matteo; Falasco, Alessia; Schiavon, Giovanni

2016-10-01

The development of SAR technology during the last decade has made it possible to collect a huge amount of data over many regions of the world. In particular, the availability of SAR images from different sensors, with metric or sub-metric spatial resolution, offers novel opportunities in different fields as land cover, urban monitoring, soil consumption etc. On the other hand, automatic approaches become crucial for the exploitation of such a huge amount of information. In such a scenario, especially if single polarization images are considered, the main issue is to select appropriate contextual descriptors, since the backscattering coefficient of a single pixel may not be sufficient to classify an object on the scene. In this paper a comparison among three different approaches for contextual features definition is presented so as to design optimum procedures for VHR SAR scene understanding. The first approach is based on Gray Level Co- Occurrence Matrix since it is widely accepted and several studies have used it for land cover classification with SAR data. The second approach is based on the Fourier spectra and it has been already proposed with positive results for this kind of problems, the third one is based on Auto-associative Neural Networks which have been already proven effective for features extraction from polarimetric SAR images. The three methods are evaluated in terms of the accuracy of the classified scene when the features extracted using each method are considered as input to a neural network classificator and applied on different Cosmo-SkyMed spotlight products.

Combining TerraSAR-X and SPOT-5 data for object-based landslide detection

NASA Astrophysics Data System (ADS)

Friedl, B.; Hölbling, D.; Füreder, P.

2012-04-01

Landslide detection and classification is an essential requirement in pre- and post-disaster hazard analysis. In earlier studies landslide detection often was achieved through time-consuming and cost-intensive field surveys and visual orthophoto interpretation. Recent studies show that Earth Observation (EO) data offer new opportunities for fast, reliable and accurate landslide detection and classification, which may conduce to an effective landslide monitoring and landslide hazard management. To ensure the fast recognition and classification of landslides at a regional scale, a (semi-)automated object-based landslide detection approach is established for a study site situated in the Huaguoshan catchment, Southern Taiwan. The study site exhibits a high vulnerability to landslides and debris flows, which are predominantly typhoon-induced. Through the integration of optical satellite data (SPOT-5 with 2.5 m GSD), SAR (Synthetic Aperture Radar) data (TerraSAR-X Spotlight with 2.95 m GSD) and digital elevation information (DEM with 5 m GSD) including its derived products (e.g. slope, curvature, flow accumulation) landslides may be examined in a more efficient way as if relying on single data sources only. The combination of optical and SAR data in an object-based image analysis (OBIA) domain for landslide detection and classification has not been investigated so far, even if SAR imagery show valuable properties for landslide detection, which differ from optical data (e.g. high sensitivity to surface roughness and soil moisture). The main purpose of this study is to recognize and analyze existing landslides by applying object-based image analysis making use of eCognition software. OBIA provides a framework for examining features defined by spectral, spatial, textural, contextual as well as hierarchical properties. Objects are derived through image segmentation and serve as input for the classification process, which relies on transparent rulesets, representing knowledge. Through class modeling, an iterative process of segmentation and classification, objects can be addressed individually in a region-specific manner. The presented approach is marked by the comprehensive use of available data sets from various sources. This full integration of optical, SAR and DEM data conduces to the development of a robust method, which makes use of the most appropriate characteristics (e.g. spectral, textural, contextual) of each data set. The proposed method contributes to a more rapid and accurate landslide mapping in order to assist disaster and crisis management. Especially SAR data proves to be useful in the aftermath of an event, as radar sensors are mostly independent of illumination and weather conditions and therefore data is more likely to be available. The full data integration allows coming up with a robust approach for the detection and classification of landslides. However, more research is needed to make the best of the integration of SAR data in an object-based environment and for making the approach easier adaptable to different study sites and data.

High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

NASA Astrophysics Data System (ADS)

Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

2018-04-01

Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

Superpixel edges for boundary detection

DOEpatents

Moya, Mary M.; Koch, Mark W.

2016-07-12

Various embodiments presented herein relate to identifying one or more edges in a synthetic aperture radar (SAR) image comprising a plurality of superpixels. Superpixels sharing an edge (or boundary) can be identified and one or more properties of the shared superpixels can be compared to determine whether the superpixels form the same or two different features. Where the superpixels form the same feature the edge is identified as an internal edge. Where the superpixels form two different features, the edge is identified as an external edge. Based upon classification of the superpixels, the external edge can be further determined to form part of a roof, wall, etc. The superpixels can be formed from a speckle-reduced SAR image product formed from a registered stack of SAR images, which is further segmented into a plurality of superpixels. The edge identification process is applied to the SAR image comprising the superpixels and edges.

The artificial object detection and current velocity measurement using SAR ocean surface images

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Strotov, Valery; Ershov, Maksim; Muraviev, Vadim; Feldman, Alexander; Smirnov, Sergey

2017-10-01

Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image processing can be used to solve many problems arising in this field of research. This paper discusses some of them including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps: image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm consists of the following main steps: prescreening, land masking, image segmentation combined with parameter measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is concluded that the proposed approaches can be used in maritime applications.

Extraction of Coastlines with Fuzzy Approach Using SENTINEL-1 SAR Image

NASA Astrophysics Data System (ADS)

Demir, N.; Kaynarca, M.; Oy, S.

2016-06-01

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

Mapping and monitoring renewable resources with space SAR

NASA Technical Reports Server (NTRS)

Ulaby, F. T.; Brisco, B.; Dobson, M. C.; Moezzi, S.

1983-01-01

The SEASAT-A SAR and SIR-A imagery was examined to evaluate the quality and type of information that can be extracted and used to monitor renewable resources on Earth. Two tasks were carried out: (1) a land cover classification study which utilized two sets of imagery acquired by the SEASAT-A SAR, one set by SIR-A, and one LANDSAT set (4 bands); and (2) a change detection to examine differences between pairs of SEASAT-A SAR images and relates them to hydrologic and/or agronomic variations in the scene.

Skipping the real world: Classification of PolSAR images without explicit feature extraction

NASA Astrophysics Data System (ADS)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

Extracting built-up areas from TerraSAR-X data using object-oriented classification method

NASA Astrophysics Data System (ADS)

Wang, SuYun; Sun, Z. C.

2017-02-01

Based on single-polarized TerraSAR-X, the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region-growing algorithm which takes the intensity of backscatter and shape-related properties into account. The object-oriented procedure consists of three main steps: firstly, the analysis of the local speckle behavior in the SAR intensity data, leading to the generation of a texture image; secondly, a segmentation based on the intensity image; thirdly, the classification of each segment using the derived texture file and intensity information in order to identify and extract build-up areas. In our research, the distribution of BAs in Dongying City is derived from single-polarized TSX SM image (acquired on 17th June 2013) with average ground resolution of 3m using our proposed approach. By cross-validating the random selected validation points with geo-referenced field sites, Quick Bird high-resolution imagery, confusion matrices with statistical indicators are calculated and used for assessing the classification results. The results demonstrate that an overall accuracy 92.89 and a kappa coefficient of 0.85 could be achieved. We have shown that connect texture information with the analysis of the local speckle divergence, combining texture and intensity of construction extraction is feasible, efficient and rapid.

An Adaptive Ship Detection Algorithm for Hrws SAR Images Under Complex Background: Application to SENTINEL1A Data

NASA Astrophysics Data System (ADS)

He, G.; Xia, Z.; Chen, H.; Li, K.; Zhao, Z.; Guo, Y.; Feng, P.

2018-04-01

Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Evaluating suitability of Pol-SAR (TerraSAR-X, Radarsat-2) for automated sea ice classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-05-01

Satellite borne SAR imagery has become an invaluable tool in the field of sea ice monitoring. Previously, single polarimetric imagery were employed in supervised and unsupervised classification schemes for sea ice investigation, which was preceded by image processing techniques such as segmentation and textural features. Recently, through the advent of polarimetric SAR sensors, investigation of polarimetric features in sea ice has attracted increased attention. While dual-polarimetric data has already been investigated in a number of works, full-polarimetric data has so far not been a major scientific focus. To explore the possibilities of full-polarimetric data and compare the differences in C- and X-bands, we endeavor to analyze in detail an array of datasets, simultaneously acquired, in C-band (RADARSAT-2) and X-band (TerraSAR-X) over ice infested areas. First, we propose an array of polarimetric features (Pauli and lexicographic based). Ancillary data from national ice services, SMOS data and expert judgement were utilized to identify the governing ice regimes. Based on these observations, we then extracted mentioned features. The subsequent supervised classification approach was based on an Artificial Neural Network (ANN). To gain quantitative insight into the quality of the features themselves (and reduce a possible impact of the Hughes phenomenon), we employed mutual information to unearth the relevance and redundancy of features. The results of this information theoretic analysis guided a pruning process regarding the optimal subset of features. In the last step we compared the classified results of all sensors and images, stated respective accuracies and discussed output discrepancies in the cases of simultaneous acquisitions.

Change detection of polarimetric SAR images based on the KummerU Distribution

NASA Astrophysics Data System (ADS)

Chen, Quan; Zou, Pengfei; Li, Zhen; Zhang, Ping

2014-11-01

In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region, NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend segmentation method can detect the change of watershed effectively.

Evaluation of RISAT-1 SAR data for tropical forestry applications

NASA Astrophysics Data System (ADS)

Padalia, Hitendra; Yadav, Sadhana

2017-01-01

India launched C band (5.35 GHz) RISAT-1 (Radar Imaging Satellite-1) on 26th April, 2012, equipped with the capability to image the Earth at multiple-resolutions and -polarizations. In this study the potential of Fine Resolution Strip (FRS) modes of RISAT-1 was evaluated for characterization and classification forests and estimation of biomass of early growth stages. The study was carried out at the two sites located in the foothills of western Himalaya, India. The pre-processing and classification of FRS-1 SAR data was performed using PolSAR Pro ver. 5.0 software. The scattering mechanisms derived from m-chi decomposition of FRS-1 RH/RV data were found physically meaningful for the characterization of various surface features types. The forest and land use type classification of the study area was developed applying Support Vector Machine (SVM) algorithm on FRS-1 derived appropriate polarimetric features. The biomass of early growth stages of Eucalyptus (up to 60 ton/ha) was estimated developing a multi-linear regression model using C band σ0 HV and σ0 HH backscatter information. The study outcomes has promise for wider application of RISAT-1 data for forest cover monitoring, especially for the tropical regions.

SAR image dataset of military ground targets with multiple poses for ATR

NASA Astrophysics Data System (ADS)

Belloni, Carole; Balleri, Alessio; Aouf, Nabil; Merlet, Thomas; Le Caillec, Jean-Marc

2017-10-01

Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability. Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset.1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden,2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques are currently challenging due to the lack of publicly available data in the SAR domain. In this paper, we present a high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm.3 A total of 1480 images are produced using a 20° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithms.

Alaska Synthetic Aperture Radar (SAR) Facility science data processing architecture

NASA Technical Reports Server (NTRS)

Hilland, Jeffrey E.; Bicknell, Thomas; Miller, Carol L.

1991-01-01

The paper describes the architecture of the Alaska SAR Facility (ASF) at Fairbanks, being developed to generate science data products for supporting research in sea ice motion, ice classification, sea-ice-ocean interaction, glacier behavior, ocean waves, and hydrological and geological study areas. Special attention is given to the individual substructures of the ASF: the Receiving Ground Station (RGS), the SAR Processor System, and the Interactive Image Analysis System. The SAR data will be linked to the RGS by the ESA ERS-1 and ERS-2, the Japanese ERS-1, and the Canadian Radarsat.

The Effect of Sub-Aperture in DRIA Framework Applied on Multi-Aspect PolSAR Data

NASA Astrophysics Data System (ADS)

Xue, Feiteng; Yin, Qiang; Lin, Yun; Hong, Wen

2016-08-01

Multi-aspect SAR is a new remote sensing technology, achieves consecutive data in large look angle as platform moves. Multi- aspect observation brings higher resolution and SNR to SAR picture. Multi-aspect PolSAR data can increase the accuracy of target identify and classification because it contains the 3-D polarimetric scattering properties.DRIA(detecting-removing-incoherent-adding)framework is a multi-aspect PolSAR data processing method. In this method, the anisotropic and isotropic scattering is separated by maximum- likelihood ratio test. The anisotropic scattering is removed to gain a removal series. The isotropic scattering is incoherent added to gain a high resolution picture. The removal series describes the anisotropic scattering property and is used in features extraction and classification.This article focuses on the effect brought by difference of sub-aperture numbers in anisotropic scattering detection and removal. The more sub-apertures are, the less look angle is. Artificial target has anisotropic scattering because of Bragg resonances. The increase of sub-aperture number brings more accurate observation in azimuth though the quality of each single image may loss. The accuracy of classification in agricultural fields is affected by the anisotropic scattering brought by Bragg resonances. The size of the sub-aperture has a significant effect in the removal result of Bragg resonances.

Space Radar Image of Manaus, Brazil

NASA Technical Reports Server (NTRS)

1999-01-01

These two images were created using data from the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). On the left is a false-color image of Manaus, Brazil acquired April 12, 1994, onboard space shuttle Endeavour. In the center of this image is the Solimoes River just west of Manaus before it combines with the Rio Negro to form the Amazon River. The scene is around 8 by 8 kilometers (5 by 5 miles) with north toward the top. The radar image was produced in L-band where red areas correspond to high backscatter at HH polarization, while green areas exhibit high backscatter at HV polarization. Blue areas show low backscatter at VV polarization. The image on the right is a classification map showing the extent of flooding beneath the forest canopy. The classification map was developed by SIR-C/X-SAR science team members at the University of California,Santa Barbara. The map uses the L-HH, L-HV, and L-VV images to classify the radar image into six categories: Red flooded forest Green unflooded tropical rain forest Blue open water, Amazon river Yellow unflooded fields, some floating grasses Gray flooded shrubs Black floating and flooded grasses Data like these help scientists evaluate flood damage on a global scale. Floods are highly episodic and much of the area inundated is often tree-covered. Spaceborne Imaging Radar-C and X-Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI), with the Deutsche Forschungsanstalt fuer Luft und Raumfahrt e.v. (DLR), the major partner in science, operations and data processing of X-SAR.

Flood Extent Mapping for Namibia Using Change Detection and Thresholding with SAR

NASA Technical Reports Server (NTRS)

Long, Stephanie; Fatoyinbo, Temilola E.; Policelli, Frederick

2014-01-01

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically <0.5 % of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.

Operational algorithm for ice-water classification on dual-polarized RADARSAT-2 images

NASA Astrophysics Data System (ADS)

Zakhvatkina, Natalia; Korosov, Anton; Muckenhuber, Stefan; Sandven, Stein; Babiker, Mohamed

2017-01-01

Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

SAR Imaging through the Earth’s Ionosphere

DTIC Science & Technology

2013-11-06

or, otherwise, if polarimetry puts too high of a demand on storage, downlink capacity, etc.), then the approach based on the Faraday rotation is not...reflected fields for both are the same in the first Born approximation. Modern practical applications of radar polarimetry employ different empirical and...Fruneau, and J.-P. Rudant. Classification of tropical vegetation using multifrequency partial SAR polarimetry . IEEE Geoscience and Remote Sensing Letters

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

USGS Publications Warehouse

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

1998-01-01

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

SAR Image Simulation of Ship Targets Based on Multi-Path Scattering

NASA Astrophysics Data System (ADS)

Guo, Y.; Wang, H.; Ma, H.; Li, K.; Xia, Z.; Hao, Y.; Guo, H.; Shi, H.; Liao, X.; Yue, H.

2018-04-01

Synthetic Aperture Radar (SAR) plays an important role in the classification and recognition of ship targets because of its all-weather working ability and fine resolution. In SAR images, besides the sea clutter, the influence of the sea surface on the radar echo is also known as the so-called multipath effect. These multipath effects will generate some extra "pseudo images", which may cause the distortion of the target image and affect the estimation of the characteristic parameters. In this paper,the multipath effect of rough sea surface and its influence on the estimation of ship characteristic parameters are studied. The imaging of the first and the secondary reflection of sea surface is presented . The artifacts not only overlap with the image of the target itself, but may also appear in the sea near the target area. It is difficult to distinguish them, and this artifact has an effect on the length and width of the ship.

Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Xu, Ru

2018-02-01

Integrating synthetic aperture radar (SAR) and optical data to improve urban land cover classification has been identified as a promising approach. However, which integration level is the most suitable remains unclear but important to many researchers and engineers. This study aimed to compare different integration levels for providing a scientific reference for a wide range of studies using optical and SAR data. SAR data from TerraSAR-X and ENVISAT ASAR in both WSM and IMP modes were used to be combined with optical data at pixel level, feature level and decision levels using four typical machine learning methods. The experimental results indicated that: 1) feature level that used both the original images and extracted features achieved a significant improvement of up to 10% compared to that using optical data alone; 2) different levels of fusion required different suitable methods depending on the data distribution and data resolution. For instance, support vector machine was the most stable at both the feature and decision levels, while random forest was suitable at the pixel level but not suitable at the decision level. 3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels. This indicated the benefits of using texture features from SAR data when being combined with optical data for land cover classification. Additionally, the research also shown that combining optical and SAR data does not guarantee improvement compared with using single data source for urban land cover classification, depending on the selection of appropriate fusion levels and fusion methods.

Pixel-based flood mapping from SAR imagery: a comparison of approaches

NASA Astrophysics Data System (ADS)

Landuyt, Lisa; Van Wesemael, Alexandra; Van Coillie, Frieke M. B.; Verhoest, Niko E. C.

2017-04-01

Due to their all-weather, day and night capabilities, SAR sensors have been shown to be particularly suitable for flood mapping applications. Thus, they can provide spatially-distributed flood extent data which are valuable for calibrating, validating and updating flood inundation models. These models are an invaluable tool for water managers, to take appropriate measures in times of high water levels. Image analysis approaches to delineate flood extent on SAR imagery are numerous. They can be classified into two categories, i.e. pixel-based and object-based approaches. Pixel-based approaches, e.g. thresholding, are abundant and in general computationally inexpensive. However, large discrepancies between these techniques exist and often subjective user intervention is needed. Object-based approaches require more processing but allow for the integration of additional object characteristics, like contextual information and object geometry, and thus have significant potential to provide an improved classification result. As means of benchmark, a selection of pixel-based techniques is applied on a ERS-2 SAR image of the 2006 flood event of River Dee, United Kingdom. This selection comprises Otsu thresholding, Kittler & Illingworth thresholding, the Fine To Coarse segmentation algorithm and active contour modelling. The different classification results are evaluated and compared by means of several accuracy measures, including binary performance measures.

Classification of earth terrain using polarimetric synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.

1989-01-01

Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

Capability of geometric features to classify ships in SAR imagery

NASA Astrophysics Data System (ADS)

Lang, Haitao; Wu, Siwen; Lai, Quan; Ma, Li

2016-10-01

Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f1), width (f2), area (f3), perimeter (f4), elongatedness (f5) and compactness (f6). Among them, two basic features, length (f1) and width (f2), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.

Bayes classification of interferometric TOPSAR data

NASA Technical Reports Server (NTRS)

Michel, T. R.; Rodriguez, E.; Houshmand, B.; Carande, R.

1995-01-01

We report the Bayes classification of terrain types at different sites using airborne interferometric synthetic aperture radar (INSAR) data. A Gaussian maximum likelihood classifier was applied on multidimensional observations derived from the SAR intensity, the terrain elevation model, and the magnitude of the interferometric correlation. Training sets for forested, urban, agricultural, or bare areas were obtained either by selecting samples with known ground truth, or by k-means clustering of random sets of samples uniformly distributed across all sites, and subsequent assignments of these clusters using ground truth. The accuracy of the classifier was used to optimize the discriminating efficiency of the set of features that was chosen. The most important features include the SAR intensity, a canopy penetration depth model, and the terrain slope. We demonstrate the classifier's performance across sites using a unique set of training classes for the four main terrain categories. The scenes examined include San Francisco (CA) (predominantly urban and water), Mount Adams (WA) (forested with clear cuts), Pasadena (CA) (urban with mountains), and Antioch Hills (CA) (water, swamps, fields). Issues related to the effects of image calibration and the robustness of the classification to calibration errors are explored. The relative performance of single polarization Interferometric data classification is contrasted against classification schemes based on polarimetric SAR data.

Terrain detection and classification using single polarization SAR

DOEpatents

Chow, James G.; Koch, Mark W.

2016-01-19

The various technologies presented herein relate to identifying manmade and/or natural features in a radar image. Two radar images (e.g., single polarization SAR images) can be captured for a common scene. The first image is captured at a first instance and the second image is captured at a second instance, whereby the duration between the captures are of sufficient time such that temporal decorrelation occurs for natural surfaces in the scene, and only manmade surfaces, e.g., a road, produce correlated pixels. A LCCD image comprising the correlated and decorrelated pixels can be generated from the two radar images. A median image can be generated from a plurality of radar images, whereby any features in the median image can be identified. A superpixel operation can be performed on the LCCD image and the median image, thereby enabling a feature(s) in the LCCD image to be classified.

Terrain feature recognition for synthetic aperture radar (SAR) imagery employing spatial attributes of targets

NASA Astrophysics Data System (ADS)

Iisaka, Joji; Sakurai-Amano, Takako

1994-08-01

This paper describes an integrated approach to terrain feature detection and several methods to estimate spatial information from SAR (synthetic aperture radar) imagery. Spatial information of image features as well as spatial association are key elements in terrain feature detection. After applying a small feature preserving despeckling operation, spatial information such as edginess, texture (smoothness), region-likeliness and line-likeness of objects, target sizes, and target shapes were estimated. Then a trapezoid shape fuzzy membership function was assigned to each spatial feature attribute. Fuzzy classification logic was employed to detect terrain features. Terrain features such as urban areas, mountain ridges, lakes and other water bodies as well as vegetated areas were successfully identified from a sub-image of a JERS-1 SAR image. In the course of shape analysis, a quantitative method was developed to classify spatial patterns by expanding a spatial pattern through the use of a series of pattern primitives.

Classification Accuracy Increase Using Multisensor Data Fusion

NASA Astrophysics Data System (ADS)

Makarau, A.; Palubinskas, G.; Reinartz, P.

2011-09-01

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc.

Analysis of data acquired by synthetic aperture radar and LANDSAT Multispectral Scanner over Kershaw County, South Carolina, during the summer season

NASA Technical Reports Server (NTRS)

Wu, S. T.

1983-01-01

Data acquired by synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) were processed and analyzed to derive forest-related resources inventory information. The SAR data were acquired by using the NASA aircraft X-band SAR with linear (HH, VV) and cross (HV, VH) polarizations and the SEASAT L-band SAR. After data processing and data quality examination, the three polarization (HH, HV, and VV) data from the aircraft X-band SAR were used in conjunction with LANDSAT MSS for multisensor data classification. The results of accuracy evaluation for the SAR, MSS and SAR/MSS data using supervised classification show that the SAR-only data set contains low classification accuracy for several land cover classes. However, the SAR/MSS data show that significant improvement in classification accuracy is obtained for all eight land cover classes. These results suggest the usefulness of using combined SAR/MSS data for forest-related cover mapping. The SAR data also detect several small special surface features that are not detectable by MSS data.

Neural networks for oil spill detection using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Avezzano, Ruggero G.; Velotto, Domenico; Soccorsi, Matteo; Del Frate, Fabio; Lehner, Susanne

2011-11-01

The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes (e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory with an overall detection accuracy above 80%.

A new clustering algorithm applicable to multispectral and polarimetric SAR images

NASA Technical Reports Server (NTRS)

Wong, Yiu-Fai; Posner, Edward C.

1993-01-01

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU; Shin, Robert T.; Nghiem, Son V.; Yueh, Herng-Aung; Han, Hsiu C.; Lim, Harold H.; Arnold, David V.

1990-01-01

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M.; Dean, M. E.; Knowlton, D. J.; Latty, R. S.

1982-01-01

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Sea slicks classification by synthetic aperture radar

NASA Astrophysics Data System (ADS)

Trivero, P.; Biamino, W.; Borasi, M.; Cavagnero, M.; Di Matteo, L.; Loreggia, D.

2014-10-01

An automatic system called OSAD (Oil Spill Automatic Detector), able to discriminate oil spills (OS) from similar features (look-alikes - LA) in SAR images, was developed some years ago. Slick detection is based on a probabilistic method (tuned with a training dataset defined by an expert photointerpreter) evaluating radiometric and geometric characteristics of the areas of interest. OSAD also provides wind field by analyzing SAR images. With the aim to completely classify sea slicks, recently a new procedure has been added. Dark areas are identified on the image and the wind is computed inside and outside for every area: if outside wind value is less than a threshold of 2 m/s it is impossible to evaluate if damping is due to a slick. On the other hand, if outside wind is higher than the threshold and the difference between inside and outside the dark area is lower than 1 m/s we consider this reduction as wind fluctuation. Wind difference higher than 1 m/s is interpreted as damping effect due to a slick; therefore the remaining dark spots are split in OS and LA by OSAD. LA are then analyzed and separated in "biogenic" or "anthropogenic" slicks following an analogous procedure. The system performances has been tested on C-band SAR images, in particular on images having spatial resolution so high to examine details near the coastline; the obtained results confirm the efficiency of the algorithm in the classification of four types of signatures usually found on the sea surface.

Moving Forward with Enhancements for Our Customers

DTIC Science & Technology

2008-11-18

The original document contains color images. 14. ABSTRACT 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18...Materials and Testing Alion Science & Technology CBRNIAC Chemical, Biological Radiological, Nuclear Battelle Memorial CPIAC Chemical Propulsion Johns

Identification of sea ice types in spaceborne synthetic aperture radar data

NASA Technical Reports Server (NTRS)

Kwok, Ronald; Rignot, Eric; Holt, Benjamin; Onstott, R.

1992-01-01

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image

NASA Astrophysics Data System (ADS)

Wang, Xiaochen; Shao, Yun; Tian, Wei; Li, Kun

2018-06-01

This study explored different methodologies using a C-band RADARSAT-2 quad-polarized Synthetic Aperture Radar (SAR) image located over China's Yellow Sea to investigate polarization decomposition parameters for identifying mixed floating pollutants from a complex ocean background. It was found that solitary polarization decomposition did not meet the demand for detecting and classifying multiple floating pollutants, even after applying a polarized SAR image. Furthermore, considering that Yamaguchi decomposition is sensitive to vegetation and the algal variety Enteromorpha prolifera, while H/A/alpha decomposition is sensitive to oil spills, a combination of parameters which was deduced from these two decompositions was proposed for marine environmental monitoring of mixed floating sea surface pollutants. A combination of volume scattering, surface scattering, and scattering entropy was the best indicator for classifying mixed floating pollutants from a complex ocean background. The Kappa coefficients for Enteromorpha prolifera and oil spills were 0.7514 and 0.8470, respectively, evidence that the composite polarized parameters based on quad-polarized SAR imagery proposed in this research is an effective monitoring method for complex marine pollution.

Burnt area mapping from ERS-SAR time series using the principal components transformation

NASA Astrophysics Data System (ADS)

Gimeno, Meritxell; San-Miguel Ayanz, Jesus; Barbosa, Paulo M.; Schmuck, Guido

2003-03-01

Each year thousands of hectares of forest burnt across Southern Europe. To date, remote sensing assessments of this phenomenon have focused on the use of optical satellite imagery. However, the presence of clouds and smoke prevents the acquisition of this type of data in some areas. It is possible to overcome this problem by using synthetic aperture radar (SAR) data. Principal component analysis (PCA) was performed to quantify differences between pre- and post- fire images and to investigate the separability over a European Remote Sensing (ERS) SAR time series. Moreover, the transformation was carried out to determine the best conditions to acquire optimal SAR imagery according to meteorological parameters and the procedures to enhance burnt area discrimination for the identification of fire damage assessment. A comparative neural network classification was performed in order to map and to assess the burnts using a complete ERS time series or just an image before and an image after the fire according to the PCA. The results suggest that ERS is suitable to highlight areas of localized changes associated with forest fire damage in Mediterranean landcover.

A new scheme for urban impervious surface classification from SAR images

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Lin, Hui; Wang, Yunpeng

2018-05-01

Urban impervious surfaces have been recognized as a significant indicator for various environmental and socio-economic studies. There is an increasingly urgent demand for timely and accurate monitoring of the impervious surfaces with satellite technology from local to global scales. In the past decades, optical remote sensing has been widely employed for this task with various techniques. However, there are still a range of challenges, e.g. handling cloud contamination on optical data. Therefore, the Synthetic Aperture Radar (SAR) was introduced for the challenging task because it is uniquely all-time- and all-weather-capable. Nevertheless, with an increasing number of SAR data applied, the methodology used for impervious surfaces classification remains unchanged from the methods used for optical datasets. This shortcoming has prevented the community from fully exploring the potential of using SAR data for impervious surfaces classification. We proposed a new scheme that is comparable to the well-known and fundamental Vegetation-Impervious surface-Soil (V-I-S) model for mapping urban impervious surfaces. Three scenes of fully polarimetric Radsarsat-2 data for the cities of Shenzhen, Hong Kong and Macau were employed to test and validate the proposed methodology. Experimental results indicated that the overall accuracy and Kappa coefficient were 96.00% and 0.8808 in Shenzhen, 93.87% and 0.8307 in Hong Kong and 97.48% and 0.9354 in Macau, indicating the applicability and great potential of the new scheme for impervious surfaces classification using polarimetric SAR data. Comparison with the traditional scheme indicated that this new scheme was able to improve the overall accuracy by up to 4.6% and Kappa coefficient by up to 0.18.

Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

NASA Astrophysics Data System (ADS)

Mahdianpari, Masoud; Salehi, Bahram; Mohammadimanesh, Fariba; Motagh, Mahdi

2017-08-01

Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant species. In this study, a novel hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a sub-region located in the north eastern portion of the Avalon Peninsula. Particularly, multi-polarization and multi-frequency SAR data, including X-band TerraSAR-X single polarized (HH), L-band ALOS-2 dual polarized (HH/HV), and C-band RADARSAT-2 fully polarized images, were applied in different classification levels. First, a SAR backscatter analysis of different land cover types was performed by training data and used in Level-I classification to separate water from non-water classes. This was followed by Level-II classification, wherein the water class was further divided into shallow- and deep-water classes, and the non-water class was partitioned into herbaceous and non-herbaceous classes. In Level-III classification, the herbaceous class was further divided into bog, fen, and marsh classes, while the non-herbaceous class was subsequently partitioned into urban, upland, and swamp classes. In Level-II and -III classifications, different polarimetric decomposition approaches, including Cloude-Pottier, Freeman-Durden, Yamaguchi decompositions, and Kennaugh matrix elements were extracted to aid the RF classifier. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. The importance of input features was also determined using the variable importance obtained by RF. It was found that the Kennaugh matrix elements, Yamaguchi, and Freeman-Durden decompositions were the most important parameters for wetland classification in this study. Using this new hierarchical RF classification approach, an overall accuracy of up to 94% was obtained for classifying different land cover types in the study area.

Design of integrated ship monitoring system using SAR, RADAR, and AIS

NASA Astrophysics Data System (ADS)

Yang, Chan-Su; Kim, Tae-Ho; Hong, Danbee; Ahn, Hyung-Wook

2013-06-01

When we talk about for the ship detection, identification and its classification, we need to go for the wide area of monitoring and it may be possible only through satellite based monitoring approach which monitors and covers coastal as well as the oceanic zone. Synthetic aperture radar (SAR) has been widely used to detect targets of interest with the advantage of the operating capability in all weather and luminance free condition (Margarit and Tabasco, 2011). In EU waters, EMSA(European Maritime Safety Agency) is operating the SafeSeaNet and CleanSeaNet systems which provide the current positions of all ships and oil spill monitoring information in and around EU waters in a single picture to Member States using AIS, LRIT and SAR images. In many countries, a similar system has been developed and the key of the matter is to integrate all available data. This abstract describes the preliminary design concept for an integration system of RADAR, AIS and SAR data for vessel traffic monitoring. SAR sensors are used to acquire image data over large coverage area either through the space borne or airborne platforms in UTC. AIS reports should be also obtained on the same date as of the SAR acquisition for the purpose to perform integration test. Land-based RADAR can provide ships positions detected and tracked in near real time. In general, SAR are used to acquire image data over large coverage area, AIS reports are obtained from ship based transmitter, and RADAR can monitor continuously ships for a limited area. In this study, we developed individual ship monitoring algorithms using RADAR(FMCW and Pulse X-band), AIS and SAR(RADARSAT-2 Full-pol Mode). We conducted field experiments two times for displaying the RADAR, AIS and SAR integration over the Pyeongtaek Port, South Korea.

Integration of SAR and AIS for ship detection and identification

NASA Astrophysics Data System (ADS)

Yang, Chan-Su; Kim, Tae-Ho

2012-06-01

This abstract describes the preliminary design concept for an integration system of SAR and AIS data. SAR sensors are used to acquire image data over large coverage area either through the space borne or airborne platforms in UTC. AIS reports should also obtained on the same date as of the SAR acquisition for the purpose to perform integration test. Once both data reports are obtained, one need to match the timings of AIS data acquisition over the SAR image acquisition time with consideration of local time & boundary to extract the closest time signal from AIS report in order to know the AIS based ship positions, but still one cannot be able to distinguish which ships have the AIS transponder after projection of AIS based position onto the SAR image acquisition boundary. As far as integration is concerned, the ship dead-reckoning concept is most important forecasted position which provides the AIS based ship position at the time of SAR image acquisition and also provides the hints for azimuth shift which occurred in SAR image for the case of moving ships which moves in the direction perpendicular to the direction of flight path. Unknown ship's DR estimation is to be carried out based on the initial positions, speed and course over ground, which has already been shorted out from AIS reports, during the step of time matching. This DR based ship's position will be the candidate element for searching the SAR based ship targets for the purpose of identification & matching within the certain boundary around DR. The searching method is performed by means of estimation of minimum distance from ship's DR to SAR based ship position, and once it determines, so the candidate element will look for matching like ship size match of DR based ship's dimension wrt SAR based ship's edge, there may be some error during the matching with SAR based ship edges with actual ship's hull design as per the longitudinal and transverse axis size information obtained from the AIS reports due to blurring effect in SAR based ship signatures, once the conditions are satisfied, candidate element will move & shift over the SAR based ship signature target with the minimum displacement and it is known to be the azimuth shift compensation and this overall methodology are known to be integration of AIS report data over the SAR image acquisition boundary with assessment of time matching. The expected result may provide the good accuracy of the SAR and AIS contact position along with dimension and classification of ships over SAR image. There may be possibilities of matching speed and course from candidate element with SAR based ship signature, but still the challenges are presents in front of us that to estimation of speed and course by means of SAR data, if it may be possible so the expected final result may be more accurate as due to extra matching effects and the results may be used for the near real time performance for ship identification with help of integrated system design based on SAR and AIS data reports.

Ultrafast Graphene Photonics and Optoelectronics

DTIC Science & Technology

2017-04-14

SUBJECT TERMS Graphene, Ultrafast Optical Processin, Terahertz Electronics ; 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18...Rep, (2016)) Fig. 4. (a) Images of scanning electron microscope for 1D and 2D gratings. (b) Ratio of the real part of the transmitted field

Space Radar Image of Manaus, Brazil

NASA Image and Video Library

1999-01-27

These two images were created using data from the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). On the left is a false-color image of Manaus, Brazil acquired April 12, 1994, onboard space shuttle Endeavour. In the center of this image is the Solimoes River just west of Manaus before it combines with the Rio Negro to form the Amazon River. The scene is around 8 by 8 kilometers (5 by 5 miles) with north toward the top. The radar image was produced in L-band where red areas correspond to high backscatter at HH polarization, while green areas exhibit high backscatter at HV polarization. Blue areas show low backscatter at VV polarization. The image on the right is a classification map showing the extent of flooding beneath the forest canopy. The classification map was developed by SIR-C/X-SAR science team members at the University of California,Santa Barbara. The map uses the L-HH, L-HV, and L-VV images to classify the radar image into six categories: Red flooded forest Green unflooded tropical rain forest Blue open water, Amazon river Yellow unflooded fields, some floating grasses Gray flooded shrubs Black floating and flooded grasses Data like these help scientists evaluate flood damage on a global scale. Floods are highly episodic and much of the area inundated is often tree-covered. http://photojournal.jpl.nasa.gov/catalog/PIA01712

Quantifying sub-pixel urban impervious surface through fusion of optical and inSAR imagery

USGS Publications Warehouse

Yang, L.; Jiang, L.; Lin, H.; Liao, M.

2009-01-01

In this study, we explored the potential to improve urban impervious surface modeling and mapping with the synergistic use of optical and Interferometric Synthetic Aperture Radar (InSAR) imagery. We used a Classification and Regression Tree (CART)-based approach to test the feasibility and accuracy of quantifying Impervious Surface Percentage (ISP) using four spectral bands of SPOT 5 high-resolution geometric (HRG) imagery and three parameters derived from the European Remote Sensing (ERS)-2 Single Look Complex (SLC) SAR image pair. Validated by an independent ISP reference dataset derived from the 33 cm-resolution digital aerial photographs, results show that the addition of InSAR data reduced the ISP modeling error rate from 15.5% to 12.9% and increased the correlation coefficient from 0.71 to 0.77. Spatially, the improvement is especially noted in areas of vacant land and bare ground, which were incorrectly mapped as urban impervious surfaces when using the optical remote sensing data. In addition, the accuracy of ISP prediction using InSAR images alone is only marginally less than that obtained by using SPOT imagery. The finding indicates the potential of using InSAR data for frequent monitoring of urban settings located in cloud-prone areas.

Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

NASA Astrophysics Data System (ADS)

Máttyus, G.

2013-05-01

Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

SONARC: A Sea Ice Monitoring and Forecasting System to Support Safe Operations and Navigation in Arctic Seas

NASA Astrophysics Data System (ADS)

Stephenson, S. R.; Babiker, M.; Sandven, S.; Muckenhuber, S.; Korosov, A.; Bobylev, L.; Vesman, A.; Mushta, A.; Demchev, D.; Volkov, V.; Smirnov, K.; Hamre, T.

2015-12-01

Sea ice monitoring and forecasting systems are important tools for minimizing accident risk and environmental impacts of Arctic maritime operations. Satellite data such as synthetic aperture radar (SAR), combined with atmosphere-ice-ocean forecasting models, navigation models and automatic identification system (AIS) transponder data from ships are essential components of such systems. Here we present first results from the SONARC project (project term: 2015-2017), an international multidisciplinary effort to develop novel and complementary ice monitoring and forecasting systems for vessels and offshore platforms in the Arctic. Automated classification methods (Zakhvatkina et al., 2012) are applied to Sentinel-1 dual-polarization SAR images from the Barents and Kara Sea region to identify ice types (e.g. multi-year ice, level first-year ice, deformed first-year ice, new/young ice, open water) and ridges. Short-term (1-3 days) ice drift forecasts are computed from SAR images using feature tracking and pattern tracking methods (Berg & Eriksson, 2014). Ice classification and drift forecast products are combined with ship positions based on AIS data from a selected period of 3-4 weeks to determine optimal vessel speed and routing in ice. Results illustrate the potential of high-resolution SAR data for near-real-time monitoring and forecasting of Arctic ice conditions. Over the next 3 years, SONARC findings will contribute new knowledge about sea ice in the Arctic while promoting safe and cost-effective shipping, domain awareness, resource management, and environmental protection.

Vegetation canopy discrimination and biomass assessment using multipolarized airborne SAR

NASA Technical Reports Server (NTRS)

Ulaby, F. T.; Dobson, M. C.; Held, D. N.

1985-01-01

Multipolarized airborne Synthetic Aperture Radar (SAR) data were acquired over a largely agricultural test site near Macomb, Illinois, in conjunction with the Shuttle Imaging Radar (SIR-B) experiment in October 1984. The NASA/JPL L-band SAR operating at 1.225 GHz made a series of daily overflights with azimuth view angles both parallel and orthogonal to those of SIR-B. The SAR data was digitally recorded in the quadpolarization configuration. An extensive set of ground measurements were obtained throughout the test site and include biophysical and soil measurements of approximately 400 agricultural fields. Preliminary evaluation of some of the airborne SAR imagery indicates a great potential for crop discrimination and assessment of canopy condition. False color composites constructed from the combination of three linear polarizations (HH, VV, and HV) were found to be clearly superior to any single polarization for purposes of crop classification. In addition, an image constructed using the HH return to modulate intensity and the phase difference between HH and VV returns to modulate chroma indicates a clear capability for assessment of canopy height and/or biomass. In particular, corn fields heavily damaged by infestations of corn borer are readily distinguished from noninfested fields.

Synthetic Aperture Radar (SAR)-based paddy rice monitoring system: Development and application in key rice producing areas in Tropical Asia

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Quicho, E.; Collivignarelli, F.; Maunahan, A.; Gatti, L.; Romuga, G. C.

2017-01-01

Reliable and regular rice information is essential part of many countries’ national accounting process but the existing system may not be sufficient to meet the information demand in the context of food security and policy. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland paddy rice, especially in tropical region where pervasive cloud cover in the rainy seasons limits the use of optical imagery. This study uses multi-temporal X-band and C-band SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations across Tropical Asia and assimilate the information into ORYZA Crop Growth Simulation model (CGSM) to generate high resolution yield maps. The resulting cultivated rice area maps had classification accuracies above 85% and yield estimates were within 81-93% agreement against district level reported yields. The study sites capture much of the diversity in water management, crop establishment and rice maturity durations and the study demonstrates the feasibility of rice detection, yield monitoring, and damage assessment in case of climate disaster at national and supra-national scales using multi-temporal SAR imagery combined with CGSM and automated methods.

Oil Spill Detection: Past and Future Trends

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Singha, Suman

2016-08-01

In the last 15 years, the detection of oil spills by satellite means has been moved from experimental to operational. Actually, what is really changed is the satellite image availability. From the late 1990's, in the age of "no data" we have moved forward 15 years to the age of "Sentinels" with an abundance of data. Either large accident related to offshore oil exploration and production activity or illegal discharges from tankers, oil on the sea surface is or can be now regularly monitored, over European Waters. National and transnational organizations (i.e. European Maritime Safety Agency's 'CleanSeaNet' Service) are routinely using SAR imagery to detect oil due to it's all weather, day and night imaging capability. However, all these years the scientific methodology on the detection remains relatively constant. From manual analysis to fully automatic detection methodologies, no significant contribution has been published in the last years and certainly none has dramatically changed the rules of the detection. On the contrary, although the overall accuracy of the methodology is questioned, the four main classification steps (dark area detection, features extraction, statistic database creation, and classification) are continuously improving. In recent years, researchers came up with the use of polarimetric SAR data for oil spill detection and characterizations, although utilization of Pol-SAR data for this purpose still remains questionable due to lack of verified dataset and low spatial coverage of Pol-SAR data. The present paper is trying to point out the drawbacks of the oil spill detection in the last years and focus on the bottlenecks of the oil spill detection methodologies. Also, solutions on the basis of data availability, management and analysis are proposed. Moreover, an ideal detection system is discussed regarding satellite image and in situ observations using different scales and sensors.

Integrated Shoreline Extraction Approach with Use of Rasat MS and SENTINEL-1A SAR Images

NASA Astrophysics Data System (ADS)

Demir, N.; Oy, S.; Erdem, F.; Şeker, D. Z.; Bayram, B.

2017-09-01

Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

Phase History Decomposition for efficient Scatterer Classification in SAR Imagery

DTIC Science & Technology

2011-09-15

frequency. Professor Rick Martin provided key advice on frequency parameter estimation and the relationship between likelihood ratio testing and the least...132 6.1.1 Imaging Error Due to Interpolation . . . . . . . . . . . . . . . . . . . . . . . . 135 6.2 Subwindow Design and Weighting... test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 MF matched filter

Unsupervised Wishart Classfication of Wetlands in Newfoundland, Canada Using Polsar Data Based on Fisher Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Homayouni, S.

2016-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

Utilizing feedback in adaptive SAR ATR systems

NASA Astrophysics Data System (ADS)

Horsfield, Owen; Blacknell, David

2009-05-01

Existing SAR ATR systems are usually trained off-line with samples of target imagery or CAD models, prior to conducting a mission. If the training data is not representative of mission conditions, then poor performance may result. In addition, it is difficult to acquire suitable training data for the many target types of interest. The Adaptive SAR ATR Problem Set (AdaptSAPS) program provides a MATLAB framework and image database for developing systems that adapt to mission conditions, meaning less reliance on accurate training data. A key function of an adaptive system is the ability to utilise truth feedback to improve performance, and it is this feature which AdaptSAPS is intended to exploit. This paper presents a new method for SAR ATR that does not use training data, based on supervised learning. This is achieved by using feature-based classification, and several new shadow features have been developed for this purpose. These features allow discrimination of vehicles from clutter, and classification of vehicles into two classes: targets, comprising military combat types, and non-targets, comprising bulldozers and trucks. The performance of the system is assessed using three baseline missions provided with AdaptSAPS, as well as three additional missions. All performance metrics indicate a distinct learning trend over the course of a mission, with most third and fourth quartile performance levels exceeding 85% correct classification. It has been demonstrated that these performance levels can be maintained even when truth feedback rates are reduced by up to 55% over the course of a mission.

Space Radar Image of Mammoth, California

NASA Technical Reports Server (NTRS)

1999-01-01

These two images were created using data from the Spaceborne Imaging Radar C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). The image on the left is a false-color composite of the Mammoth Mountain area in California's Sierra Nevada Mountains centered at 37.6 degrees north, 119.0 degrees west. It was acquired on-board the space shuttle Endeavour on its 67th orbit on April 13, 1994. In the image on the left, red is C-band HV-polarization, green is C-band HH-polarization and blue is the ratio of C-band VV-polarization to C-band HV-polarization. On the right is a classification map of the surface features which was developed by SIR-C/X-SAR science team members at the University of California, Santa Barbara. The area is about 23 by 46 kilometers (14 by 29 miles). In the classification image, the colors represent the following surfaces: White snow Red frozen lake, covered by snow Brown bare ground Blue lake (open water) Yellow short vegetation (mainly brush) Green sparse forest Dark green dense forest Maps like this one are helpful to scientists studying snow wetness and snow water equivalent in the snow pack. Across the globe, over major portions of the middle and high latitudes, and at high elevations in the tropical latitudes, snow and alpine glaciers are the largest contributors to run-off in rivers and to ground-water recharge. Snow hydrologists are using radar in an attempt to estimate both the quantity of water held by seasonal snow packs and the timing of snow melt. Snow and ice also play important roles in regional climates; understanding the processes in seasonal snow cover is also important for studies of the chemical balance of alpine drainage basins. SIR-C/X-SAR is a powerful tool because it is sensitive to most snow pack conditions and is less influenced by weather conditions than other remote sensing instruments, such as the Landsat satellite. Spaceborne Imaging Radar-C and X-Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI), with the Deutsche Forschungsanstalt fuer Luft und Raumfahrt e.v. (DLR), the major partner in science, operations and data processing of X-SAR.

Model-based approach to the detection and classification of mines in sidescan sonar.

PubMed

Reed, Scott; Petillot, Yvan; Bell, Judith

2004-01-10

This paper presents a model-based approach to mine detection and classification by use of sidescan sonar. Advances in autonomous underwater vehicle technology have increased the interest in automatic target recognition systems in an effort to automate a process that is currently carried out by a human operator. Current automated systems generally require training and thus produce poor results when the test data set is different from the training set. This has led to research into unsupervised systems, which are able to cope with the large variability in conditions and terrains seen in sidescan imagery. The system presented in this paper first detects possible minelike objects using a Markov random field model, which operates well on noisy images, such as sidescan, and allows a priori information to be included through the use of priors. The highlight and shadow regions of the object are then extracted with a cooperating statistical snake, which assumes these regions are statistically separate from the background. Finally, a classification decision is made using Dempster-Shafer theory, where the extracted features are compared with synthetic realizations generated with a sidescan sonar simulator model. Results for the entire process are shown on real sidescan sonar data. Similarities between the sidescan sonar and synthetic aperture radar (SAR) imaging processes ensure that the approach outlined here could be made applied to SAR image analysis.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

Agricultural Land Cover from Multitemporal C-Band SAR Data

NASA Astrophysics Data System (ADS)

Skriver, H.

2013-12-01

Henning Skriver DTU Space, Technical University of Denmark Ørsteds Plads, Building 348, DK-2800 Lyngby e-mail: hs@space.dtu.dk Problem description This paper focuses on land cover type from SAR data using high revisit acquisitions, including single and dual polarisation and fully polarimetric data, at C-band. The data set were acquired during an ESA-supported campaign, AgriSAR09, with the Radarsat-2 system. Ground surveys to obtain detailed land cover maps were performed during the campaign. Classification methods using single- and dual-polarisation data, and fully polarimetric data are used with multitemporal data with short revisit time. Results for airborne campaigns have previously been reported in Skriver et al. (2011) and Skriver (2012). In this paper, the short revisit satellite SAR data will be used to assess the trade-off between polarimetric SAR data and data as single or dual polarisation SAR data. This is particularly important in relation to the future GMES Sentinel-1 SAR satellites, where two satellites with a relatively wide swath will ensure a short revisit time globally. Questions dealt with are: which accuracy can we expect from a mission like the Sentinel-1, what is the improvement of using polarimetric SAR compared to single or dual polarisation SAR, and what is the optimum number of acquisitions needed. Methodology The data have sufficient number of looks for the Gaussian assumption to be valid for the backscatter coefficients for the individual polarizations. The classification method used for these data is therefore the standard Bayesian classification method for multivariate Gaussian statistics. For the full-polarimetric cases two classification methods have been applied, the standard ML Wishart classifier, and a method based on a reversible transform of the covariance matrix into backscatter intensities. The following pre-processing steps were performed on both data sets: The scattering matrix data in the form of SLC products were coregistered, converted to covariance matrix format and multilooked to a specific equivalent number of looks. Results The multitemporal data improve significantly the classification results, and single acquisition data cannot provide the necessary classification performance. The multitemporal data are especially important for the single and dual polarization data, but less important for the fully polarimetric data. The satellite data set produces realistic classification results based on about 2000 fields. The best classification results for the single-polarized mode provide classification errors in the mid-twenties. Using the dual-polarized mode reduces the classification error with about 5 percentage points, whereas the polarimetric mode reduces it with about 10 percentage points. These results show, that it will be possible to obtain reasonable results with relatively simple systems with short revisit time. This very important result shows that systems like the Sentinel-1 mission will be able to produce fairly good results for global land cover classification. References Skriver, H. et al., 2011, 'Crop Classification using Short-Revisit Multitemporal SAR Data', IEEE J. Sel. Topics in Appl. Earth Obs. Rem. Sens., vol. 4, pp. 423-431. Skriver, H., 2012, 'Crop classification by multitemporal C- and L-band single- and dual-polarization and fully polarimetric SAR', IEEE Trans. Geosc. Rem. Sens., vol. 50, pp. 2138-2149.

Evaluation of single-band snow-patch mapping using high-resolution microwave remote sensing: an application in the maritime Antarctic

NASA Astrophysics Data System (ADS)

Mora, Carla; Jiménez, Juan Javier; Pina, Pedro; Catalão, João; Vieira, Gonçalo

2017-01-01

The mountainous and ice-free terrains of the maritime Antarctic generate complex mosaics of snow patches, ranging from tens to hundreds of metres. These can only be accurately mapped using high-resolution remote sensing. In this paper we evaluate the application of radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping snow patches at a test area on Fildes Peninsula (King George Island, South Shetlands). Snow-patch mapping and characterization of snow stratigraphy were conducted at the time of image acquisition on 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting and with frequent ice layers in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results when discriminating between wet snow, lake water and bare soil. However, significant overlap in the backscattering signal was found. Average wet-snow backscattering was -18.0 dB in HH mode, with water showing -21.1 dB and bare soil showing -11.9 dB. Single-band pixel-based and object-oriented image classification methods were used to assess the classification potential of TerraSAR-X SpotLight imagery. The best results were obtained with an object-oriented approach using a watershed segmentation with a support vector machine (SVM) classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to north-west facing snow patches, which showed significant error, an issue related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in SpotLight mode provides high-quality imagery for mapping wet snow and snowmelt in the maritime Antarctic. The classification procedure that we propose is a simple method and a first step to an implementation in operational mode if a good digital elevation model is available.

Sea ice type dynamics in the Arctic based on Sentinel-1 Data

NASA Astrophysics Data System (ADS)

Babiker, Mohamed; Korosov, Anton; Park, Jeong-Won

2017-04-01

Sea ice observation from satellites has been carried out for more than four decades and is one of the most important applications of EO data in operational monitoring as well as in climate change studies. Several sensors and retrieval methods have been developed and successfully utilized to measure sea ice area, concentration, drift, type, thickness, etc [e.g. Breivik et al., 2009]. Today operational sea ice monitoring and analysis is fully dependent on use of satellite data. However, new and improved satellite systems, such as multi-polarisation Synthetic Apperture Radar (SAR), require further studies to develop more advanced and automated sea ice monitoring methods. In addition, the unprecedented volume of data available from recently launched Sentinel missions provides both challenges and opportunities for studying sea ice dynamics. In this study we investigate sea ice type dynamics in the Fram strait based on Sentinel-1 A, B SAR data. Series of images for the winter season are classified into 4 ice types (young ice, first year ice, multiyear ice and leads) using the new algorithm developed by us for sea ice classification, which is based on segmentation, GLCM calculation, Haralick texture feature extraction, unsupervised and supervised classifications and Support Vector Machine (SVM) [Zakhvatkina et al., 2016; Korosov et al., 2016]. This algorithm is further improved by applying thermal and scalloping noise removal [Park et al. 2016]. Sea ice drift is retrieved from the same series of Sentinel-1 images using the newly developed algorithm based on combination of feature tracking and pattern matching [Mukenhuber et al., 2016]. Time series of these two products (sea ice type and sea ice drift) are combined in order to study sea ice deformation processes at small scales. Zones of sea ice convergence and divergence identified from sea ice drift are compared with ridges and leads identified from texture features. That allows more specific interpretation of SAR imagery and more accurate automatic classification. In addition, the map of four ice types calculated using the texture features from one SAR image is propagated forward using the sea ice drift vectors. The propagated ice type is compared with ice type derived from the next image. The comparison identifies changes in ice type which occurred during drift and allows to reduce uncertainties in sea ice type calculation.

Applicability Assessment of Uavsar Data in Wetland Monitoring: a Case Study of Louisiana Wetland

NASA Astrophysics Data System (ADS)

Zhao, J.; Niu, Y.; Lu, Z.; Yang, J.; Li, P.; Liu, W.

2018-04-01

Wetlands are highly productive and support a wide variety of ecosystem goods and services. Monitoring wetland is essential and potential. Because of the repeat-pass nature of satellite orbit and airborne, time-series of remote sensing data can be obtained to monitor wetland. UAVSAR is a NASA L-band synthetic aperture radar (SAR) sensor compact pod-mounted polarimetric instrument for interferometric repeat-track observations. Moreover, UAVSAR images can accurately map crustal deformations associated with natural hazards, such as volcanoes and earthquakes. And its polarization agility facilitates terrain and land-use classification and change detection. In this paper, the multi-temporal UAVSAR data are applied for monitoring the wetland change. Using the multi-temporal polarimetric SAR (PolSAR) data, the change detection maps are obtained by unsupervised and supervised method. And the coherence is extracted from the interfometric SAR (InSAR) data to verify the accuracy of change detection map. The experimental results show that the multi-temporal UAVSAR data is fit for wetland monitor.

An assessment of the Height Above Nearest Drainage terrain descriptor for the thematic enhancement of automatic SAR-based flood monitoring services

NASA Astrophysics Data System (ADS)

Chow, Candace; Twele, André; Martinis, Sandro

2016-10-01

Flood extent maps derived from Synthetic Aperture Radar (SAR) data can communicate spatially-explicit information in a timely and cost-effective manner to support disaster management. Automated processing chains for SAR-based flood mapping have the potential to substantially reduce the critical time delay between the delivery of post-event satellite data and the subsequent provision of satellite derived crisis information to emergency management authorities. However, the accuracy of SAR-based flood mapping can vary drastically due to the prevalent land cover and topography of a given scene. While expert-based image interpretation with the consideration of contextual information can effectively isolate flood surface features, a fully-automated feature differentiation algorithm mainly based on the grey levels of a given pixel is comparatively more limited for features with similar SAR-backscattering characteristics. The inclusion of ancillary data in the automatic classification procedure can effectively reduce instances of misclassification. In this work, a near-global `Height Above Nearest Drainage' (HAND) index [10] was calculated with digital elevation data and drainage directions from the HydroSHEDS mapping project [2]. The index can be used to separate flood-prone regions from areas with a low probability of flood occurrence. Based on the HAND-index, an exclusion mask was computed to reduce water look-alikes with respect to the hydrologictopographic setting. The applicability of this near-global ancillary data set for the thematic improvement of Sentinel-1 and TerraSAR-X based services for flood and surface water monitoring has been validated both qualitatively and quantitatively. Application of a HAND-based exclusion mask resulted in improvements to the classification accuracy of SAR scenes with high amounts of water look-alikes and considerable elevation differences.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

NASA Astrophysics Data System (ADS)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

Land Cover Monitoring for Water Resources Management in Angola

NASA Astrophysics Data System (ADS)

Miguel, Irina; Navarro, Ana; Rolim, Joao; Catalao, Joao; Silva, Joel; Painho, Marco; Vekerdy, Zoltan

2016-08-01

The aim of this paper is to assess the impact of improved temporal resolution and multi-source satellite data (SAR and optical) on land cover mapping and monitoring for efficient water resources management. For that purpose, we developed an integrated approach based on image classification and on NDVI and SAR backscattering (VV and VH) time series for land cover mapping and crop's irrigation requirements computation. We analysed 28 SPOT-5 Take-5 images with high temporal revisiting time (5 days), 9 Sentinel-1 dual polarization GRD images and in-situ data acquired during the crop growing season. Results show that the combination of images from different sources provides the best information to map agricultural areas. The increase of the images temporal resolution allows the improvement of the estimation of the crop parameters, and then, to calculate of the crop's irrigation requirements. However, this aspect was not fully exploited due to the lack of EO data for the complete growing season.

Determination of The Water Catchment Area in Semarang City Using a Combination of Object Based Image Analysis (OBIA) Classification, InSAR and Geographic Information System (GIS) Methods Based On a High-Resolution SPOT 6 Image and Radar Imagery

NASA Astrophysics Data System (ADS)

Prasetyo, Yudo; Ardi Gunawan, Setyo; Maksum, Zia Ul

2016-11-01

Semarang is the biggest city in central Java-Indonesia which has a rapid and massive infrastructure development nowadays. In order to control water resources and flood, the local goverment has been built east and west flood canal in Kaligarang and West Semarang River. One of main problem in Semarang city is the lack of fresh water in dry season because ground water is not rechargeable well. Rechargeable groundwater ability depends on underground water recharge rate and catchment area condition. The objective of the study is to determine condition and classification of water catchment area in Semarang city. The catchment area conditions will be determine by five parameters as follows soil type, land use, slope, ground water potential and rainfall intensity. In this study, we use three methods approach to solve the problem which is segmentation classification to acquire land use classification from high resolution imagery using nearest neighborhood algorithm, Interferometric Synthetic Aperture Radar (SAR) to derive DTM from SAR Imagery and multi criteria weighting and spatial analysis using GIS method. There are three types optical image (ALOS PRISM, SPOT-6 and ALOS PALSAR) to calculate water catchment area condition in Semarang city. For final result, this research will divide the water catchment into six criteria as follows good, naturally normal, early critical, a little bit critical, critical and very critical condition. The result shows that water catchment area condition is in an early critical condition around 2607,523 Ha (33,17 %), naturally normal condition around 1507,674 Ha (19,18 %), a little bit critical condition around 1452,931 Ha (18,48 %), good with 1157,04 Ha (14,72 %), critical with 1058,639 Ha (13,47 %) and very critical with 75,0387 Ha (0,95 %). The distribution of water catchment area conditions in West and East Flood Canal have an irreguler pattern. In northern area of watershed consists of begin to critical, naturally normal and good condition. Meanwhile in southern area of watershed consists of a little bit critical, critical and very critical condition.

Ice Types in the Beaufort Sea, Alaska

NASA Technical Reports Server (NTRS)

2003-01-01

Determining the amount and type of sea ice in the polar oceans is crucial to improving our knowledge and understanding of polar weather and long term climate fluctuations. These views from two satellite remote sensing instruments; the synthetic aperture radar (SAR) on board the RADARSAT satellite and the Multi-angle Imaging SpectroRadiometer (MISR), illustrate different methods that may be used to assess sea ice type. Sea ice in the Beaufort Sea off the north coast of Alaska was classified and mapped in these concurrent images acquired March 19, 2001 and mapped to the same geographic area.

To identify sea ice types, the National Oceanic and Atmospheric Administration (NOAA) National Ice Center constructs ice charts using several data sources including RADARSAT SAR images such as the one shown at left. SAR classifies sea ice types primarily by how the surface and subsurface roughness influence radar backscatter. In the SAR image, white lines delineate different sea ice zones as identified by the National Ice Center. Regions of mostly multi-year ice (A) are separated from regions with large amounts of first year and younger ice (B-D), and the dashed white line at bottom marks the coastline. In general, sea ice types that exhibit increased radar backscatter appear bright in SAR and are identified as rougher, older ice types. Younger, smoother ice types appear dark to SAR. Near the top of the SAR image, however, red arrows point to bright areas in which large, crystalline 'frost flowers' have formed on young, thin ice, causing this young ice type to exhibit an increased radar backscatter. Frost flowers are strongly backscattering at radar wavelengths (cm) due to both surface roughness and the high salinity of frost flowers, which causes them to be highly reflective to radar energy.

Surface roughness is also registered by MISR, although the roughness observed is at a different spatial scale. Older, rougher ice areas are predominantly backward scattering to the MISR cameras, whereas younger, smoother ice types are predominantly forward scattering. The MISR map at right was generated using a statistical classification routine (called ISODATA) and analyzed using ice charts from the National Ice Center. Five classes of sea ice were found based upon the classification of MISR angular data. These are described, based on interpretation of the SAR image, by the image key. Very smooth ice areas that are predominantly forward scattering are colored red. Frost flowers are largely smooth to the MISR visible band sensor and are mapped as forward scattering. Areas mapped as blue are predominantly backward scattering, and the other three classes have statistically distinct angular signatures and fall within the middle of the forward/backward scattering continuum. Some areas that may be first year or younger ice between the multi year ice floes are not discernible to SAR, illustrating how MISR potentially can make a unique contribution to sea ice mapping.

The Multi-angle Imaging SpectroRadiometer observes the daylit Earth continuously and every 9 days views the entire globe between 82 degrees north and 82 degrees south latitude. This data product was generated from a portion of the imagery acquired during Terra orbit 6663. The MISR image has been cropped to include an area that is 200 kilometers wide, and utilizes data from blocks 30 to 33 within World Reference System-2 path 71.

MISR was built and is managed by NASA's Jet Propulsion Laboratory,Pasadena, CA, for NASA's Office of Earth Science, Washington, DC. The Terra satellite is managed by NASA's Goddard Space Flight Center, Greenbelt, MD. JPL is a division of the California Institute of Technology.

Surface Water Detection Using Fused Synthetic Aperture Radar, Airborne LiDAR and Optical Imagery

NASA Astrophysics Data System (ADS)

Braun, A.; Irwin, K.; Beaulne, D.; Fotopoulos, G.; Lougheed, S. C.

2016-12-01

Each remote sensing technique has its unique set of strengths and weaknesses, but by combining techniques the classification accuracy can be increased. The goal of this project is to underline the strengths and weaknesses of Synthetic Aperture Radar (SAR), LiDAR and optical imagery data and highlight the opportunities where integration of the three data types can increase the accuracy of identifying water in a principally natural landscape. The study area is located at the Queen's University Biological Station, Ontario, Canada. TerraSAR-X (TSX) data was acquired between April and July 2016, consisting of four single polarization (HH) staring spotlight mode backscatter intensity images. Grey-level thresholding is used to extract surface water bodies, before identifying and masking zones of radar shadow and layover by using LiDAR elevation models to estimate the canopy height and applying simple geometry algorithms. The airborne LiDAR survey was conducted in June 2014, resulting in a discrete return dataset with a density of 1 point/m2. Radiometric calibration to correct for range and incidence angle is applied, before classifying the points as water or land based on corrected intensity, elevation, roughness, and intensity density. Panchromatic and multispectral (4-band) imagery from Quickbird was collected in September 2005 at spatial resolutions of 0.6m and 2.5m respectively. Pixel-based classification is applied to identify and distinguish water bodies from land. A classification system which inputs SAR-, LiDAR- and optically-derived water presence models in raster formats is developed to exploit the strengths and weaknesses of each technique. The total percentage of water detected in the sample area for SAR backscatter, LiDAR intensity, and optical imagery was 27%, 19% and 18% respectively. The output matrix of the classification system indicates that in over 72% of the study area all three methods agree on the classification. Analysis was specifically targeted towards areas where the methods disagree, highlighting how each technique should be properly weighted over these areas to increase the classification accuracy of water. The conclusions and techniques developed in this study are applicable to other areas where similar environmental conditions and data availability exist.

A Combined Use of Decomposition and Texture for Terrain Classification of Fully Polarimetric SAR Images

NASA Astrophysics Data System (ADS)

Rodionova, N. V.

2007-03-01

This p aper presents two-stag e unsupervised terrain classification of fully polarimetr ic SA R data using Freeman and Durden decomposition based on three simp le scattering mechanisms: surface, volume and double bounce (first step), and textur al features (uncorrelated uniformity , contr ast, inv erse mo men t and entropy) obtained from grey lev el co-occurrence matr ices (GLCM) (second step). Textural f eatures ar e defined in moving w indow 5x5 pixels w ith N=32 (N - number of grey lev els) . This algorith m preserves th e purity of domin ant polarimetric scattering properties and defines textural features in each scatter ing category. It is shown better object discrimin ation after app lying textur e w ith in fix ed scattering category. Speckle r eduction is one of th e main mo ments in imag e interpr etation improvement because of its great influen ce on textur e. Results from unfiltered and Lee filtered polar imetr ic SAR imag es show that the v alues of contrast and en tropy decr ease and th e values of uniformity and inverse moment increase with speckle reduction, that's tru e for all polarizations (HH, VV, HV). Th e d iscr imination b etw een objects increases after speckle f ilter ing. Polar ization influen ce on textur e features is def ined by calculating th e features in SAR images w ith HH , VV and HV polarizations before and after speck le filter ing, and then creating RG B images. It is shown mor e polarization inf luence on textur e features (uniformity , inverse mo ment and entropy) before filtering and less influen ce - after speck le f iltering. I t's not true for contrast wher e polar ization influen ce is not ch anged practically w ith filtering. SIR-C/X-SA R SLC L-band imag es of Moscow r egion are used for illustr ation.

Surface water classification and monitoring using polarimetric synthetic aperture radar

NASA Astrophysics Data System (ADS)

Irwin, Katherine Elizabeth

Surface water classification using synthetic aperture radar (SAR) is an established practice for monitoring flood hazards due to the high temporal and spatial resolution it provides. Surface water change is a dynamic process that varies both spatially and temporally, and can occur on various scales resulting in significant impacts on affected areas. Small-scale flooding hazards, caused by beaver dam failure, is an example of surface water change, which can impact nearby infrastructure and ecosystems. Assessing these hazards is essential to transportation and infrastructure maintenance. With current satellite missions operating in multiple polarizations, spatio-temporal resolutions, and frequencies, a comprehensive comparison between SAR products for surface water monitoring is necessary. In this thesis, surface water extent models derived from high resolution single-polarization TerraSAR-X (TSX) data, medium resolution dual-polarization TSX data and low resolution quad-polarization RADARSAT-2 (RS-2) data are compared. There exists a compromise between acquiring SAR data with a high resolution or high information content. Multi-polarization data provides additional phase and intensity information, which makes it possible to better classify areas of flooded vegetation and wetlands. These locations are often where fluctuations in surface water occur and are essential for understanding dynamic underlying processes. However, often multi-polarized data is acquired at a low resolution, which cannot image these zones effectively. High spatial resolution, single-polarization TSX data provides the best model of open water. However, these single-polarization observations have limited information content and are affected by shadow and layover errors. This often hinders the classification of other land cover types. The dual-polarization TSX data allows for the classification of flooded vegetation, but classification is less accurate compared to the quad-polarization RS-2 data. The RS-2 data allows for the discrimination of open water, marshes/fields and forested areas. However, the RS-2 data is less applicable to small scale surface water monitoring (e.g. beaver dam failure), due to its low spatial resolution. By understanding the strengths and weaknesses of available SAR technology, an appropriate product can be chosen for a specific target application involving surface water change. This research benefits the eventual development of a space-based monitoring strategy over longer periods.

Crop Identification Using Time Series of Landsat-8 and Radarsat-2 Images: Application in a Groundwater Irrigated Region, South India

NASA Astrophysics Data System (ADS)

Sharma, A. K.; Hubert-Moy, L.; Betbederet, J.; Ruiz, L.; Sekhar, M.; Corgne, S.

2016-08-01

Monitoring land use and land cover and more particularly irrigated cropland dynamics is of great importance for water resources management and land use planning. The objective of this study was to evaluate the combined use of multi-temporal optical and radar data with a high spatial resolution in order to improve the precision of irrigated crop identification by taking into account information on crop phenological stages. SAR and optical parameters were derived from time- series of seven quad-pol RADARSAT-2 and four Landsat-8 images which were acquired on the Berambadi catchment, South India, during the monsoon crop season at the growth stages of turmeric crop. To select the best parameter to discriminate turmeric crops, an analysis of covariance (ANCOVA) was applied on all the time-series parameters and the most discriminant ones were classified using the Support Vector Machine (SVM) technique. Results show that in absence of optical images, polarimetric parameters derived from SAR time-series can be used for the turmeric area estimates and that the combined use of SAR and optical parameters can improve the classification accuracy to identify turmeric.

Classifying coastal resources by integrating optical and radar imagery and color infrared photography

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, Gene A.; Sapkota, Sijan

1998-01-01

A progressive classification of a marsh and forest system using Landsat Thematic Mapper (TM), color infrared (CIR) photograph, and ERS-1 synthetic aperture radar (SAR) data improved classification accuracy when compared to classification using solely TM reflective band data. The classification resulted in a detailed identification of differences within a nearly monotypic black needlerush marsh. Accuracy percentages of these classes were surprisingly high given the complexities of classification. The detailed classification resulted in a more accurate portrayal of the marsh transgressive sequence than was obtainable with TM data alone. Individual sensor contribution to the improved classification was compared to that using only the six reflective TM bands. Individually, the green reflective CIR and SAR data identified broad categories of water, marsh, and forest. In combination with TM, SAR and the green CIR band each improved overall accuracy by about 3% and 15% respectively. The SAR data improved the TM classification accuracy mostly in the marsh classes. The green CIR data also improved the marsh classification accuracy and accuracies in some water classes. The final combination of all sensor data improved almost all class accuracies from 2% to 70% with an overall improvement of about 20% over TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m in some areas.

Sedimentary Environments Mapping in the Yellow Sea Using TanDEM-X and Optic Satellites

NASA Astrophysics Data System (ADS)

Ryu, J. H.; Lee, Y. K.; Kim, S. W.

2017-12-01

Due to land reclamation and dredging, 57% of China's coastal wetlands have disappeared since the 1950s, and the total area of tidal flats in South Korea decreased from approximately 2,800km2 in 1990 to 2392km2 in 2005(Qiu, 2011 and MLTM, 2010). Intertidal DEM and sedimentary facies are useful for understanding intertidal functions and monitoring their response to natural and anthropogenic actions. Highly accurate intertidal DEMs with 5-m resolution were generated based on the TanDEM-X interferometric SAR (InSAR) technique because TanDEM-X allows the acquisition of the coherent InSAR pairs with no time lag or approximately 10-second temporal baseline between master and slave SAR image. We successfully generated intertidal zone DEMs with 5-7-m spatial resolutions and interferometric height accuracies better than 0.15 m for three representative tidal flats on the west coast of the Korean Peninsula and one site of chinese coastal region in the Yellow Sea. Surface sediment classification based on remotely sensed data must circumspectly consider an effective critical grain size, water content, local topography, and intertidal structures. The earlier studies have some limitation that the classification map is not considered to analysis various environmental conditions. Therefore, the purpose of this study was minutely to mapping the surface sedimentary facies by analyzing the tidal channel, topography with multi-sensor remotely sensed data and in-situ data.

Sea Ice Classification Using Synthetic Aperture Radar

DTIC Science & Technology

1990-06-01

same as the correlation of VBI to VB2 . There are other considerations that will influence the strength of the discrimination. Some of these...high density data tapes that the images used in this thesis were extracted . The fact that the SAR was not calibrated may have some effect on the...imagery. These images were extracted from the data collected on missions flown on 5-8 April 1987. The areal coverage of these missions are shown in

Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance

NASA Astrophysics Data System (ADS)

Ratha, Debanshu; Bhattacharya, Avik; Frery, Alejandro C.

2018-01-01

In this letter, we propose a novel technique for obtaining scattering components from Polarimetric Synthetic Aperture Radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories i.e. odd-bounce, double-bounce and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of [J.-S. Lee, M. R. Grunes, E. Pottier, and L. Ferro-Famil, Unsupervised terrain classification preserving polarimetric scattering characteristics, IEEE Trans. Geos. Rem. Sens., vol. 42, no. 4, pp. 722731, April 2004.] based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 datasets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle (OA) corrected PolSAR image. Furthermore, (1) the scattering similarity is a completely non-negative quantity unlike the negative powers that might occur in double- bounce and odd-bounce scattering component under Freeman Durden decomposition (FDD), and (2) the methodology can be extended to more canonical targets as well as for bistatic scattering.

SAR data for river ice monitoring. How to meet requirements?

NASA Astrophysics Data System (ADS)

Łoś, Helena; Osińska-Skotak, Katarzyna; Pluto-Kossakowska, Joanna

2017-04-01

Although river ice is a natural element of rivers regime it can lead to severe problems such as winter floods or damages of bridges and bank revetments. Services that monitor river ice condition are still often based on field observation. For several year, however, Earth observation data have become of a great interest, especially SAR images, which allows to observe ice and river condition independently of clouds and sunlight. One of requirements of an effective monitoring system is frequent and regular data acquisition. To help to meet this requirement we assessed an impact of selected SAR data parameters into automatic ice types identification. Presented work consists of two parts. The first one focuses on comparison of C-band and X-band data in terms of the main ice type detection. The second part contains an analysis of polarisation reduction from quad-pol to dual-pol data. As the main element of data processing we chose the supervised classification with maximum likelihood algorithm adapted to Wishart distribution. The classification was preceded by statistical analysis of radar signal obtained for selected ice types including separability measures. Two river were selected as areas of interest - the Peace River in Canada and the Vistula in Poland. The results shows that using data registered in both bands similar accuracy of classification into main ice types can be obtain. Differences appear with details e.g. thin initial ice. Classification results obtained from quad-pol and dual-pol data were similar while four classes were selected. With six classes, however, differences between polarisation types have been noticed.

Automated Wetland Delineation from Multi-Frequency and Multi-Polarized SAR Images in High Temporal and Spatial Resolution

NASA Astrophysics Data System (ADS)

Moser, L.; Schmitt, A.; Wendleder, A.

2016-06-01

Water scarcity is one of the main challenges posed by the changing climate. Especially in semi-arid regions where water reservoirs are filled during the very short rainy season, but have to store enough water for the extremely long dry season, the intelligent handling of water resources is vital. This study focusses on Lac Bam in Burkina Faso, which is the largest natural lake of the country and of high importance for the local inhabitants for irrigated farming, animal watering, and extraction of water for drinking and sanitation. With respect to the competition for water resources an independent area-wide monitoring system is essential for the acceptance of any decision maker. The following contribution introduces a weather and illumination independent monitoring system for the automated wetland delineation with a high temporal (about two weeks) and a high spatial sampling (about five meters). The similarities of the multispectral and multi-polarized SAR acquisitions by RADARSAT-2 and TerraSAR-X are studied as well as the differences. The results indicate that even basic approaches without pre-classification time series analysis or post-classification filtering are already enough to establish a monitoring system of prime importance for a whole region.

Application of SAR remote sensing and crop modeling for operational rice crop monitoring in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Maunahan, A. A.; Gatti, L.; Quicho, E. D.; Pazhanivelan, S.; Campos-Taberner, M.; Collivignarelli, F.; Haro, J. G.; Intrman, A.; Phuong, D.; Boschetti, M.; Prasadini, P.; Busetto, L.; Minh, V. Q.; Tuan, V. Q.

2017-12-01

This study uses multi-temporal SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations in South and South Asian countries and assimilate the information into ORYZA Crop Growth Simulation Model (CGSM) to monitor rice yield. The study demonstrates examples of operational application of this rice monitoring system in: (1) detecting drought impact on rice planting in Central Thailand and Tamil Nadu, India, (2) mapping heat stress impact on rice yield in Andhra Pradesh, India, and (3) generating historical rice yield data for districts in Red River Delta, Vietnam.

Monitoring of "urban villages" in Shenzhen, China from high-resolution GF-1 and TerraSAR-X data

NASA Astrophysics Data System (ADS)

Wei, Chunzhu; Blaschke, Thomas; Taubenböck, Hannes

2015-10-01

Urban villages comprise mainly low-rise and congested, often informal settlements surrounded by new constructions and high-rise buildings whereby structures can be very different between neighboring areas. Monitoring urban villages and analyzing their characteristics are crucial for urban development and sustainability research. In this study, we carried out a combined analysis of multispectral GaoFen-1 (GF-1) and high resolution TerraSAR-X radar (TSX) imagery to extract the urban village information. GF-1 and TSX data are combined with the Gramshmidt spectral sharpening method so as to provide new input data for urban village classification. The Grey-Level Co-occurrence Matrix (GLCM) approach was also applied to four directions to provide another four types (all, 0°, 90°, 45° directions) of TSX-based inputs for urban village detection. We analyzed the urban village mapping performance using the Random Forest approach. The results demonstrate that the best overall accuracy and the best producer accuracy of urban villages reached with the GLCM 90° dataset (82.33%, 68.54% respectively). Adding single polarization TSX data as input information to the optical image GF-1 provided an average product accuracy improvement of around 7% in formal built-up area classification. The SAR and optical fusion imagery also provided an effective means to eliminate some layover, shadow effects, and dominant scattering at building locations and green spaces, improving the producer accuracy by 7% in urban area classification. To sum up, the added value of SAR information is demonstrated by the enhanced results achievable over built-up areas, including formal and informal settlements.

Interferometric SAR for characterization of ravines as a function of their density, depth, and surface cover

NASA Astrophysics Data System (ADS)

Chatterjee, R. S.; Saha, S. K.; Suresh Kumar; Sharika Mathew; Lakhera, R. C.; Dadhwal, V. K.

In recent years, the problem of ravine erosion with consequent loss of usable land has received much attention worldwide. The Chambal ravine zone in India is well known for being an extremely intricate, deeply incised network of ravines in a 10 km wide zone on the flanks of the Chambal River. It occupies an area of ˜0.5 million hectares at the expense of fertile agricultural land of the Chambal Valley. The broad grouping of the ravines considering their reclamation potential, as carried out by previous workers based on visual interpretation of optical remote sensing data, is mostly descriptive in nature. In the present study, characterization of the ravines as a function of their erosion potential expressed through ravine density, ravine depth, and ravine surface cover was made in quantitative terms exploiting the preferential characteristics of side-looking, long-wavelength, coherent SAR signal and precision measurements associated with the InSAR technique. The outlines of ravines appear remarkably prominent in SAR backscattered amplitude images due to the high sensitivity of the SAR signal to terrain ruggedness. Using local statistics-based meso and macro textural information of SAR backscattered amplitude images in 7×7 pixel windows (the pixel size being 20 m×20 m), the ravine-affected area has been classified into three density classes, namely low, moderate, and high density ravine classes. C-band InSAR digital elevation models (DEMs) of sparsely vegetated ravine areas essentially give the terrain height. From the pixel-by-pixel terrain height, the ravine depth was calculated by differencing the maximum and minimum terrain heights of the pixels in a 100 m distance range. Considering the vertical precision of the ERS InSAR DEMs of ˜5 m and ravine depth classification by previous workers [Sharma, H.S., 1968. Genesis and pattern of ravines of the Lower Chambal Valley, India. Special Issue. 21st International Geographical Union Congress 30(4), 14-24; Seth, S.P., Bhatnagar, R.K., Chauhan, S.S., 1969. Reclamability classification and nature of ravines of Chambal Command Areas. Journal of Soil and Water Conservation in India 17 (3-4), 39-44.], three depth classes, namely shallow (<5 m), moderately deep (5-20 m), and deep (>20 m) ravines, were made. Using the temporal decorrelation property of the close time interval InSAR data pair, namely the ERS SAR tandem pair, four ravine surface cover classes, namely barren land, grass/scrub/crop land, sparse vegetation, and wet land/dense vegetation, could be delineated, which was corroborated by the spectral signatures in the optical range and selective ground truths.

Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data

NASA Astrophysics Data System (ADS)

Yang, Huijin; Pan, Bin; Wu, Wenfu; Tai, Jianhao

2018-07-01

Rice is one of the most important cereals in the world. With the change of agricultural land, it is urgently necessary to update information about rice planting areas. This study aims to map rice planting areas with a field-based approach through the integration of multi-temporal Sentinel-1A and Landsat-8 OLI data in Wuhua County of South China where has many basins and mountains. This paper, using multi-temporal SAR and optical images, proposes a methodology for the identification of rice-planting areas. This methodology mainly consists of SSM applied to time series SAR images for the calculation of a similarity measure, image segmentation process applied to the pan-sharpened optical image for the searching of homogenous objects, and the integration of SAR and optical data for the elimination of some speckles. The study compares the per-pixel approach with the per-field approach and the results show that the highest accuracy (91.38%) based on the field-based approach is 1.18% slightly higher than that based on the pixel-based approach for VH polarization, which is brought by eliminating speckle noise through comparing the rice maps of these two approaches. Therefore, the integration of Sentinel-1A and Landsat-8 OLI images with a field-based approach has great potential for mapping rice or other crops' areas.

Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

PubMed Central

Marapareddy, Ramakalavathi; Aanstoos, James V.; Younan, Nicolas H.

2016-01-01

Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ1, λ2, and λ3), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers. PMID:27322270

Coefficient of variation for use in crop area classification across multiple climates

NASA Astrophysics Data System (ADS)

Whelen, Tracy; Siqueira, Paul

2018-05-01

In this study, the coefficient of variation (CV) is introduced as a unitless statistical measurement for the classification of croplands using synthetic aperture radar (SAR) data. As a measurement of change, the CV is able to capture changing backscatter responses caused by cycles of planting, growing, and harvesting, and thus is able to differentiate these areas from a more static forest or urban area. Pixels with CV values above a given threshold are classified as crops, and below the threshold are non-crops. This paper uses cross-polarized L-band SAR data from the ALOS PALSAR satellite to classify eleven regions across the United States, covering a wide range of major crops and climates. Two separate sets of classification were done, with the first targeting the optimum classification thresholds for each dataset, and the second using a generalized threshold for all datasets to simulate a large-scale operationalized situation. Overall accuracies for the first phase of classification ranged from 66%-81%, and 62%-84% for the second phase. Visual inspection of the results shows numerous possibilities for improving the classifications while still using the same classification method, including increasing the number and temporal frequency of input images in order to better capture phenological events and mitigate the effects of major precipitation events, as well as more accurate ground truth data. These improvements would make the CV method a viable tool for monitoring agriculture throughout the year on a global scale.

Remote sensing of frozen lakes on the North Slope of Alaska

USGS Publications Warehouse

French, N.; Savage, S.; Shuchman, R.; Edson, R.; Payne, J.; Josberger, E.

2004-01-01

We used synthetic aperture radar (SAR) images from the ERS-2 remote sensing satellite to map the freeze condition of lakes on Alaska's North Slope, the geographic region to the north of the Brooks Range. An mage from March 1997, to coincide with the period of maximum freeze depth, was used for the frozen lake mapping. Emphasis was placed on distinguishing between lakes frozen to the lakebed and lakes with some portion unfrozen to the bed (a binary classification). The result of the analysis is a map identifying lakes as frozen to the lakebed and lakes not frozen to the lakebed. This analysis of one SAR image has shown the feasibility of a simple technique for mapping frozen lake condition for supporting decision making and understanding impacts of climate change on the North Slope.

Imaging of downward-looking linear array SAR using three-dimensional spatial smoothing MUSIC algorithm

NASA Astrophysics Data System (ADS)

Zhang, Siqian; Kuang, Gangyao

2014-10-01

In this paper, a novel three-dimensional imaging algorithm of downward-looking linear array SAR is presented. To improve the resolution, multiple signal classification (MUSIC) algorithm has been used. However, since the scattering centers are always correlated in real SAR system, the estimated covariance matrix becomes singular. To address the problem, a three-dimensional spatial smoothing method is proposed in this paper to restore the singular covariance matrix to a full-rank one. The three-dimensional signal matrix can be divided into a set of orthogonal three-dimensional subspaces. The main idea of the method is based on extracting the array correlation matrix as the average of all correlation matrices from the subspaces. In addition, the spectral height of the peaks contains no information with regard to the scattering intensity of the different scattering centers, thus it is difficulty to reconstruct the backscattering information. The least square strategy is used to estimate the amplitude of the scattering center in this paper. The above results of the theoretical analysis are verified by 3-D scene simulations and experiments on real data.

A novel latent gaussian copula framework for modeling spatial correlation in quantized SAR imagery with applications to ATR

NASA Astrophysics Data System (ADS)

Thelen, Brian T.; Xique, Ismael J.; Burns, Joseph W.; Goley, G. Steven; Nolan, Adam R.; Benson, Jonathan W.

2017-04-01

With all of the new remote sensing modalities available, and with ever increasing capabilities and frequency of collection, there is a desire to fundamentally understand/quantify the information content in the collected image data relative to various exploitation goals, such as detection/classification. A fundamental approach for this is the framework of Bayesian decision theory, but a daunting challenge is to have significantly flexible and accurate multivariate models for the features and/or pixels that capture a wide assortment of distributions and dependen- cies. In addition, data can come in the form of both continuous and discrete representations, where the latter is often generated based on considerations of robustness to imaging conditions and occlusions/degradations. In this paper we propose a novel suite of "latent" models fundamentally based on multivariate Gaussian copula models that can be used for quantized data from SAR imagery. For this Latent Gaussian Copula (LGC) model, we derive an approximate, maximum-likelihood estimation algorithm and demonstrate very reasonable estimation performance even for the larger images with many pixels. However applying these LGC models to large dimen- sions/images within a Bayesian decision/classification theory is infeasible due to the computational/numerical issues in evaluating the true full likelihood, and we propose an alternative class of novel pseudo-likelihoood detection statistics that are computationally feasible. We show in a few simple examples that these statistics have the potential to provide very good and robust detection/classification performance. All of this framework is demonstrated on a simulated SLICY data set, and the results show the importance of modeling the dependencies, and of utilizing the pseudo-likelihood methods.

A Matlab Program for Textural Classification Using Neural Networks

NASA Astrophysics Data System (ADS)

Leite, E. P.; de Souza, C.

2008-12-01

A new MATLAB code that provides tools to perform classification of textural images for applications in the Geosciences is presented. The program, here coined TEXTNN, comprises the computation of variogram maps in the frequency domain for specific lag distances in the neighborhood of a pixel. The result is then converted back to spatial domain, where directional or ominidirectional semivariograms are extracted. Feature vectors are built with textural information composed of the semivariance values at these lag distances and, moreover, with histogram measures of mean, standard deviation and weighted fill-ratio. This procedure is applied to a selected group of pixels or to all pixels in an image using a moving window. A feed- forward back-propagation Neural Network can then be designed and trained on feature vectors of predefined classes (training set). The training phase minimizes the mean-squared error on the training set. Additionally, at each iteration, the mean-squared error for every validation is assessed and a test set is evaluated. The program also calculates contingency matrices, global accuracy and kappa coefficient for the three data sets, allowing a quantitative appraisal of the predictive power of the Neural Network models. The interpreter is able to select the best model obtained from a k-fold cross-validation or to use a unique split-sample data set for classification of all pixels in a given textural image. The code is opened to the geoscientific community and is very flexible, allowing the experienced user to modify it as necessary. The performance of the algorithms and the end-user program were tested using synthetic images, orbital SAR (RADARSAT) imagery for oil seepage detection, and airborne, multi-polarimetric SAR imagery for geologic mapping. The overall results proved very promising.

Evaluation of space SAR as a land-cover classification

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Williams, T. H. L.

1985-01-01

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Feasibility of sea ice typing with synthetic aperture radar (SAR): Merging of Landsat thematic mapper and ERS 1 SAR satellite imagery

NASA Technical Reports Server (NTRS)

Steffen, Konrad; Heinrichs, John

1994-01-01

Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and Landsat thematic mapper (TM) images were acquired for the same area in the Beaufort Sea, April 16 and 18, 1992. The two image pairs were colocated to the same grid (25-m resolution), and a supervised ice type classification was performed on the TM images in order to classify ice free, nilas, gray ice, gray-white ice, thin first-year ice, medium and thick first-year ice, and old ice. Comparison of the collocated SAR pixels showed that ice-free areas can only be classified under calm wind conditions (less than 3 m/s) and for surface winds greater than 10 m/s based on the backscattering coefficient alone. This is true for pack ice regions during the cold months of the year where ice-free areas are spatially limited and where the capillary waves that cause SAR backscatter are dampened by entrained ice crystals. For nilas, two distinct backscatter classes were found at -17 dB and at -10 dB. The higher backscattering coefficient is attributed to the presence of frost flowers on light nilas. Gray and gray-white ice have a backscatter signature similar to first-year ice and therefore cannot be distinguished by SAR alone. First-year and old ice can be clearly separated based on their backscattering coefficient. The performance of the Geophysical Processor System ice classifier was tested against the Landsat derived ice products. It was found that smooth first-year ice and rough first-year ice were not significantly different in the backscatter domain. Ice concentration estimates based on ERS 1 C band SAR showed an error range of 5 to 8% for high ice concentration regions, mainly due to misclassified ice-free and smooth first-year ice areas. This error is expected to increase for areas of lower ice concentration. The combination of C band SAR and TM channels 2, 4, and 6 resulted in ice typing performance with an estimated accuracy of 90% for all seven ice classes.

Combined DEM Extration Method from StereoSAR and InSAR

NASA Astrophysics Data System (ADS)

Zhao, Z.; Zhang, J. X.; Duan, M. Y.; Huang, G. M.; Yang, S. C.

2015-06-01

A pair of SAR images acquired from different positions can be used to generate digital elevation model (DEM). Two techniques exploiting this characteristic have been introduced: stereo SAR and interferometric SAR. They permit to recover the third dimension (topography) and, at the same time, to identify the absolute position (geolocation) of pixels included in the imaged area, thus allowing the generation of DEMs. In this paper, StereoSAR and InSAR combined adjustment model are constructed, and unify DEM extraction from InSAR and StereoSAR into the same coordinate system, and then improve three dimensional positioning accuracy of the target. We assume that there are four images 1, 2, 3 and 4. One pair of SAR images 1,2 meet the required conditions for InSAR technology, while the other pair of SAR images 3,4 can form stereo image pairs. The phase model is based on InSAR rigorous imaging geometric model. The master image 1 and the slave image 2 will be used in InSAR processing, but the slave image 2 is only used in the course of establishment, and the pixels of the slave image 2 are relevant to the corresponding pixels of the master image 1 through image coregistration coefficient, and it calculates the corresponding phase. It doesn't require the slave image in the construction of the phase model. In Range-Doppler (RD) model, the range equation and Doppler equation are a function of target geolocation, while in the phase equation, the phase is also a function of target geolocation. We exploit combined adjustment model to deviation of target geolocation, thus the problem of target solution is changed to solve three unkonwns through seven equations. The model was tested for DEM extraction under spaceborne InSAR and StereoSAR data and compared with InSAR and StereoSAR methods respectively. The results showed that the model delivered a better performance on experimental imagery and can be used for DEM extraction applications.

What is missing? An operational inundation mapping framework by SAR data

NASA Astrophysics Data System (ADS)

Shen, X.; Anagnostou, E. N.; Zeng, Z.; Kettner, A.; Hong, Y.

2017-12-01

Compared to optical sensors, synthetic aperture radar (SAR) works all-day all-weather. In addition, its spatial resolution does not decrease with the height of the platform and is thus applicable to a range of important studies. However, existing studies did not address the operational demands of real-time inundation mapping. The direct proof is that no water body product exists for any SAR-based satellites. Then what is missing between science and products? Automation and quality. What makes it so difficult to develop an operational inundation mapping technique based on SAR data? Spectrum-wise, unlike optical water indices such as MNDWI, AWEI etc., where a relative constant threshold may apply across acquisition of images, regions and sensors, the threshold to separate water from non-water pixels in each SAR images has to be individually chosen. The optimization of the threshold is the first obstacle to the automation of the SAR data algorithm. Morphologically, the quality and reliability of the results have been compromised by over-detection caused by smooth surface and shadowing area, the noise-like speckle and under-detection caused by strong-scatter disturbance. In this study, we propose a three-step framework that addresses all aforementioned issues of operational inundation mapping by SAR data. The framework consists of 1) optimization of Wishart distribution parameters of single/dual/fully-polarized SAR data, 2) morphological removal of over-detection, and 3) machine-learning based removal of under-detection. The framework utilizes not only the SAR data, but also the synergy of digital elevation model (DEM), and optical sensor-based products of fine resolution, including the water probability map, land cover classification map (optional), and river width. The framework has been validated throughout multiple areas in different parts of the world using different satellite SAR data and globally available ancillary data products. Therefore, it has the potential to contribute as an operational inundation mapping algorithm to any SAR missions, such as SWOT, ALOS, Sentinel, etc. Selected results using ALOS/PALSAR-1 L-band dual polarized data around the Connecticut River is provided in the attached Figure.

Mitigating illumination gradients in a SAR image based on the image data and antenna beam pattern

DOEpatents

Doerry, Armin W.

2013-04-30

Illumination gradients in a synthetic aperture radar (SAR) image of a target can be mitigated by determining a correction for pixel values associated with the SAR image. This correction is determined based on information indicative of a beam pattern used by a SAR antenna apparatus to illuminate the target, and also based on the pixel values associated with the SAR image. The correction is applied to the pixel values associated with the SAR image to produce corrected pixel values that define a corrected SAR image.

Analysis of wind and wave events at the MIZ based on TerraSAR-X satellite images

NASA Astrophysics Data System (ADS)

Gebhardt, Claus; Bidlot, Jean-Raymond; Jacobsen, Sven; Lehner, Susanne; Pleskachevsky, Andrey; Singha, Suman

2017-04-01

The seasonal opening-up of large expanses of open water in the Beaufort/Chukchi Sea is a phenomenon observed in recent years. The diameter of the open-water area is on the order of 1000 km around the sea ice minimum in summer. Thus, wind events in the area are accompanied by the build-up of sea waves. Significant wave heights of few to several meters may be reached. Under low to moderate winds, the morphology of the MIZ is governed by oceanic forcing. As a result, the MIZ resembles ocean circulation features such as eddies, meanders, etc.. In the case of strong wind events, however, the wind forcing may gain control. We analyse effects related to wind and wave events at the MIZ using TerraSAR-X satellite imagery. Methods such as the retrieval of sea state and wind data by empirical algorithms and automatic sea ice classification are applied. This is facilitated by a series of TerraSAR-X images acquired in support of a cruise of the research vessel R/V Sikuliaq in the Beaufort/Chukchi Sea in autumn 2015. For selected images, the results are presented and compared to numerical model forecasts which were as well part of the cruise support.

Synthetic aperture radar image formation for the moving-target and near-field bistatic cases

NASA Astrophysics Data System (ADS)

Ding, Yu

This dissertation addresses topics in two areas of synthetic aperture radar (SAR) image formation: time-frequency based SAR imaging of moving targets and a fast backprojection (BP) algorithm for near-field bistatic SAR imaging. SAR imaging of a moving target is a challenging task due to unknown motion of the target. We approach this problem in a theoretical way, by analyzing the Wigner-Ville distribution (WVD) based SAR imaging technique. We derive approximate closed-form expressions for the point-target response of the SAR imaging system, which quantify the image resolution, and show how the blurring in conventional SAR imaging can be eliminated, while the target shift still remains. Our analyses lead to accurate prediction of the target position in the reconstructed images. The derived expressions also enable us to further study additional aspects of WVD-based SAR imaging. Bistatic SAR imaging is more involved than the monostatic SAR case, because of the separation of the transmitter and the receiver, and possibly the changing bistatic geometry. For near-field bistatic SAR imaging, we develop a novel fast BP algorithm, motivated by a newly proposed fast BP algorithm in computer tomography. First we show that the BP algorithm is the spatial-domain counterpart of the benchmark o -- k algorithm in bistatic SAR imaging, yet it avoids the frequency-domain interpolation in the o -- k algorithm, which may cause artifacts in the reconstructed image. We then derive the band-limited property for BP methods in both monostatic and bistatic SAR imaging, which is the basis for developing the fast BP algorithm. We compare our algorithm with other frequency-domain based algorithms, and show that it achieves better reconstructed image quality, while having the same computational complexity as that of the frequency-domain based algorithms.

Successive Over-Relaxation Technique for High-Performance Blind Image Deconvolution

DTIC Science & Technology

2015-06-08

deconvolution, space surveillance, Gauss - Seidel iteration 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18, NUMBER OF PAGES 5...sensible approximate solutions to the ill-posed nonlinear inverse problem. These solutions are addresses as fixed points of the iteration which consists in...alternating approximations (AA) for the object and for the PSF performed with a prescribed number of inner iterative descents from trivial (zero

Early breast tumor and late SARS detections using space-variant multispectral infrared imaging at a single pixel

NASA Astrophysics Data System (ADS)

Szu, Harold H.; Buss, James R.; Kopriva, Ivica

2004-04-01

We proposed the physics approach to solve a physical inverse problem, namely to choose the unique equilibrium solution (at the minimum free energy: H= E - ToS, including the Wiener, l.m.s E, and ICA, Max S, as special cases). The "unsupervised classification" presumes that required information must be learned and derived directly and solely from the data alone, in consistence with the classical Duda-Hart ATR definition of the "unlabelled data". Such truly unsupervised methodology is presented for space-variant imaging processing for a single pixel in the real world case of remote sensing, early tumor detections and SARS. The indeterminacy of the multiple solutions of the inverse problem is regulated or selected by means of the absolute minimum of isothermal free energy as the ground truth of local equilibrium condition at the single-pixel foot print.

Improving crop classification through attention to the timing of airborne radar acquisitions

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Protz, R.

1984-01-01

Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

Assessment of Polarimetric SAR Interferometry for Improving Ship Classification based on Simulated Data

PubMed Central

Margarit, Gerard; Mallorqui, Jordi J.

2008-01-01

This paper uses a complete and realistic SAR simulation processing chain, GRECOSAR, to study the potentialities of Polarimetric SAR Interferometry (POLInSAR) in the development of new classification methods for ships. Its high processing efficiency and scenario flexibility have allowed to develop exhaustive scattering studies. The results have revealed, first, vessels' geometries can be described by specific combinations of Permanent Polarimetric Scatterers (PePS) and, second, each type of vessel could be characterized by a particular spatial and polarimetric distribution of PePS. Such properties have been recently exploited to propose a new Vessel Classification Algorithm (VCA) working with POLInSAR data, which, according to several simulation tests, may provide promising performance in real scenarios. Along the paper, explanation of the main steps summarizing the whole research activity carried out with ships and GRECOSAR are provided as well as examples of the main results and VCA validation tests. Special attention will be devoted to the new improvements achieved, which are related to simulations processing a new and highly realistic sea surface model. The paper will show that, for POLInSAR data with fine resolution, VCA can help to classify ships with notable robustness under diverse and adverse observation conditions. PMID:27873954

Comparing the Behavior of Polarimetric SAR Imagery (TerraSAR-X and Radarsat-2) for Automated Sea Ice Classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of single- polarimetric classifiers. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).

Large Oil Spill Classification Using SAR Images Based on Spatial Histogram

NASA Astrophysics Data System (ADS)

Schvartzman, I.; Havivi, S.; Maman, S.; Rotman, S. R.; Blumberg, D. G.

2016-06-01

Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR) is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010). The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA) data.

Discrimination of crop types with TerraSAR-X-derived information

NASA Astrophysics Data System (ADS)

Sonobe, Rei; Tani, Hiroshi; Wang, Xiufeng; Kobayashi, Nobuyuki; Shimamura, Hideki

Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J-M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5-96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications.

Method for removing RFI from SAR images

DOEpatents

Doerry, Armin W.

2003-08-19

A method of removing RFI from a SAR by comparing two SAR images on a pixel by pixel basis and selecting the pixel with the lower magnitude to form a composite image. One SAR image is the conventional image produced by the SAR. The other image is created from phase-history data which has been filtered to have the frequency bands containing the RFI removed.

Controlling Data Collection to Support SAR Image Rotation

DOEpatents

Doerry, Armin W.; Cordaro, J. Thomas; Burns, Bryan L.

2008-10-14

A desired rotation of a synthetic aperture radar (SAR) image can be facilitated by adjusting a SAR data collection operation based on the desired rotation. The SAR data collected by the adjusted SAR data collection operation can be efficiently exploited to form therefrom a SAR image having the desired rotational orientation.

Translating the Science of Alertness and Performance from Laboratory to Field: Using State-of-the-Art Monitoring Imaging and Performance Enhancement Technologies to Improve the Alertness and Safety of the Military and Civilian Workforce

DTIC Science & Technology

2008-06-01

imaging (fMRI) environments, b) custom 32 channel electrode caps for use in fMRI environmentsnew EEG/ EOG signal analysts software, c) ambulatory...personnel 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: REPORT b. ABSTRACT u c. THIS PAGE U 17. LIMITATION OF ABSTRACT SAR 18. NUMBER...digital electroencephalogram (EEG) and electrooculogram ( EOG ) recording systems for ambulatory use as well as for use in functional magnet-resonance

A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions

PubMed Central

Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

2016-01-01

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application. PMID:27924935

A New SAR Image Segmentation Algorithm for the Detection of Target and Shadow Regions.

PubMed

Huang, Shiqi; Huang, Wenzhun; Zhang, Ting

2016-12-07

The most distinctive characteristic of synthetic aperture radar (SAR) is that it can acquire data under all weather conditions and at all times. However, its coherent imaging mechanism introduces a great deal of speckle noise into SAR images, which makes the segmentation of target and shadow regions in SAR images very difficult. This paper proposes a new SAR image segmentation method based on wavelet decomposition and a constant false alarm rate (WD-CFAR). The WD-CFAR algorithm not only is insensitive to the speckle noise in SAR images but also can segment target and shadow regions simultaneously, and it is also able to effectively segment SAR images with a low signal-to-clutter ratio (SCR). Experiments were performed to assess the performance of the new algorithm on various SAR images. The experimental results show that the proposed method is effective and feasible and possesses good characteristics for general application.

Radar image and data fusion for natural hazards characterisation

USGS Publications Warehouse

Lu, Zhong; Dzurisin, Daniel; Jung, Hyung-Sup; Zhang, Jixian; Zhang, Yonghong

2010-01-01

Fusion of synthetic aperture radar (SAR) images through interferometric, polarimetric and tomographic processing provides an all - weather imaging capability to characterise and monitor various natural hazards. This article outlines interferometric synthetic aperture radar (InSAR) processing and products and their utility for natural hazards characterisation, provides an overview of the techniques and applications related to fusion of SAR/InSAR images with optical and other images and highlights the emerging SAR fusion technologies. In addition to providing precise land - surface digital elevation maps, SAR - derived imaging products can map millimetre - scale elevation changes driven by volcanic, seismic and hydrogeologic processes, by landslides and wildfires and other natural hazards. With products derived from the fusion of SAR and other images, scientists can monitor the progress of flooding, estimate water storage changes in wetlands for improved hydrological modelling predictions and assessments of future flood impacts and map vegetation structure on a global scale and monitor its changes due to such processes as fire, volcanic eruption and deforestation. With the availability of SAR images in near real - time from multiple satellites in the near future, the fusion of SAR images with other images and data is playing an increasingly important role in understanding and forecasting natural hazards.

Investigation of Land Subsidence using ALOS PALSAR data: a case study in Mentougou (Beijing, China)

NASA Astrophysics Data System (ADS)

Chen, Jianping; Xiang, Jie; Xie, Shuai; Liu, Jing; Tarolli, Paolo

2017-04-01

Mining activities have been documented for centuries in Mentougou, and land subsidence resulting from mining operations has already been known over the past few decades. However, there has been ongoing concern that excessive groundwater extraction may lead to further subsidence. Therefore it is critical to map the land cover changes to understand the actual impact of these activities. So, the land cover changes from 2006 to 2011 were examined based on multi-source remote sensing imageries( including ALOS and landsat-7) by using object-oriented classifications combined with a decision tree and retrospective approaches. Also, land subsidence in Mentougou between 2006 and 2011 has been mapped using the interferometric synthetic aperture radar (InSAR) time-series analysis with the ALOS L-band SAR data. We processed 14 ascending SAR images during May 2006 to July 2011. Comparison of InSAR measurements with the land cover changes and pre-existing faults suggest that mining activities is the main cause of land subsidence. The land subsidence observed from InSAR data are approximately up to 15 mm/year in open-pit mining area and up to 24 mm/year in underground mining areas. The InSAR result are validated by the ground survey data in several areas, and the comparison between the InSAR result with the mining schedule showed there were some correlations between them. The result underline the potential use of InSAR measurements to provide better investigation for land subsidence, and also suggest that the most influential factors for land subsidence is underground coal mine.

Backscatter Analysis Using Multi-Temporal SENTINEL-1 SAR Data for Crop Growth of Maize in Konya Basin, Turkey

NASA Astrophysics Data System (ADS)

Abdikan, S.; Sekertekin, A.; Ustunern, M.; Balik Sanli, F.; Nasirzadehdizaji, R.

2018-04-01

Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80 % accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

Multitask saliency detection model for synthetic aperture radar (SAR) image and its application in SAR and optical image fusion

NASA Astrophysics Data System (ADS)

Liu, Chunhui; Zhang, Duona; Zhao, Xintao

2018-03-01

Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.

Local region power spectrum-based unfocused ship detection method in synthetic aperture radar images

NASA Astrophysics Data System (ADS)

Wei, Xiangfei; Wang, Xiaoqing; Chong, Jinsong

2018-01-01

Ships on synthetic aperture radar (SAR) images will be severely defocused and their energy will disperse into numerous resolution cells under long SAR integration time. Therefore, the image intensity of ships is weak and sometimes even overwhelmed by sea clutter on SAR image. Consequently, it is hard to detect the ships from SAR intensity images. A ship detection method based on local region power spectrum of SAR complex image is proposed. Although the energies of the ships are dispersed on SAR intensity images, their spectral energies are rather concentrated or will cause the power spectra of local areas of SAR images to deviate from that of sea surface background. Therefore, the key idea of the proposed method is to detect ships via the power spectra distortion of local areas of SAR images. The local region power spectrum of a moving target on SAR image is analyzed and the way to obtain the detection threshold through the probability density function (pdf) of the power spectrum is illustrated. Numerical P- and L-band airborne SAR ocean data are utilized and the detection results are also illustrated. Results show that the proposed method can well detect the unfocused ships, with a detection rate of 93.6% and a false-alarm rate of 8.6%. Moreover, by comparing with some other algorithms, it indicates that the proposed method performs better under long SAR integration time. Finally, the applicability of the proposed method and the way of parameters selection are also discussed.

SAR image registration based on Susan algorithm

NASA Astrophysics Data System (ADS)

Wang, Chun-bo; Fu, Shao-hua; Wei, Zhong-yi

2011-10-01

Synthetic Aperture Radar (SAR) is an active remote sensing system which can be installed on aircraft, satellite and other carriers with the advantages of all day and night and all-weather ability. It is the important problem that how to deal with SAR and extract information reasonably and efficiently. Particularly SAR image geometric correction is the bottleneck to impede the application of SAR. In this paper we introduces image registration and the Susan algorithm knowledge firstly, then introduces the process of SAR image registration based on Susan algorithm and finally presents experimental results of SAR image registration. The Experiment shows that this method is effective and applicable, no matter from calculating the time or from the calculation accuracy.

Large Scale Crop Mapping in Ukraine Using Google Earth Engine

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Spaceborne SAR Imaging Algorithm for Coherence Optimized.

PubMed

Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun

2016-01-01

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application.

Spaceborne SAR Imaging Algorithm for Coherence Optimized

PubMed Central

Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun

2016-01-01

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application. PMID:26871446

The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data

NASA Astrophysics Data System (ADS)

Huang, G.; Sun, Y.; Zhao, Z.

2018-04-01

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

BOREAS RSS-15 SIR-C and Landsat TM Biomass and Landcover Maps of the NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Ranson, K. Jon

2000-01-01

As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C, X-SAR, and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on 02-Sep-1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Passive, Highly-Sensitive, Room-Temperature Magnetic Field Sensors and Arrays for Detection and Imaging of Hidden Threats in Urban Environments

DTIC Science & Technology

2012-07-01

units made from the various sensors. This was because the different types of ME laminates have different electrical properties ( resistance and...DC resistance of a sensor (Rdc) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT: SAR 18. NUMBER OF PAGES 338 19a. NAME OF...3.3.6. Electric -field tuning effect ..................................................................70 A.3.4. Dielectric loss noise reduction

Synthetic Aperture Radar (SAR) data processing

NASA Technical Reports Server (NTRS)

Beckner, F. L.; Ahr, H. A.; Ausherman, D. A.; Cutrona, L. J.; Francisco, S.; Harrison, R. E.; Heuser, J. S.; Jordan, R. L.; Justus, J.; Manning, B.

1978-01-01

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed.

Forest above ground biomass estimation and forest/non-forest classification for Odisha, India, using L-band Synthetic Aperture Radar (SAR) data

NASA Astrophysics Data System (ADS)

Suresh, M.; Kiran Chand, T. R.; Fararoda, R.; Jha, C. S.; Dadhwal, V. K.

2014-11-01

Tropical forests contribute to approximately 40 % of the total carbon found in terrestrial biomass. In this context, forest/non-forest classification and estimation of forest above ground biomass over tropical regions are very important and relevant in understanding the contribution of tropical forests in global biogeochemical cycles, especially in terms of carbon pools and fluxes. Information on the spatio-temporal biomass distribution acts as a key input to Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) action plans. This necessitates precise and reliable methods to estimate forest biomass and to reduce uncertainties in existing biomass quantification scenarios. The use of backscatter information from a host of allweather capable Synthetic Aperture Radar (SAR) systems during the recent past has demonstrated the potential of SAR data in forest above ground biomass estimation and forest / nonforest classification. In the present study, Advanced Land Observing Satellite (ALOS) / Phased Array L-band Synthetic Aperture Radar (PALSAR) data along with field inventory data have been used in forest above ground biomass estimation and forest / non-forest classification over Odisha state, India. The ALOSPALSAR 50 m spatial resolution orthorectified and radiometrically corrected HH/HV dual polarization data (digital numbers) for the year 2010 were converted to backscattering coefficient images (Schimada et al., 2009). The tree level measurements collected during field inventory (2009-'10) on Girth at Breast Height (GBH at 1.3 m above ground) and height of all individual trees at plot (plot size 0.1 ha) level were converted to biomass density using species specific allometric equations and wood densities. The field inventory based biomass estimations were empirically integrated with ALOS-PALSAR backscatter coefficients to derive spatial forest above ground biomass estimates for the study area. Further, The Support Vector Machines (SVM) based Radial Basis Function classification technique was employed to carry out binary (forest-non forest) classification using ALOSPALSAR HH and HV backscatter coefficient images and field inventory data. The textural Haralick's Grey Level Cooccurrence Matrix (GLCM) texture measures are determined on HV backscatter image for Odisha, for the year 2010. PALSAR HH, HV backscatter coefficient images, their difference (HHHV) and HV backscatter coefficient based eight textural parameters (Mean, Variance, Dissimilarity, Contrast, Angular second moment, Homogeneity, Correlation and Contrast) are used as input parameters for Support Vector Machines (SVM) tool. Ground based inputs for forest / non-forest were taken from field inventory data and high resolution Google maps. Results suggested significant relationship between HV backscatter coefficient and field based biomass (R2 = 0.508, p = 0.55) compared to HH with biomass values ranging from 5 to 365 t/ha. The spatial variability of biomass with reference to different forest types is in good agreement. The forest / nonforest classified map suggested a total forest cover of 50214 km2 with an overall accuracy of 92.54 %. The forest / non-forest information derived from the present study showed a good spatial agreement with the standard forest cover map of Forest Survey of India (FSI) and corresponding published area of 50575 km2. Results are discussed in the paper.

On the SAR derived alert in the detection of oil spills according to the analysis of the EGEMP.

PubMed

Ferraro, Guido; Baschek, Björn; de Montpellier, Geraldine; Njoten, Ove; Perkovic, Marko; Vespe, Michele

2010-01-01

Satellite services that deliver information about possible oil spills at sea currently use different labels of "confidence" to describe the detections based on radar image processing. A common approach is to use a classification differentiating between low, medium and high levels of confidence. There is an ongoing discussion on the suitability of the existing classification systems of possible oil spills detected by radar satellite images with regard to the relevant significance and correspondence to user requirements. This paper contains a basic analysis of user requirements, current technical possibilities of satellite services as well as proposals for a redesign of the classification system as an evolution towards a more structured alert system. This research work offers a first review of implemented methodologies for the categorisation of detected oil spills, together with the proposal of explorative ideas evaluated by the European Group of Experts on satellite Monitoring of sea-based oil Pollution (EGEMP). Copyright 2009 Elsevier Ltd. All rights reserved.

77 FR 38285 - Pesticide Products; Registration Applications

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-27

... (SAR) Inducer with Methyl Jasmonate at 98.7%. Proposed classification/use: Manufacturing use product..., Ames, IA 50010. Product name: Scimitar. Active ingredient: Biochemical SAR Inducer with Methyl...

Brain MR imaging at ultra-low radiofrequency power.

PubMed

Sarkar, Subhendra N; Alsop, David C; Madhuranthakam, Ananth J; Busse, Reed F; Robson, Philip M; Rofsky, Neil M; Hackney, David B

2011-05-01

To explore the lower limits for radiofrequency (RF) power-induced specific absorption rate (SAR) achievable at 1.5 T for brain magnetic resonance (MR) imaging without loss of tissue signal or contrast present in high-SAR clinical imaging in order to create a potentially viable MR method at ultra-low RF power to image tissues containing implanted devices. An institutional review board-approved HIPAA-compliant prospective MR study design was used, with written informed consent from all subjects prior to MR sessions. Seven healthy subjects were imaged prospectively at 1.5 T with ultra-low-SAR optimized three-dimensional (3D) fast spin-echo (FSE) and fluid-attenuated inversion-recovery (FLAIR) T2-weighted sequences and an ultra-low-SAR 3D spoiled gradient-recalled acquisition in the steady state T1-weighted sequence. Corresponding high-SAR two-dimensional (2D) clinical sequences were also performed. In addition to qualitative comparisons, absolute signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) for multicoil, parallel imaging acquisitions were generated by using a Monte Carlo method for quantitative comparison between ultra-low-SAR and high-SAR results. There were minor to moderate differences in the absolute tissue SNR and CNR values and in qualitative appearance of brain images obtained by using ultra-low-SAR and high-SAR techniques. High-SAR 2D T2-weighted imaging produced slightly higher SNR, while ultra-low-SAR 3D technique not only produced higher SNR for T1-weighted and FLAIR images but also higher CNRs for all three sequences for most of the brain tissues. The 3D techniques adopted here led to a decrease in the absorbed RF power by two orders of magnitude at 1.5 T, and still the image quality was preserved within clinically acceptable imaging times. RSNA, 2011

Image based SAR product simulation for analysis

NASA Technical Reports Server (NTRS)

Domik, G.; Leberl, F.

1987-01-01

SAR product simulation serves to predict SAR image gray values for various flight paths. Input typically consists of a digital elevation model and backscatter curves. A new method is described of product simulation that employs also a real SAR input image for image simulation. This can be denoted as 'image-based simulation'. Different methods to perform this SAR prediction are presented and advantages and disadvantages discussed. Ascending and descending orbit images from NASA's SIR-B experiment were used for verification of the concept: input images from ascending orbits were converted into images from a descending orbit; the results are compared to the available real imagery to verify that the prediction technique produces meaningful image data.

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points - A Review.

PubMed

Zou, Weibao; Li, Yan; Li, Zhilin; Ding, Xiaoli

2009-01-01

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram.

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

PubMed Central

Zou, Weibao; Li, Yan; Li, Zhilin; Ding, Xiaoli

2009-01-01

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram. PMID:22399966

Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

NASA Astrophysics Data System (ADS)

Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

2016-02-01

Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

Earthquake damage mapping by using remotely sensed data: the Haiti case study

NASA Astrophysics Data System (ADS)

Romaniello, Vito; Piscini, Alessandro; Bignami, Christian; Anniballe, Roberta; Stramondo, Salvatore

2017-01-01

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

NASA Astrophysics Data System (ADS)

Rezaeian, A.; Homayouni, S.; Safari, A.

2015-12-01

Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

Analysis of data acquired by synthetic aperture radar over Dade County, Florida, and Acadia Parish, Louisiana

NASA Technical Reports Server (NTRS)

Wu, S. T.

1983-01-01

Results of digital processing of airborne X-band synthetic aperture radar (SAR) data acquired over Dade County, Florida, and Acadia Parish, Louisiana are presented. The goal was to investigate the utility of SAR data for land cover mapping and area estimation under the AgRISTARS Domestic Crops and Land Cover Project. In the case of the Acadia Paris study area, LANDSAT multispectral scanner (MSS) data were also used to form a combined SAR and MSS data set. The results of accuracy evaluation for the SAR, MSS, and SAR/MSS data using supervised classification show that the combined SAR/MSS data set results in an improved classification accuracy of the five land cover classes as compared with SAR-only and MSS-only data sets. In the case of the Dade County study area, the results indicate that both HH and VV polarization data are highly responsive to the row orientation of the row crop but not to the specific vegetation which forms the row structure. On the other hand, the HV polarization data are relatively insensitive to the orientation of row crop. Therefore, the HV polarization data may be used to discriminate the specific vegetation that forms the row structure.

Structural classification of marshes with Polarimetric SAR highlighting the temporal mapping of marshes exposed to oil

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, Amina; Jones, Cathleen E.

2015-01-01

Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC) correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.             

SAR target recognition using behaviour library of different shapes in different incidence angles and polarisations

NASA Astrophysics Data System (ADS)

Fallahpour, Mojtaba Behzad; Dehghani, Hamid; Jabbar Rashidi, Ali; Sheikhi, Abbas

2018-05-01

Target recognition is one of the most important issues in the interpretation of the synthetic aperture radar (SAR) images. Modelling, analysis, and recognition of the effects of influential parameters in the SAR can provide a better understanding of the SAR imaging systems, and therefore facilitates the interpretation of the produced images. Influential parameters in SAR images can be divided into five general categories of radar, radar platform, channel, imaging region, and processing section, each of which has different physical, structural, hardware, and software sub-parameters with clear roles in the finally formed images. In this paper, for the first time, a behaviour library that includes the effects of polarisation, incidence angle, and shape of targets, as radar and imaging region sub-parameters, in the SAR images are extracted. This library shows that the created pattern for each of cylindrical, conical, and cubic shapes is unique, and due to their unique properties these types of shapes can be recognised in the SAR images. This capability is applied to data acquired with the Canadian RADARSAT1 satellite.

Perennial Polyculture Farming: Seeds of Another Agricultural Revolution?

DTIC Science & Technology

2007-01-01

CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 29 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b ...land/nri01/nri01eros.html. 6 See Judith D. Soule and Jon K . Piper, Farming in Nature’s Image, Island Press, Washington, D.C., 1992, p. 13...Dependance on Natural Ecocsystems, Island Press, Washington, D.C., 1997; B . A. Stein et al., Precious Heritage: The Status of Biodiversity in the

Comparison and Analysis of Geometric Correction Models of Spaceborne SAR

PubMed Central

Jiang, Weihao; Yu, Anxi; Dong, Zhen; Wang, Qingsong

2016-01-01

Following the development of synthetic aperture radar (SAR), SAR images have become increasingly common. Many researchers have conducted large studies on geolocation models, but little work has been conducted on the available models for the geometric correction of SAR images of different terrain. To address the terrain issue, four different models were compared and are described in this paper: a rigorous range-doppler (RD) model, a rational polynomial coefficients (RPC) model, a revised polynomial (PM) model and an elevation derivation (EDM) model. The results of comparisons of the geolocation capabilities of the models show that a proper model for a SAR image of a specific terrain can be determined. A solution table was obtained to recommend a suitable model for users. Three TerraSAR-X images, two ALOS-PALSAR images and one Envisat-ASAR image were used for the experiment, including flat terrain and mountain terrain SAR images as well as two large area images. Geolocation accuracies of the models for different terrain SAR images were computed and analyzed. The comparisons of the models show that the RD model was accurate but was the least efficient; therefore, it is not the ideal model for real-time implementations. The RPC model is sufficiently accurate and efficient for the geometric correction of SAR images of flat terrain, whose precision is below 0.001 pixels. The EDM model is suitable for the geolocation of SAR images of mountainous terrain, and its precision can reach 0.007 pixels. Although the PM model does not produce results as precise as the other models, its efficiency is excellent and its potential should not be underestimated. With respect to the geometric correction of SAR images over large areas, the EDM model has higher accuracy under one pixel, whereas the RPC model consumes one third of the time of the EDM model. PMID:27347973

Synthetic aperture radar target detection, feature extraction, and image formation techniques

NASA Technical Reports Server (NTRS)

Li, Jian

1994-01-01

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes

NASA Astrophysics Data System (ADS)

Braun, A.; Hochschild, V.

2015-04-01

Over 15 million people were officially considered as refugees in the year 2012 and another 28 million as internally displaced people (IDPs). Natural disasters, climatic and environmental changes, violent regional conflicts and population growth force people to migrate in all parts of this world. This trend is likely to continue in the near future, as political instabilities increase and land degradation progresses. EO4HumEn aims at developing operational services to support humanitarian operations during crisis situations by means of dedicated geo-spatial information products derived from Earth observation and GIS data. The goal is to develop robust, automated methods of image analysis routines for population estimation, identification of potential groundwater extraction sites and monitoring the environmental impact of refugee/IDP camps. This study investigates the combination of satellite SAR data with optical sensors and elevation information for the assessment of the environmental conditions around refugee camps. In order to estimate their impact on land degradation, land cover classifications are required which target dynamic landscapes. We performed a land use / land cover classification based on a random forest algorithm and 39 input prediction rasters based on Landsat 8 data and additional layers generated from radar texture and elevation information. The overall accuracy was 92.9 %, while optical data had the highest impact on the final classification. By analysing all combinations of the three input datasets we additionally estimated their impact on single classification outcomes and land cover classes.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

NASA Astrophysics Data System (ADS)

Vickers, H.; Eckerstorfer, M.; Malnes, E.; Larsen, Y.; Hindberg, H.

2016-11-01

Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide.

Synergy of Optical and SAR Data for Mapping and Monitoring Mangroves

NASA Astrophysics Data System (ADS)

Monzon, A. K.; Reyes, S. R.; Veridiano, R. K.; Tumaneng, R.; De Alban, J. D.

2016-06-01

Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.

Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring

NASA Astrophysics Data System (ADS)

Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo

2013-12-01

During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST software, with new detection, filtering and classification algorithms. Particularly, dedicated filtering algorithm development based on Wavelet filtering was exploited for the improvement of oil spill detection and classification. In this work we present the functionalities of the developed software and the main results in support of the developed algorithm validity.

Interferometric synthetic aperture radar: Building tomorrow's tools today

USGS Publications Warehouse

Lu, Zhong

2006-01-01

A synthetic aperture radar (SAR) system transmits electromagnetic (EM) waves at a wavelength that can range from a few millimeters to tens of centimeters. The radar wave propagates through the atmosphere and interacts with the Earth’s surface. Part of the energy is reflected back to the SAR system and recorded. Using a sophisticated image processing technique, called SAR processing (Curlander and McDonough, 1991), both the intensity and phase of the reflected (or backscattered) signal of each ground resolution element (a few meters to tens of meters) can be calculated in the form of a complex-valued SAR image representing the reflectivity of the ground surface. The amplitude or intensity of the SAR image is determined primarily by terrain slope, surface roughness, and dielectric constants, whereas the phase of the SAR image is determined primarily by the distance between the satellite antenna and the ground targets, slowing of the signal by the atmosphere, and the interaction of EM waves with ground surface. Interferometric SAR (InSAR) imaging, a recently developed remote sensing technique, utilizes the interaction of EM waves, referred to as interference, to measure precise distances. Very simply, InSAR involves the use of two or more SAR images of the same area to extract landscape topography and its deformation patterns.

Making SAR Data Accessible - ASF's ALOS PALSAR Radiometric Terrain Correction Project

NASA Astrophysics Data System (ADS)

Meyer, F. J.; Arko, S. A.; Gens, R.

2015-12-01

While SAR data have proven valuable for a wide range of geophysical research questions, so far, largely only the SAR-educated science communities have been able to fully exploit the information content of internationally available SAR archives. The main issues that have been preventing a more widespread utilization of SAR are related to (1) the diversity and complexity of SAR data formats, (2) the complexity of the processing flows needed to extract geophysical information from SAR, (3) the lack of standardization and automation of these processing flows, and (4) the often ignored geocoding procedures, leaving the data in image coordinate space. In order to improve upon this situation, ASF's radiometric terrain-correction (RTC) project is generating uniformly formatted and easily accessible value-added products from the ASF Distributed Active Archive Center's (DAAC) five-year archive of JAXA's ALOS PALSAR sensor. Specifically, the project applies geometric and radiometric corrections to SAR data to allow for an easy and direct combination of obliquely acquired SAR data with remote sensing imagery acquired in nadir observation geometries. Finally, the value-added data is provided to the user in the broadly accepted Geotiff format, in order to support the easy integration of SAR data into GIS environments. The goal of ASF's RTC project is to make SAR data more accessible and more attractive to the broader SAR applications community, especially to those users that currently have limited SAR expertise. Production of RTC products commenced October 2014 and will conclude late in 2015. As of July 2015, processing of 71% of ASF's ALOS PALSAR archive was completed. Adding to the utility of this dataset are recent changes to the data access policy that allow the full-resolution RTC products to be provided to the public, without restriction. In this paper we will introduce the processing flow that was developed for the RTC project and summarize the calibration and validation procedures that were implemented to determine and monitor system performance. The paper will also show the current progress of RTC processing, provide examples of generated data sets, and demonstrate the benefit of the RTC archives for applications such as land-use classification and change detection.

Satellite SAR geocoding with refined RPC model

NASA Astrophysics Data System (ADS)

Zhang, Lu; Balz, Timo; Liao, Mingsheng

2012-04-01

Recent studies have proved that the Rational Polynomial Camera (RPC) model is able to act as a reliable replacement of the rigorous Range-Doppler (RD) model for the geometric processing of satellite SAR datasets. But its capability in absolute geolocation of SAR images has not been evaluated quantitatively. Therefore, in this article the problems of error analysis and refinement of SAR RPC model are primarily investigated to improve the absolute accuracy of SAR geolocation. Range propagation delay and azimuth timing error are identified as two major error sources for SAR geolocation. An approach based on SAR image simulation and real-to-simulated image matching is developed to estimate and correct these two errors. Afterwards a refined RPC model can be built from the error-corrected RD model and then used in satellite SAR geocoding. Three experiments with different settings are designed and conducted to comprehensively evaluate the accuracies of SAR geolocation with both ordinary and refined RPC models. All the experimental results demonstrate that with RPC model refinement the absolute location accuracies of geocoded SAR images can be improved significantly, particularly in Easting direction. In another experiment the computation efficiencies of SAR geocoding with both RD and RPC models are compared quantitatively. The results show that by using the RPC model such efficiency can be remarkably improved by at least 16 times. In addition the problem of DEM data selection for SAR image simulation in RPC model refinement is studied by a comparative experiment. The results reveal that the best choice should be using the proper DEM datasets of spatial resolution comparable to that of the SAR images.

A discussion on the use of X-band SAR images in marine applications

NASA Astrophysics Data System (ADS)

Schiavulli, D.; Sorrentino, A.; Migliaccio, M.

2012-10-01

The Synthetic Aperture Radar (SAR) is able to generate images of the sea surface that can be exploited to extract geophysical information of environmental interest. In order to enhance the operational use of these data in the marine applications the revisit time is to be improved. This goal can be achieved by using SAR virtual or real constellations and/or exploiting new antenna technologies that allow huge swath and fine resolution. Within this framework, the presence of the Italian and German X-band SAR constellations is of special interest while the new SAR technologies are not nowadays operated. Although SAR images are considered to be independent of weather conditions, this is only partially true at higher frequencies, e.g. X-band. In fact, observations can present signature corresponding to high intensity precipitating clouds, i.e. rain cells. Further, ScanSAR images may be characterized by the presence of processing artifacts, called scalloping, that corrupt image interpretation. In this paper we review these key facts that are at the basis of an effective use of X-band SAR images for marine applications.

Composite SAR imaging using sequential joint sparsity

NASA Astrophysics Data System (ADS)

Sanders, Toby; Gelb, Anne; Platte, Rodrigo B.

2017-06-01

This paper investigates accurate and efficient ℓ1 regularization methods for generating synthetic aperture radar (SAR) images. Although ℓ1 regularization algorithms are already employed in SAR imaging, practical and efficient implementation in terms of real time imaging remain a challenge. Here we demonstrate that fast numerical operators can be used to robustly implement ℓ1 regularization methods that are as or more efficient than traditional approaches such as back projection, while providing superior image quality. In particular, we develop a sequential joint sparsity model for composite SAR imaging which naturally combines the joint sparsity methodology with composite SAR. Our technique, which can be implemented using standard, fractional, or higher order total variation regularization, is able to reduce the effects of speckle and other noisy artifacts with little additional computational cost. Finally we show that generalizing total variation regularization to non-integer and higher orders provides improved flexibility and robustness for SAR imaging.

Oil Spill detection off the eastern coast of India using Sentinel-1 dual polarimeteric SAR imagery

NASA Astrophysics Data System (ADS)

De, S.; Bhattacharya, A.; Gautam, R.

2017-12-01

Among the various Earth observing sensors, the spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) is considered as one of the most flexible and has been widely used in disaster response applications due to its all-weather illumination independent capability. Sentinel-1 is a two-satellite constellation with a C-band polarimetric Synthetic Aperture Radar (PolSAR) sensor, which provides global coverage with a 12-day repeat cycle in the same acquisition geometry, and the possibility of a 3-day repeat imaging in independent geometry, making it ideal for operational geodynamic monitoring. The proposed study aims to detect changes in polarimetric parameters associated with an oil spill event occurred off the coast of Ennore, Tamil Nadu, India (13.228° N Lon: 80.363° E ) on 28 January 2017. The initial spill covered an area of approximately 7.26 sq. km, spreading to an area of 12.56 sq. km. in a single day. The spread was mainly attributed to the strong shore parallel southerly current. To this end, two PolSAR images were used from before and after the event acquired on 17 and 29 January 2017, respectively in dual-polarimetric (VV,VH) interferometric wide swath mode and with same acquisition geometry. The images are first calibrated, co-registered and terrain corrected to make them comparable in a geo-coordinate framework. A refined Lee speckle filter is applied with a 5x5 window to reduce the influence of coherent speckle. The pair of images are then used to generate a hellinger distance based change index corresponding to each polarimetric channel. The indices are then applied as input to a Convolutional Neural Network (CNN) with the objective of discriminating the areas corresponding to changes due to the oil spill, movement of ships, rough ocean surface etc. The final result is a binary change detection map of the oil spill area. The results obtained were compared with that obtained by survey of the affected oil spill area by the Integrated Coastal and Marine Area Management (ICMAM) Project. A close agreement was found with the results of our SAR-image based classification technique and that published in the event report by the same agency.

Improving the extraction of crisis information in the context of flood, fire, and landslide rapid mapping using SAR and optical remote sensing data

NASA Astrophysics Data System (ADS)

Martinis, Sandro; Clandillon, Stephen; Twele, André; Huber, Claire; Plank, Simon; Maxant, Jérôme; Cao, Wenxi; Caspard, Mathilde; May, Stéphane

2016-04-01

Optical and radar satellite remote sensing have proven to provide essential crisis information in case of natural disasters, humanitarian relief activities and civil security issues in a growing number of cases through mechanisms such as the Copernicus Emergency Management Service (EMS) of the European Commission or the International Charter 'Space and Major Disasters'. The aforementioned programs and initiatives make use of satellite-based rapid mapping services aimed at delivering reliable and accurate crisis information after natural hazards. Although these services are increasingly operational, they need to be continuously updated and improved through research and development (R&D) activities. The principal objective of ASAPTERRA (Advancing SAR and Optical Methods for Rapid Mapping), the ESA-funded R&D project being described here, is to improve, automate and, hence, speed-up geo-information extraction procedures in the context of natural hazards response. This is performed through the development, implementation, testing and validation of novel image processing methods using optical and Synthetic Aperture Radar (SAR) data. The methods are mainly developed based on data of the German radar satellites TerraSAR-X and TanDEM-X, the French satellite missions Pléiades-1A/1B as well as the ESA missions Sentinel-1/2 with the aim to better characterize the potential and limitations of these sensors and their synergy. The resulting algorithms and techniques are evaluated in real case applications during rapid mapping activities. The project is focussed on three types of natural hazards: floods, landslides and fires. Within this presentation an overview of the main methodological developments in each topic is given and demonstrated in selected test areas. The following developments are presented in the context of flood mapping: a fully automated Sentinel-1 based processing chain for detecting open flood surfaces, a method for the improved detection of flooded vegetation in Sentinel-1data using Entropy/Alpha decomposition, unsupervised Wishart Classification, and object-based post-classification as well as semi-automatic approaches for extracting inundated areas and flood traces in rural and urban areas from VHR and HR optical imagery using machine learning techniques. Methodological developments related to fires are the implementation of fast and robust methods for mapping burnt scars using change detection procedures using SAR (Sentinel-1, TerraSAR-X) and HR optical (e.g. SPOT, Sentinel-2) data as well as the extraction of 3D surface and volume change information from Pléiades stereo-pairs. In the context of landslides, fast and transferable change detection procedures based on SAR (TerraSAR-X) and optical (SPOT) data as well methods for extracting the extent of landslides only based on polarimetric VHR SAR (TerraSAR-X) data are presented.

25m-resolution Global Mosaic and Forest/Non-Forest map using PALSAR-2 data set

NASA Astrophysics Data System (ADS)

Itoh, T.; Shimada, M.; Motooka, T.; Hayashi, M.; Tadono, T.; DAN, R.; Isoguchi, O.; Yamanokuchi, T.

2017-12-01

A continuous observation of forests is important as information necessary for monitoring deforestation, climate change and environmental changes i.e. Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+). Japan Aerospace Exploration Agency (JAXA) is conducting research on forest monitoring using satellite-based L-Band Synthetic Aperture Radars (SARs) continuously. Using the FBD (Fine Beam Dual polarizations) data of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS), JAXA created the global 25 m-resolution mosaic images and the Forest/Non-Forest (FNF) maps dataset for forest monitoring. SAR can monitor forest areas under all weather conditions, and L-band is highly sensitive to forests and their changes, therefore it is suitable for forest observation. JAXA also created the global 25 m mosaics and FNF maps using ALOS-2/PALSAR-2 launched on 2014 as a successor to ALOS. FNF dataset by PALSAR and PALSAR-2 covers from 2007 to 2010, and from 2015 to 2016, respectively. Therefore, it is possible to monitor forest changes during approx. 10 years. The classification method is combination of the object-based classification and the thresholding of HH and HV polarized images, and the result of FNF was compared with Forest Resource Assessment (FRA, developed by FAO) and their inconsistency is less than 10 %. Also, by comparing with the optical image of Google Earth, rate of coincidence was 80 % or more. We will create PALSAR-2 global mosaics and FNF dataset continuously to contribute global forest monitoring.

Despeckling Polsar Images Based on Relative Total Variation Model

NASA Astrophysics Data System (ADS)

Jiang, C.; He, X. F.; Yang, L. J.; Jiang, J.; Wang, D. Y.; Yuan, Y.

2018-04-01

Relatively total variation (RTV) algorithm, which can effectively decompose structure information and texture in image, is employed in extracting main structures of the image. However, applying the RTV directly to polarimetric SAR (PolSAR) image filtering will not preserve polarimetric information. A new RTV approach based on the complex Wishart distribution is proposed considering the polarimetric properties of PolSAR. The proposed polarization RTV (PolRTV) algorithm can be used for PolSAR image filtering. The L-band Airborne SAR (AIRSAR) San Francisco data is used to demonstrate the effectiveness of the proposed algorithm in speckle suppression, structural information preservation, and polarimetric property preservation.

Colorizing SENTINEL-1 SAR Images Using a Variational Autoencoder Conditioned on SENTINEL-2 Imagery

NASA Astrophysics Data System (ADS)

Schmitt, M.; Hughes, L. H.; Körner, M.; Zhu, X. X.

2018-05-01

In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore, we will investigate if the low-level representations learned intrinsically by the deep network can be used for SAR image interpretation in an end-to-end manner.

Extracting Urban Morphology for Atmospheric Modeling from Multispectral and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Wittke, S.; Karila, K.; Puttonen, E.; Hellsten, A.; Auvinen, M.; Karjalainen, M.

2017-05-01

This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1) Digital Elevation Model (DEM) and 2) land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91 %. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP).

Pre-Processes for Urban Areas Detection in SAR Images

NASA Astrophysics Data System (ADS)

Altay Açar, S.; Bayır, Ş.

2017-11-01

In this study, pre-processes for urban areas detection in synthetic aperture radar (SAR) images are examined. These pre-processes are image smoothing, thresholding and white coloured regions determination. Image smoothing is carried out to remove noises then thresholding is applied to obtain binary image. Finally, candidate urban areas are detected by using white coloured regions determination. All pre-processes are applied by utilizing the developed software. Two different SAR images which are acquired by TerraSAR-X are used in experimental study. Obtained results are shown visually.

C-band RISAT-1 imagery for geospatial mapping of cryospheric surface features in the Antarctic environment

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Panditrao, Satej N.; Luis, Alvarinho J.

2016-05-01

Cryospheric surface feature classification is one of the widely used applications in the field of polar remote sensing. Precise surface feature maps derived from remotely sensed imageries are the major requirement for many geoscientific applications in polar regions. The present study explores the capabilities of C-band dual polarimetric (HH & HV) SAR imagery from Indian Radar Imaging Satellite (RISAT-1) for land cryospheric surface feature mapping. The study areas selected for the present task were Larsemann Hills and Schirmacher Oasis, East Antarctica. RISAT-1 Fine Resolution STRIPMAP (FRS-1) mode data with 3-m spatial resolution was used in the present research attempt. In order to provide additional context to the amount of information in dual polarized RISAT-1 SAR data, a band HH+HV was introduced to make use of the original two polarizations. In addition to the data calibration, transformed divergence (TD) procedure was performed for class separability analysis to evaluate the quality of the statistics before image classification. For most of the class pairs the TD values were comparable, which indicated that the classes have good separability. Fuzzy and Artificial Neural Network classifiers were implemented and accuracy was checked. Nonparametric classifier Support Vector Machine (SVM) was also used to classify RISAT-1 data with an optimized polarization combination into three land-cover classes consisting of sea ice/snow/ice, rocks/landmass, and lakes/waterbodies. This study demonstrates that C-band FRS1 image mode data from the RISAT-1 mission can be exploited to identify, map and monitor land cover features in the polar regions, even during dark winter period. For better landcover classification and analysis, hybrid polarimetric data (cFRS-1 mode) from RISAT-1, which incorporates phase information, unlike the dual-pol linear (HH, HV) can be used for obtaining better polarization signatures.

Probing/Manipulating the Interfacial Atomic Bonding between High k Dielectrics and InGaAs for Ultimate CMOS

DTIC Science & Technology

2015-04-24

region of n-In0.53Ga0.47As MOSCAP. 15. SUBJECT TERMS CMOS, Magneto-optical imaging , Nanotechnology, Indium Gallium Arsenide 16...Nanotechnology, Indium Gallium Arsenide 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 11 19a...more accessible to water vapor than it is in the complete TEMAHf molecule. There it is surrounded by 8 aliphatic methyl and ethyl groups with a total of

Analysis of ROC on chest direct digital radiography (DR) after image processing in diagnosis of SARS

NASA Astrophysics Data System (ADS)

Lv, Guozheng; Lan, Rihui; Zeng, Qingsi; Zheng, Zhong

2004-05-01

The Severe Acute Respiratory Syndrome (SARS, also called Infectious Atypical Pneumonia), which initially broke out in late 2002, has threatened the public"s health seriously. How to confirm the patients contracting SARS becomes an urgent issue in diagnosis. This paper intends to evaluate the importance of Image Processing in the diagnosis on SARS at the early stage. Receiver Operating Characteristics (ROC) analysis has been employed in this study to compare the value of DR images in the diagnosis of SARS patients before and after image processing by Symphony Software supplied by E-Com Technology Ltd., and DR image study of 72 confirmed or suspected SARS patients were reviewed respectively. All the images taken from the studied patients were processed by Symphony. Both the original and processed images were taken into ROC analysis, based on which the ROC graph for each group of images has been produced as described below: For processed images: a = 1.9745, b = 1.4275, SA = 0.8714; For original images: a = 0.9066, b = 0.8310, SA = 0.7572; (a - intercept, b - slop, SA - Area below the curve). The result shows significant difference between the original images and processed images (P<0.01). In summary, the images processed by Symphony are superior to the original ones in detecting the opacity lesion, and increases the accuracy of SARS diagnosis.

An Improved Method of AGM for High Precision Geolocation of SAR Images

NASA Astrophysics Data System (ADS)

Zhou, G.; He, C.; Yue, T.; Huang, W.; Huang, Y.; Li, X.; Chen, Y.

2018-05-01

In order to take full advantage of SAR images, it is necessary to obtain the high precision location of the image. During the geometric correction process of images, to ensure the accuracy of image geometric correction and extract the effective mapping information from the images, precise image geolocation is important. This paper presents an improved analytical geolocation method (IAGM) that determine the high precision geolocation of each pixel in a digital SAR image. This method is based on analytical geolocation method (AGM) proposed by X. K. Yuan aiming at realizing the solution of RD model. Tests will be conducted using RADARSAT-2 SAR image. Comparing the predicted feature geolocation with the position as determined by high precision orthophoto, results indicate an accuracy of 50m is attainable with this method. Error sources will be analyzed and some recommendations about improving image location accuracy in future spaceborne SAR's will be given.

Wetland Classification for Black Duck Habitat Management Using Combined Polarimetric RADARSAT 2 and SPOT Imagery

NASA Astrophysics Data System (ADS)

Zhang, W.; Hu, B.; Brown, G.

2018-04-01

The black duck population has decreased significantly due to loss of its breeding habitat. Wetlands are an important feature that relates to habitat management and requires monitoring. Synthetic Aperture Radar (SAR) systems are helpful to map the wetland as the microwave signals are sensitive to water content and can be used to map surface water extent, saturated soils, and flooded vegetation. In this study, RadarSat 2 Polarimetric data is employed to map surface water and track changes in extent over the years through image thresholding and reviewed different approaches of Polarimetric decompositions for detecting flooded vegetation. Also, object-based analysis associated with beaver activity is conducted with combined multispectral SPOT satellite imagery. Results show SAR data has proven ability to improve mapping open water areas and locate flooded vegetation areas.

Structural Information Detection Based Filter for GF-3 SAR Images

NASA Astrophysics Data System (ADS)

Sun, Z.; Song, Y.

2018-04-01

GF-3 satellite with high resolution, large swath, multi-imaging mode, long service life and other characteristics, can achieve allweather and all day monitoring for global land and ocean. It has become the highest resolution satellite system in the world with the C-band multi-polarized synthetic aperture radar (SAR) satellite. However, due to the coherent imaging system, speckle appears in GF-3 SAR images, and it hinders the understanding and interpretation of images seriously. Therefore, the processing of SAR images has big challenges owing to the appearance of speckle. The high-resolution SAR images produced by the GF-3 satellite are rich in information and have obvious feature structures such as points, edges, lines and so on. The traditional filters such as Lee filter and Gamma MAP filter are not appropriate for the GF-3 SAR images since they ignore the structural information of images. In this paper, the structural information detection based filter is constructed, successively including the point target detection in the smallest window, the adaptive windowing method based on regional characteristics, and the most homogeneous sub-window selection. The despeckling experiments on GF-3 SAR images demonstrate that compared with the traditional filters, the proposed structural information detection based filter can well preserve the points, edges and lines as well as smooth the speckle more sufficiently.

Interferometric synthetic aperture radar (InSAR)—its past, present and future

USGS Publications Warehouse

Lu, Zhong; Kwoun, Oh-Ig; Rykhus, R.P.

2007-01-01

Very simply, interferometric synthetic aperture radar (InSAR) involves the use of two or more synthetic aperture radar (SAR) images of the same area to extract landscape topography and its deformation patterns. A SAR system transmits electromagnetic waves at a wavelength that can range from a few millimeters to tens of centimeters and therefore can operate during day and night under all-weather conditions. Using SAR processing technique (Curlander and McDonough, 1991), both the intensity and phase of the reflected (or backscattered) radar signal of each ground resolution element (a few meters to tens of meters) can be calculated in the form of a complex-valued SAR image that represents the reflectivity of the ground surface. The amplitude or intensity of the SAR image is determined primarily by terrain slope, surface roughness, and dielectric constants, whereas the phase of the SAR image is determined primarily by the distance between the satellite antenna and the ground targets. InSAR imaging utilizes the interaction of electromagnetic waves, referred to as interference, to measure precise distances between the satellite antenna and ground resolution elements to derive landscape topography and its subtle change in elevation.

Feature-based RNN target recognition

NASA Astrophysics Data System (ADS)

Bakircioglu, Hakan; Gelenbe, Erol

1998-09-01

Detection and recognition of target signatures in sensory data obtained by synthetic aperture radar (SAR), forward- looking infrared, or laser radar, have received considerable attention in the literature. In this paper, we propose a feature based target classification methodology to detect and classify targets in cluttered SAR images, that makes use of selective signature data from sensory data, together with a neural network technique which uses a set of trained networks based on the Random Neural Network (RNN) model (Gelenbe 89, 90, 91, 93) which is trained to act as a matched filter. We propose and investigate radial features of target shapes that are invariant to rotation, translation, and scale, to characterize target and clutter signatures. These features are then used to train a set of learning RNNs which can be used to detect targets within clutter with high accuracy, and to classify the targets or man-made objects from natural clutter. Experimental data from SAR imagery is used to illustrate and validate the proposed method, and to calculate Receiver Operating Characteristics which illustrate the performance of the proposed algorithm.

Characteristics of arachnoids from Magellan data

NASA Technical Reports Server (NTRS)

Dawson, C. B.; Crumpler, L. S.

1993-01-01

Current high resolution Magellan data enables more detailed geological study of arachnoids, first identified by Barsukov et al. as features characterized by a combination of radar-bright, concentric rings and radiating lineations, named 'arachnoids' on the basis of their spider and web-like appearance. Identification of arachnoids in Magellan data has been based on SAR images, in keeping with the original definition. However, there is some overlap by other workers in identification of arachnoids, corona (predominantly bright rings), and novae (predominantly radiating lineations), as all of these features share some common characteristics. Features used in this survey were chosen based on their classification as arachnoids in Head et al.'s catalog and on SAR characteristics matching Barsukov et al.'s original definition. The 259 arachnoids have been currently identified on Venus, all of which were considered in this study. Fifteen arachnoids from different regions, chosen for their 'type' characteristics and lack of deformation by other regional processes, were studied in depth, using SAR and altimetric data to map and profile these arachnoids in an attempt to better determine their geologic and altimetric characteristics and possible formation sequences.

Mapping Forest Height in Gabon Using UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Lidar Fusion

NASA Astrophysics Data System (ADS)

Simard, M.; Denbina, M. W.

2017-12-01

Using data collected by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and Land, Vegetation, and Ice Sensor (LVIS) lidar, we have estimated forest canopy height for a number of study areas in the country of Gabon using a new machine learning data fusion approach. Using multi-baseline polarimetric synthetic aperture radar interferometry (PolInSAR) data collected by UAVSAR, forest heights can be estimated using the random volume over ground model. In the case of multi-baseline UAVSAR data consisting of many repeat passes with spatially separated flight tracks, we can estimate different forest height values for each different image pair, or baseline. In order to choose the best forest height estimate for each pixel, the baselines must be selected or ranked, taking care to avoid baselines with unsuitable spatial separation, or severe temporal decorrelation effects. The current baseline selection algorithms in the literature use basic quality metrics derived from the PolInSAR data which are not necessarily indicative of the true height accuracy in all cases. We have developed a new data fusion technique which treats PolInSAR baseline selection as a supervised classification problem, where the classifier is trained using a sparse sampling of lidar data within the PolInSAR coverage area. The classifier uses a large variety of PolInSAR-derived features as input, including radar backscatter as well as features based on the PolInSAR coherence region shape and the PolInSAR complex coherences. The resulting data fusion method produces forest height estimates which are more accurate than a purely radar-based approach, while having a larger coverage area than the input lidar training data, combining some of the strengths of each sensor. The technique demonstrates the strong potential for forest canopy height and above-ground biomass mapping using fusion of PolInSAR with data from future spaceborne lidar missions such as the upcoming Global Ecosystems Dynamics Investigation (GEDI) lidar.

Investigation of ionospheric effects on SAR Interferometry (InSAR): A case study of Hong Kong

NASA Astrophysics Data System (ADS)

Zhu, Wu; Ding, Xiao-Li; Jung, Hyung-Sup; Zhang, Qin; Zhang, Bo-Chen; Qu, Wei

2016-08-01

Synthetic Aperture Radar Interferometry (InSAR) has demonstrated its potential for high-density spatial mapping of ground displacement associated with earthquakes, volcanoes, and other geologic processes. However, this technique may be affected by the ionosphere, which can result in the distortions of Synthetic Aperture Radar (SAR) images, phases, and polarization. Moreover, ionospheric effect has become and is becoming further significant with the increasing interest in low-frequency SAR systems, limiting the further development of InSAR technique. Although some research has been carried out, thorough analysis of ionospheric influence on true SAR imagery is still limited. Based on this background, this study performs a thorough investigation of ionospheric effect on InSAR through processing L-band ALOS-1/PALSAR-1 images and dual-frequency Global Positioning System (GPS) data over Hong Kong, where the phenomenon of ionospheric irregularities often occurs. The result shows that the small-scale ionospheric irregularities can cause the azimuth pixel shifts and phase advance errors on interferograms. Meanwhile, it is found that these two effects result in the stripe-shaped features in InSAR images. The direction of the stripe-shaped effects keep approximately constant in space for our InSAR dataset. Moreover, the GPS-derived rate of total electron content change index (ROTI), an index to reflect the level of ionospheric disturbances, may be a useful indicator for predicting the ionospheric effect for SAR images. This finding can help us evaluate the quality of SAR images when considering the ionospheric effect.

Watershed identification of polygonal patterns in noisy SAR images.

PubMed

Moreels, Pierre; Smrekar, Suzanne E

2003-01-01

This paper describes a new approach to pattern recognition in synthetic aperture radar (SAR) images. A visual analysis of the images provided by NASA's Magellan mission to Venus has revealed a number of zones showing polygonal-shaped faults on the surface of the planet. The goal of the paper is to provide a method to automate the identification of such zones. The high level of noise in SAR images and its multiplicative nature make automated image analysis difficult and conventional edge detectors, like those based on gradient images, inefficient. We present a scheme based on an improved watershed algorithm and a two-scale analysis. The method extracts potential edges in the SAR image, analyzes the patterns obtained, and decides whether or not the image contains a "polygon area". This scheme can also be applied to other SAR or visual images, for instance in observation of Mars and Jupiter's satellite Europa.

An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data - A case study in complex temperate forest stands

NASA Astrophysics Data System (ADS)

Abdullahi, Sahra; Schardt, Mathias; Pretzsch, Hans

2017-05-01

Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data.

Satellite on-board real-time SAR processor prototype

NASA Astrophysics Data System (ADS)

Bergeron, Alain; Doucet, Michel; Harnisch, Bernd; Suess, Martin; Marchese, Linda; Bourqui, Pascal; Desnoyers, Nicholas; Legros, Mathieu; Guillot, Ludovic; Mercier, Luc; Châteauneuf, François

2017-11-01

A Compact Real-Time Optronic SAR Processor has been successfully developed and tested up to a Technology Readiness Level of 4 (TRL4), the breadboard validation in a laboratory environment. SAR, or Synthetic Aperture Radar, is an active system allowing day and night imaging independent of the cloud coverage of the planet. The SAR raw data is a set of complex data for range and azimuth, which cannot be compressed. Specifically, for planetary missions and unmanned aerial vehicle (UAV) systems with limited communication data rates this is a clear disadvantage. SAR images are typically processed electronically applying dedicated Fourier transformations. This, however, can also be performed optically in real-time. Originally the first SAR images were optically processed. The optical Fourier processor architecture provides inherent parallel computing capabilities allowing real-time SAR data processing and thus the ability for compression and strongly reduced communication bandwidth requirements for the satellite. SAR signal return data are in general complex data. Both amplitude and phase must be combined optically in the SAR processor for each range and azimuth pixel. Amplitude and phase are generated by dedicated spatial light modulators and superimposed by an optical relay set-up. The spatial light modulators display the full complex raw data information over a two-dimensional format, one for the azimuth and one for the range. Since the entire signal history is displayed at once, the processor operates in parallel yielding real-time performances, i.e. without resulting bottleneck. Processing of both azimuth and range information is performed in a single pass. This paper focuses on the onboard capabilities of the compact optical SAR processor prototype that allows in-orbit processing of SAR images. Examples of processed ENVISAT ASAR images are presented. Various SAR processor parameters such as processing capabilities, image quality (point target analysis), weight and size are reviewed.

Flood extent and water level estimation from SAR using data-model integration

NASA Astrophysics Data System (ADS)

Ajadi, O. A.; Meyer, F. J.

2017-12-01

Synthetic Aperture Radar (SAR) images have long been recognized as a valuable data source for flood mapping. Compared to other sources, SAR's weather and illumination independence and large area coverage at high spatial resolution supports reliable, frequent, and detailed observations of developing flood events. Accordingly, SAR has the potential to greatly aid in the near real-time monitoring of natural hazards, such as flood detection, if combined with automated image processing. This research works towards increasing the reliability and temporal sampling of SAR-derived flood hazard information by integrating information from multiple SAR sensors and SAR modalities (images and Interferometric SAR (InSAR) coherence) and by combining SAR-derived change detection information with hydrologic and hydraulic flood forecast models. First, the combination of multi-temporal SAR intensity images and coherence information for generating flood extent maps is introduced. The application of least-squares estimation integrates flood information from multiple SAR sensors, thus increasing the temporal sampling. SAR-based flood extent information will be combined with a Digital Elevation Model (DEM) to reduce false alarms and to estimate water depth and flood volume. The SAR-based flood extent map is assimilated into the Hydrologic Engineering Center River Analysis System (Hec-RAS) model to aid in hydraulic model calibration. The developed technology is improving the accuracy of flood information by exploiting information from data and models. It also provides enhanced flood information to decision-makers supporting the response to flood extent and improving emergency relief efforts.

Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

NASA Astrophysics Data System (ADS)

Jahncke, Raymond; Leblon, Brigitte; Bush, Peter; LaRocque, Armand

2018-06-01

Wetland maps currently in use by the Province of Nova Scotia, namely the Department of Natural Resources (DNR) wetland inventory map and the swamp wetland classes of the DNR forest map, need to be updated. In this study, wetlands were mapped in an area southwest of Halifax, Nova Scotia by classifying a combination of multi-date and multi-beam RADARSAT-2 C-band polarimetric SAR (polSAR) images with spring Lidar, and fall QuickBird optical data using the Random Forests (RF) classifier. The resulting map has five wetland classes (open-water/marsh complex, open bog, open fen, shrub/treed fen/bog, swamp), plus lakes and various upland classes. Its accuracy was assessed using data from 156 GPS wetland sites collected in 2012 and compared to the one obtained with the current wetland map of Nova Scotia. The best overall classification was obtained using a combination of Lidar, RADARSAT-2 HH, HV, VH, VV intensity with polarimetric variables, and QuickBird multispectral (89.2%). The classified image was compared to GPS validation sites to assess the mapping accuracy of the wetlands. It was first done considering a group consisting of all wetland classes including lakes. This showed that only 69.9% of the wetland sites were correctly identified when only the QuickBird classified image was used in the classification. With the addition of variables derived from lidar, the number of correctly identified wetlands increased to 88.5%. The accuracy remained the same with the addition of RADARSAT-2 (88.5%). When we tested the accuracy for identifying wetland classes (e.g. marsh complex vs. open bog) instead of grouped wetlands, the resulting wetland map performed best with either QuickBird and Lidar, or QuickBird, Lidar, and RADARSAT-2 (66%). The Province of Nova Scotia's current wetland inventory and its associated wetland classes (aerial-photo interpreted) were also assessed against the GPS wetland sites. This provincial inventory correctly identified 62.2% of the grouped wetlands and only 18.6% of the wetland classes. The current inventory's poor performance demonstrates the value of incorporating a combination of new data sources into the provincial wetland mapping.

Sparsity-driven coupled imaging and autofocusing for interferometric SAR

NASA Astrophysics Data System (ADS)

Zengin, Oǧuzcan; Khwaja, Ahmed Shaharyar; ćetin, Müjdat

2018-04-01

We propose a sparsity-driven method for coupled image formation and autofocusing based on multi-channel data collected in interferometric synthetic aperture radar (IfSAR). Relative phase between SAR images contains valuable information. For example, it can be used to estimate the height of the scene in SAR interferometry. However, this relative phase could be degraded when independent enhancement methods are used over SAR image pairs. Previously, Ramakrishnan et al. proposed a coupled multi-channel image enhancement technique, based on a dual descent method, which exhibits better performance in phase preservation compared to independent enhancement methods. Their work involves a coupled optimization formulation that uses a sparsity enforcing penalty term as well as a constraint tying the multichannel images together to preserve the cross-channel information. In addition to independent enhancement, the relative phase between the acquisitions can be degraded due to other factors as well, such as platform location uncertainties, leading to phase errors in the data and defocusing in the formed imagery. The performance of airborne SAR systems can be affected severely by such errors. We propose an optimization formulation that combines Ramakrishnan et al.'s coupled IfSAR enhancement method with the sparsity-driven autofocus (SDA) approach of Önhon and Çetin to alleviate the effects of phase errors due to motion errors in the context of IfSAR imaging. Our method solves the joint optimization problem with a Lagrangian optimization method iteratively. In our preliminary experimental analysis, we have obtained results of our method on synthetic SAR images and compared its performance to existing methods.

Superresolution SAR Imaging Algorithm Based on Mvm and Weighted Norm Extrapolation

NASA Astrophysics Data System (ADS)

Zhang, P.; Chen, Q.; Li, Z.; Tang, Z.; Liu, J.; Zhao, L.

2013-08-01

In this paper, we present an extrapolation approach, which uses minimum weighted norm constraint and minimum variance spectrum estimation, for improving synthetic aperture radar (SAR) resolution. Minimum variance method is a robust high resolution method to estimate spectrum. Based on the theory of SAR imaging, the signal model of SAR imagery is analyzed to be feasible for using data extrapolation methods to improve the resolution of SAR image. The method is used to extrapolate the efficient bandwidth in phase history field and better results are obtained compared with adaptive weighted norm extrapolation (AWNE) method and traditional imaging method using simulated data and actual measured data.

The Study of Residential Areas Extraction Based on GF-3 Texture Image Segmentation

NASA Astrophysics Data System (ADS)

Shao, G.; Luo, H.; Tao, X.; Ling, Z.; Huang, Y.

2018-04-01

The study chooses the standard stripe and dual polarization SAR images of GF-3 as the basic data. Residential areas extraction processes and methods based upon GF-3 images texture segmentation are compared and analyzed. GF-3 images processes include radiometric calibration, complex data conversion, multi-look processing, images filtering, and then conducting suitability analysis for different images filtering methods, the filtering result show that the filtering method of Kuan is efficient for extracting residential areas, then, we calculated and analyzed the texture feature vectors using the GLCM (the Gary Level Co-occurrence Matrix), texture feature vectors include the moving window size, step size and angle, the result show that window size is 11*11, step is 1, and angle is 0°, which is effective and optimal for the residential areas extracting. And with the FNEA (Fractal Net Evolution Approach), we segmented the GLCM texture images, and extracted the residential areas by threshold setting. The result of residential areas extraction verified and assessed by confusion matrix. Overall accuracy is 0.897, kappa is 0.881, and then we extracted the residential areas by SVM classification based on GF-3 images, the overall accuracy is less 0.09 than the accuracy of extraction method based on GF-3 Texture Image Segmentation. We reached the conclusion that residential areas extraction based on GF-3 SAR texture image multi-scale segmentation is simple and highly accurate. although, it is difficult to obtain multi-spectrum remote sensing image in southern China, in cloudy and rainy weather throughout the year, this paper has certain reference significance.

Guided SAR image despeckling with probabilistic non local weights

NASA Astrophysics Data System (ADS)

Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

2017-12-01

SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

Ship Detection in SAR Image Based on the Alpha-stable Distribution

PubMed Central

Wang, Changcheng; Liao, Mingsheng; Li, Xiaofeng

2008-01-01

This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alpha-stable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution. PMID:27873794

Schatten Matrix Norm Based Polarimetric SAR Data Regularization Application over Chamonix Mont-Blanc

NASA Astrophysics Data System (ADS)

Le, Thu Trang; Atto, Abdourrahmane M.; Trouve, Emmanuel

2013-08-01

The paper addresses the filtering of Polarimetry Synthetic Aperture Radar (PolSAR) images. The filtering strategy is based on a regularizing cost function associated with matrix norms called the Schatten p-norms. These norms apply on matrix singular values. The proposed approach is illustrated upon scattering and coherency matrices on RADARSAT-2 PolSAR images over the Chamonix Mont-Blanc site. Several p values of Schatten p-norms are surveyed and their capabilities on filtering PolSAR images is provided in comparison with conventional strategies for filtering PolSAR data.

Generating high-accuracy urban distribution map for short-term change monitoring based on convolutional neural network by utilizing SAR imagery

NASA Astrophysics Data System (ADS)

Iino, Shota; Ito, Riho; Doi, Kento; Imaizumi, Tomoyuki; Hikosaka, Shuhei

2017-10-01

In the developing countries, urban areas are expanding rapidly. With the rapid developments, a short term monitoring of urban changes is important. A constant observation and creation of urban distribution map of high accuracy and without noise pollution are the key issues for the short term monitoring. SAR satellites are highly suitable for day or night and regardless of atmospheric weather condition observations for this type of study. The current study highlights the methodology of generating high-accuracy urban distribution maps derived from the SAR satellite imagery based on Convolutional Neural Network (CNN), which showed the outstanding results for image classification. Several improvements on SAR polarization combinations and dataset construction were performed for increasing the accuracy. As an additional data, Digital Surface Model (DSM), which are useful to classify land cover, were added to improve the accuracy. From the obtained result, high-accuracy urban distribution map satisfying the quality for short-term monitoring was generated. For the evaluation, urban changes were extracted by taking the difference of urban distribution maps. The change analysis with time series of imageries revealed the locations of urban change areas for short-term. Comparisons with optical satellites were performed for validating the results. Finally, analysis of the urban changes combining X-band, L-band and C-band SAR satellites was attempted to increase the opportunity of acquiring satellite imageries. Further analysis will be conducted as future work of the present study

Change classification in SAR time series: a functional approach

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

2017-10-01

Change detection represents a broad field of research in SAR remote sensing, consisting of many different approaches. Besides the simple recognition of change areas, the analysis of type, category or class of the change areas is at least as important for creating a comprehensive result. Conventional strategies for change classification are based on supervised or unsupervised landuse / landcover classifications. The main drawback of such approaches is that the quality of the classification result directly depends on the selection of training and reference data. Additionally, supervised processing methods require an experienced operator who capably selects the training samples. This training step is not necessary when using unsupervised strategies, but nevertheless meaningful reference data must be available for identifying the resulting classes. Consequently, an experienced operator is indispensable. In this study, an innovative concept for the classification of changes in SAR time series data is proposed. Regarding the drawbacks of traditional strategies given above, it copes without using any training data. Moreover, the method can be applied by an operator, who does not have detailed knowledge about the available scenery yet. This knowledge is provided by the algorithm. The final step of the procedure, which main aspect is given by the iterative optimization of an initial class scheme with respect to the categorized change objects, is represented by the classification of these objects to the finally resulting classes. This assignment step is subject of this paper.

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

NASA Astrophysics Data System (ADS)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

A SAR Observation and Numerical Study on Ocean Surface Imprints of Atmospheric Vortex Streets.

PubMed

Li, Xiaofeng; Zheng, Weizhong; Zou, Cheng-Zhi; Pichel, William G

2008-05-21

The sea surface imprints of Atmospheric Vortex Street (AVS) off Aleutian Volcanic Islands, Alaska were observed in two RADARSAT-1 Synthetic Aperture Radar (SAR) images separated by about 11 hours. In both images, three pairs of distinctive vortices shedding in the lee side of two volcanic mountains can be clearly seen. The length and width of the vortex street are about 60-70 km and 20 km, respectively. Although the AVS's in the two SAR images have similar shapes, the structure of vortices within the AVS is highly asymmetrical. The sea surface wind speed is estimated from the SAR images with wind direction input from Navy NOGAPS model. In this paper we present a complete MM5 model simulation of the observed AVS. The surface wind simulated from the MM5 model is in good agreement with SAR-derived wind. The vortex shedding rate calculated from the model run is about 1 hour and 50 minutes. Other basic characteristics of the AVS including propagation speed of the vortex, Strouhal and Reynolds numbers favorable for AVS generation are also derived. The wind associated with AVS modifies the cloud structure in the marine atmospheric boundary layer. The AVS cloud pattern is also observed on a MODIS visible band image taken between the two RADARSAT SAR images. An ENVISAT advance SAR image taken 4 hours after the second RADARSAT SAR image shows that the AVS has almost vanished.

Comparison of Filters Dedicated to Speckle Suppression in SAR Images

NASA Astrophysics Data System (ADS)

Kupidura, P.

2016-06-01

This paper presents the results of research on the effectiveness of different filtering methods dedicated to speckle suppression in SAR images. The tests were performed on RadarSat-2 images and on an artificial image treated with simulated speckle noise. The research analysed the performance of particular filters related to the effectiveness of speckle suppression and to the ability to preserve image details and edges. Speckle is a phenomenon inherent to radar images - a deterministic noise connected with land cover type, but also causing significant changes in digital numbers of pixels. As a result, it may affect interpretation, classification and other processes concerning radar images. Speckle, resembling "salt and pepper" noise, has the form of a set of relatively small groups of pixels of values markedly different from values of other pixels representing the same type of land cover. Suppression of this noise may also cause suppression of small image details, therefore the ability to preserve the important parts of an image, was analysed as well. In the present study, selected filters were tested, and methods dedicated particularly to speckle noise suppression: Frost, Gamma-MAP, Lee, Lee-Sigma, Local Region, general filtering methods which might be effective in this respect: Mean, Median, in addition to morphological filters (alternate sequential filters with multiple structuring element and by reconstruction). The analysis presented in this paper compared the effectiveness of different filtering methods. It proved that some of the dedicated radar filters are efficient tools for speckle suppression, but also demonstrated a significant efficiency of the morphological approach, especially its ability to preserve image details.

Development of structure-activity relationship for metal oxide nanoparticles

NASA Astrophysics Data System (ADS)

Liu, Rong; Zhang, Hai Yuan; Ji, Zhao Xia; Rallo, Robert; Xia, Tian; Chang, Chong Hyun; Nel, Andre; Cohen, Yoram

2013-05-01

Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L-1 and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions.Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L-1 and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr01533e

Application of Polarimetric-Interferometric Phase Coherence Optimization (PIPCO) Procedure to SIR-C/X-SAR Tien-Shan Tracks 122.20(94 Oct. 08)/154.20(94 Oct. 09) Repeat-Orbit C/L-Band Pol-D-InSAR Imag

NASA Technical Reports Server (NTRS)

Boerner, W. M.; Mott, H.; Verdi, J.; Darizhapov, D.; Dorjiev, B.; Tsybjito, T.; Korsunov, V.; Tatchkov, G.; Bashkuyev, Y.; Cloude, S.;

Synthetic aperture design for increased SAR image rate

DOEpatents

Bielek, Timothy P [Albuquerque, NM; Thompson, Douglas G [Albuqerque, NM; Walker, Bruce C [Albuquerque, NM

2009-03-03

High resolution SAR images of a target scene at near video rates can be produced by using overlapped, but nevertheless, full-size synthetic apertures. The SAR images, which respectively correspond to the apertures, can be analyzed in sequence to permit detection of movement in the target scene.

Comparison of Shuttle Imaging Radar-B ocean wave image spectra with linear model predictions based on aircraft measurements

NASA Technical Reports Server (NTRS)

Monaldo, Frank M.; Lyzenga, David R.

1988-01-01

During October 1984, coincident Shuttle Imaging Radar-B synthetic aperture radar (SAR) imagery and wave measurements from airborne instrumentation were acquired. The two-dimensional wave spectrum was measured by both a radar ocean-wave spectrometer and a surface-contour radar aboard the aircraft. In this paper, two-dimensional SAR image intensity variance spectra are compared with these independent measures of ocean wave spectra to verify previously proposed models of the relationship between such SAR image spectra and ocean wave spectra. The results illustrate both the functional relationship between SAR image spectra and ocean wave spectra and the limitations imposed on the imaging of short-wavelength, azimuth-traveling waves.

Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multiaspect Acquisitions

NASA Astrophysics Data System (ADS)

Montazeri, Sina; Gisinger, Christoph; Eineder, Michael; Zhu, Xiao xiang

2018-05-01

Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute three-dimensional localization of natural Persistent Scatterer (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

PubMed Central

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-01-01

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. PMID:27070606

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

PubMed

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Automatic Coregistration for Multiview SAR Images in Urban Areas

NASA Astrophysics Data System (ADS)

Xiang, Y.; Kang, W.; Wang, F.; You, H.

2017-09-01

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

Mapping the Recent US Hurricanes Triggered Flood Events in Near Real Time

NASA Astrophysics Data System (ADS)

Shen, X.; Lazin, R.; Anagnostou, E. N.; Wanik, D. W.; Brakenridge, G. R.

2017-12-01

Synthetic Aperture Radar (SAR) observations is the only reliable remote sensing data source to map flood inundation during severe weather events. Unfortunately, since state-of-art data processing algorithms cannot meet the automation and quality standard of a near-real-time (NRT) system, quality controlled inundation mapping by SAR currently depends heavily on manual processing, which limits our capability to quickly issue flood inundation maps at global scale. Specifically, most SAR-based inundation mapping algorithms are not fully automated, while those that are automated exhibit severe over- and/or under-detection errors that limit their potential. These detection errors are primarily caused by the strong overlap among the SAR backscattering probability density functions (PDF) of different land cover types. In this study, we tested a newly developed NRT SAR-based inundation mapping system, named Radar Produced Inundation Diary (RAPID), using Sentinel-1 dual polarized SAR data over recent flood events caused by Hurricanes Harvey, Irma, and Maria (2017). The system consists of 1) self-optimized multi-threshold classification, 2) over-detection removal using land-cover information and change detection, 3) under-detection compensation, and 4) machine-learning based correction. Algorithm details are introduced in another poster, H53J-1603. Good agreements were obtained by comparing the result from RAPID with visual interpretation of SAR images and manual processing from Dartmouth Flood Observatory (DFO) (See Figure 1). Specifically, the over- and under-detections that is typically noted in automated methods is significantly reduced to negligible levels. This performance indicates that RAPID can address the automation and accuracy issues of current state-of-art algorithms and has the potential to apply operationally on a number of satellite SAR missions, such as SWOT, ALOS, Sentinel etc. RAPID data can support many applications such as rapid assessment of damage losses and disaster alleviation/rescue at global scale.

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

PubMed Central

Leng, Xiangguang; Ji, Kefeng; Zhou, Shilin; Xing, Xiangwei; Zou, Huanxin

2016-01-01

With the rapid development of spaceborne synthetic aperture radar (SAR) and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way. PMID:27563902

Effect of Antenna Pointing Errors on SAR Imaging Considering the Change of the Point Target Location

NASA Astrophysics Data System (ADS)

Zhang, Xin; Liu, Shijie; Yu, Haifeng; Tong, Xiaohua; Huang, Guoman

2018-04-01

Towards spaceborne spotlight SAR, the antenna is regulated by the SAR system with specific regularity, so the shaking of the internal mechanism is inevitable. Moreover, external environment also has an effect on the stability of SAR platform. Both of them will cause the jitter of the SAR platform attitude. The platform attitude instability will introduce antenna pointing error on both the azimuth and range directions, and influence the acquisition of SAR original data and ultimate imaging quality. In this paper, the relations between the antenna pointing errors and the three-axis attitude errors are deduced, then the relations between spaceborne spotlight SAR imaging of the point target and antenna pointing errors are analysed based on the paired echo theory, meanwhile, the change of the azimuth antenna gain is considered as the spotlight SAR platform moves ahead. The simulation experiments manifest the effects on spotlight SAR imaging caused by antenna pointing errors are related to the target location, that is, the pointing errors of the antenna beam will severely influence the area far away from the scene centre of azimuth direction in the illuminated scene.

SAR processing using SHARC signal processing systems

NASA Astrophysics Data System (ADS)

Huxtable, Barton D.; Jackson, Christopher R.; Skaron, Steve A.

1998-09-01

Synthetic aperture radar (SAR) is uniquely suited to help solve the Search and Rescue problem since it can be utilized either day or night and through both dense fog or thick cloud cover. Other papers in this session, and in this session in 1997, describe the various SAR image processing algorithms that are being developed and evaluated within the Search and Rescue Program. All of these approaches to using SAR data require substantial amounts of digital signal processing: for the SAR image formation, and possibly for the subsequent image processing. In recognition of the demanding processing that will be required for an operational Search and Rescue Data Processing System (SARDPS), NASA/Goddard Space Flight Center and NASA/Stennis Space Center are conducting a technology demonstration utilizing SHARC multi-chip modules from Boeing to perform SAR image formation processing.

SAR imaging - Seeing the unseen

NASA Technical Reports Server (NTRS)

Kobrick, M.

1982-01-01

The functional abilities and operations of synthetic aperture radar (SAR) are described. SAR employs long wavelength radio waves in bursts, imaging a target by 'listening' to the small frequency changes that result from the Doppler shift due to the relative motion of the imaging craft and the motions of the target. The time delay of the signal return allows a determination of the location of the target, leading to the build up of a two-dimensional image. The uses of both Doppler shifts and time delay enable detailed imagery which is independent of distance. The synthetic aperture part of the name of SAR derives from the beaming of multiple pulses, which result in a picture that is effectively the same as using a large antenna. Mechanisms contributing to the fineness of SAR images are outlined.

Using ERS-2 SAR images for routine observation of marine pollution in European coastal waters.

PubMed

Gade, M; Alpers, W

1999-09-30

More than 660 synthetic aperture radar (SAR) images acquired over the southern Baltic Sea, the North Sea, and the Gulf of Lion in the Mediterranean Sea by the Second European Remote Sensing Satellite (ERS-2) have been analyzed since December 1996 with respect to radar signatures of marine pollution and other phenomena causing similar signatures. First results of our analysis reveal that the seas are most polluted along the main shipping routes. The sizes of the detected oil spills vary between < 0.1 km2 and > 56 km2. SAR images acquired during descending (morning) and ascending (evening) satellite passes show different percentages of oil pollution, because most of this pollution occurs during night time and is still visible on the SAR images acquired in the morning time. Moreover, we found a higher amount of oil spills on SAR images acquired during summer (April-September) than on SAR images acquired during winter (October-March). We attribute this finding to the higher mean wind speed encountered in all three test areas during winter. By using an ERS-2 SAR image of the North Sea test area we show how the reduction of the normalized radar backscattering cross section (NRCS) by an oil spill depends on wind speed.

Sea Ice Monitoring from Space with Synthetic Aperture Radar

NASA Astrophysics Data System (ADS)

Eltoft, T.; Dierking, W.; Doulgeris, A.; Kasapoglu, G.; Kraemer, T.

2013-03-01

This paper summarizes the knowledge status in some areas of SAR monitoring of sea ice. It starts with a brief summary of the whitepaper by Breivik et al. from OceanObs’09 [3], and then focuses on segmentation and classification, drift estimation, and assimilation strategies, which are considered as key areas in the development of more mature sea ice products from SAR and polarimetric SAR (PoLSAR) data.

NASA/JPL Aircraft SAR Workshop Proceedings

NASA Technical Reports Server (NTRS)

Donovan, N. (Editor); Evans, D. L. (Editor); Held, D. N. (Editor)

1985-01-01

Speaker-supplied summaries of the talks given at the NASA/JPL Aircraft SAR Workshop on February 4 and 5, 1985, are provided. These talks dealt mostly with composite quadpolarization imagery from a geologic or ecologic prespective. An overview and summary of the system characteristics of the L-band synthetic aperture radar (SAR) flown on the NASA CV-990 aircraft are included as supplementary information. Other topics ranging from phase imagery and interferometric techniques classifications of specific areas, and the potentials and limitations of SAR imagery in various applications are discussed.

Calibration and Validation of Airborne InSAR Geometric Model

NASA Astrophysics Data System (ADS)

Chunming, Han; huadong, Guo; Xijuan, Yue; Changyong, Dou; Mingming, Song; Yanbing, Zhang

2014-03-01

The image registration or geo-coding is a very important step for many applications of airborne interferometric Synthetic Aperture Radar (InSAR), especially for those involving Digital Surface Model (DSM) generation, which requires an accurate knowledge of the geometry of the InSAR system. While the trajectory and attitude instabilities of the aircraft introduce severe distortions in three dimensional (3-D) geometric model. The 3-D geometrical model of an airborne SAR image depends on the SAR processor itself. Working at squinted model, i.e., with an offset angle (squint angle) of the radar beam from broadside direction, the aircraft motion instabilities may produce distortions in airborne InSAR geometric relationship, which, if not properly being compensated for during SAR imaging, may damage the image registration. The determination of locations of the SAR image depends on the irradiated topography and the exact knowledge of all signal delays: range delay and chirp delay (being adjusted by the radar operator) and internal delays which are unknown a priori. Hence, in order to obtain reliable results, these parameters must be properly calibrated. An Airborne InSAR mapping system has been developed by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS) to acquire three-dimensional geo-spatial data with high resolution and accuracy. To test the performance of the InSAR system, the Validation/Calibration (Val/Cal) campaign has carried out in Sichun province, south-west China, whose results will be reported in this paper.

Methodology for locale-scale monitoring for the PROTHEGO project: the Choirokoitia case study

NASA Astrophysics Data System (ADS)

Themistocleous, Kyriacos; Agapiou, Athos; Cuca, Branka; Danezis, Chris; Cigna, Francesca; Margottini, Claudio; Spizzichino, Daniele

2016-10-01

PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) is a collaborative research project funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) - Heritage Plus in 2015-2018 (www.prothego.eu). PROTHEGO aims to make an innovative contribution towards the analysis of geohazards in areas of cultural heritage, and uses novel space technology based on radar interferometry (InSAR) to retrieve information on ground stability and motion in the 400+ UNESCO's World Heritage List monuments and sites of Europe. InSAR can be used to measure micro-movements to identify geo-hazards. In order to verify the InSAR image data, field and close range measurements are necessary. This paper presents the methodology for local-scale monitoring of the Choirokoitia study site in Cyprus, inscribed in the UNESCO World Heritage List, and part of the demonstration sites of PROTHEGO. Various field and remote sensing methods will be exploited for the local-scale monitoring, static GNSS, total station, leveling, laser scanning and UAV and compared with the Persistent Scatterer Interferometry results. The in-situ measurements will be taken systematically in order to document any changes and geo-hazards that affect standing archaeological remains. In addition, ground truth from in-situ visits will provide feedback related to the classification results of urban expansion and land use change maps. Available archival and current optical satellite images will be used to calibrate and identify the level of risk at the Cyprus case study site. The ground based geotechnical monitoring will be compared and validated with InSAR data to evaluate cultural heritage sites deformation trend and to understand its behaviour over the last two decades.

Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2

NASA Astrophysics Data System (ADS)

McNairn, H.; Kross, A.; Lapen, D.; Caves, R.; Shang, J.

2014-05-01

Early and on-going crop production forecasts are important to facilitate food price stability for regions at risk, and for agriculture exporters, to set market value. Most regional and global efforts in forecasting rely on multiple sources of information from the field. With increased access to data from spaceborne Synthetic Aperture Radar (SAR), these sensors could contribute information on crop acreage. But these acreage estimates must be available early in the season to assist with production forecasts. This study acquired TerraSAR-X and RADARSAT-2 data over a region in eastern Canada dominated by economically important corn and soybean production. Using a supervised decision tree classifier, results determined that either sensor was capable of delivering highly accurate maps of corn and soybeans at the end of the growing season. Accuracies far exceeded 90%. Spatial and multi-temporal filtering approaches were compared and small improvements in accuracies were found by applying the multi-temporal filter to the RADARSAT-2 data. Of significant interest, this study determined that by using only three TerraSAR-X images corn could be accurately identified by the end of June, a mere six weeks after planting and at a vegetative growth stage (V6 - sixth leaf collar developed). However, soybeans required additional acquisitions given the variance in planting densities and planting dates in this region of Canada. In this case, accurate soybean classification required TerraSAR-X images until early August at the start of the reproductive stage (R5 - seed development is beginning). Also important, by applying a multi-temporal filter accurate mapping (close to 90%) of corn and soybeans from RADARSAT-2 could occur five weeks earlier (by August 19) than if a spatial filter was used. Thus application of this filtering approach could accelerate delivery of a crop inventory for this region of Canada. Corn and soybeans are important commodities both globally and within Canada. This study makes an important contribution as it demonstrates that TerraSAR-X can deliver acreage estimates of these two crops early enough to assist with in-season production forecasting.

Interpretation of the Cosmo-SkyMed observations of the 2009 Tanaro river flood

NASA Astrophysics Data System (ADS)

Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L.

2010-09-01

The potentiality of spaceborne Synthetic Aperture Radar (SAR) for flood mapping was demonstrated by several past investigations. The synoptic view and the capability to operate in almost all-weather conditions and during both day and night are the key features that make the SAR images useful for monitoring inundation events. In addition, their high spatial resolution allows a fairly accurate delineation of the flood extent. The Cosmo-SkyMed (COnstellation of small Satellites for Mediterranean basin Observation) mission offers a unique opportunity to obtain radar images characterized by short revisit time, so that an operational use of Cosmo-SkyMed data in flood management systems can be envisaged. However, the interpretation of SAR images of flooded areas might be complex, because of the dependence of the radar response from flooded pixels on land cover, system parameters and environmental conditions. An example of radar data whose interpretation is not straightforward is represented by the Cosmo-SkyMed observations of the overflowing of the Tanaro river, close to the city of Alessandria (Northern Italy), occurred on April, 27-28 2009. Within the framework of a study, funded by the Italian Space Agency (ASI), aiming at evaluating the usefulness of Earth Observation techniques into operational flood prediction and assessment chains (named OPERA, civil protection from floods), ASI provided a number of Cosmo-SkyMed images of the Tanaro basin. In this study, we use three images that were acquired during three days in succession: from April, 29 to May, 1 2009, as well as other two acquisitions performed two weeks later (May, 16 and May, 17 2009), when the effects of the flood were disappeared. In this work, we firstly extract information on the spatial extension of homogeneous objects present in the scene through a segmentation procedure. In this way we cope with the speckle noise characteristic of SAR images and produce, from the multi-temporal series of five imagery we employ, a map formed by homogeneous regions. Among these regions we single out some areas presenting a fairly complex temporal evolution of the radar return. To correctly explain the multi-temporal radar signature of these segments, we use of a well-established electromagnetic model. Some reference multi-temporal backscattering trends are analyzed with the aid of the theoretical model to associate the segments to the classes of flooded or non-flooded areas. Using these reference trends as a training set, a classification algorithm is also developed to generate a map of the flood evolution. This study aims at demonstrating the importance and the feasibility of a method based on a joint use of a well-established electromagnetic scattering model and an advanced image processing technique to reliably interpreting SAR observations of floods.

Research on Airborne SAR Imaging Based on Esc Algorithm

NASA Astrophysics Data System (ADS)

Dong, X. T.; Yue, X. J.; Zhao, Y. H.; Han, C. M.

2017-09-01

Due to the ability of flexible, accurate, and fast obtaining abundant information, airborne SAR is significant in the field of Earth Observation and many other applications. Optimally the flight paths are straight lines, but in reality it is not the case since some portion of deviation from the ideal path is impossible to avoid. A small disturbance from the ideal line will have a major effect on the signal phase, dramatically deteriorating the quality of SAR images and data. Therefore, to get accurate echo information and radar images, it is essential to measure and compensate for nonlinear motion of antenna trajectories. By means of compensating each flying trajectory to its reference track, MOCO method corrects linear phase error and quadratic phase error caused by nonlinear antenna trajectories. Position and Orientation System (POS) data is applied to acquiring accuracy motion attitudes and spatial positions of antenna phase centre (APC). In this paper, extend chirp scaling algorithm (ECS) is used to deal with echo data of airborne SAR. An experiment is done using VV-Polarization raw data of C-band airborne SAR. The quality evaluations of compensated SAR images and uncompensated SAR images are done in the experiment. The former always performs better than the latter. After MOCO processing, azimuth ambiguity is declined, peak side lobe ratio (PSLR) effectively improves and the resolution of images is improved obviously. The result shows the validity and operability of the imaging process for airborne SAR.

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

NASA Astrophysics Data System (ADS)

Gobeyn, Sacha; Van Wesemael, Alexandra; Neal, Jeffrey; Lievens, Hans; Eerdenbrugh, Katrien Van; De Vleeschouwer, Niels; Vernieuwe, Hilde; Schumann, Guy J.-P.; Di Baldassarre, Giuliano; Baets, Bernard De; Bates, Paul D.; Verhoest, Niko E. C.

2017-02-01

Synthetic Aperture Radar (SAR) data have proven to be a very useful source of information for the calibration of flood inundation models. Previous studies have focused on assigning uncertainties to SAR images in order to improve flood forecast systems (e.g. Giustarini et al. (2015) and Stephens et al. (2012)). This paper investigates whether the timing of a SAR acquisition of a flood has an important impact on the calibration of a flood inundation model. As no suitable time series of SAR data exists, we generate a sequence of consistent SAR images through the use of a synthetic framework. This framework uses two available ERS-2 SAR images of the study area, one taken during the flood event of interest, the second taken during a dry reference period. The obtained synthetic observations at different points in time during the flood event are used to calibrate the flood inundation model. The results of this study indicate that the uncertainty of the roughness parameters is lower when the model is calibrated with an image taken before rather than during or after the flood peak. The results also show that the error on the modelled extent is much lower when the model is calibrated with a pre-flood peak image than when calibrated with a near-flood peak or a post-flood peak image. It is concluded that the timing of the SAR image acquisition of the flood has a clear impact on the model calibration and consequently on the precision of the predicted flood extent.

Impact of the Timing of a SAR Image Acquisition on the Calibration of a Flood Inundation Model

NASA Technical Reports Server (NTRS)

Gobeyn, Sacha; Van Wesemael, Alexandra; Neal, Jeffrey; Lievens, Hans; Van Eerdenbrugh, Katrien; De Vleeschouwer, Niels; Vernieuwe, Hilde; Schumann, Guy J.-P.; Di Baldassarre, Giuliano; De Baets, Bernard;

Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery

NASA Astrophysics Data System (ADS)

Nascimento, Wilson R.; Souza-Filho, Pedro Walfir M.; Proisy, Christophe; Lucas, Richard M.; Rosenqvist, Ake

2013-01-01

Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particularly where human disturbance occurs and because of uncertainties associated with sea level and climatic fluctuation. In many tropical regions, such efforts have focused largely on the use of optical data despite low capture rates because of persistent cloud cover. Recognizing the ability of Synthetic Aperture Radar (SAR) for providing cloud-free observations, this study investigated the use of JERS-1 SAR and ALOS PALSAR data, acquired in 1996 and 2008 respectively, for mapping the extent of mangroves along the Brazilian coastline, from east of the Amazon River mouth, Pará State, to the Bay of São José in Maranhão. For each year, an object-orientated classification of major land covers (mangrove, secondary vegetation, gallery and swamp forest, open water, intermittent lakes and bare areas) was performed with the resulting maps then compared to quantify change. Comparison with available ground truth data indicated a general accuracy in the 2008 image classification of all land covers of 96% (kappa = 90.6%, tau = 92.6%). Over the 12 year period, the area of mangrove increased by 718.6 km2 from 6705 m2 to 7423.60 km2, with 1931.0 km² of expansion and 1213 km² of erosion noted; 5493 km² remained unchanged in extent. The general accuracy relating to changes in mangroves was 83.3% (Kappa 66.1%; tau 66.7%). The study confirmed that these mangroves constituted the largest continuous belt globally and were experiencing significant change because of the dynamic coastal environment and the influence of sedimentation from the Amazon River along the shoreline. The study recommends continued observations using combinations of SAR and optical data to establish trends in mangrove distributions and implications for provision of ecosystem services (e.g., fish/invertebrate nurseries, carbon storage and coastal protection).

MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

PubMed

Deledalle, Charles-Alban; Denis, Loic; Tabti, Sonia; Tupin, Florence

2017-09-01

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric, or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

NASA Astrophysics Data System (ADS)

Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

2017-10-01

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Separation of Target Rigid Body and Micro-Doppler Effects in ISAR/SAR Imaging

DTIC Science & Technology

2006-09-01

tour- nantes et vibrantes de la cible. De nouveaux algorithmes et m~thodes devront donc DRDC Ottawa TM 2006-187 v &tre 6tudi6s plus en profondeur afin...UNCLASSIFIED SECURITY CLASSIFICATION OF FORM Defence R&D Canada R & D pour la defense Canada Canada’s Leader in Defence Chef de file au Canada en mati~re and...I 1f1 Defence Research and Recherche et developpement Development Canada pour la defense Canada DEFENCE ril DEFENSE Separation of target rigid body

Hybrid Geometric Calibration Method for Multi-Platform Spaceborne SAR Image with Sparse Gcps

NASA Astrophysics Data System (ADS)

Lv, G.; Tang, X.; Ai, B.; Li, T.; Chen, Q.

2018-04-01

Geometric calibration is able to provide high-accuracy geometric coordinates of spaceborne SAR image through accurate geometric parameters in the Range-Doppler model by ground control points (GCPs). However, it is very difficult to obtain GCPs that covering large-scale areas, especially in the mountainous regions. In addition, the traditional calibration method is only used for single platform SAR images and can't support the hybrid geometric calibration for multi-platform images. To solve the above problems, a hybrid geometric calibration method for multi-platform spaceborne SAR images with sparse GCPs is proposed in this paper. First, we calibrate the master image that contains GCPs. Secondly, the point tracking algorithm is used to obtain the tie points (TPs) between the master and slave images. Finally, we calibrate the slave images using TPs as the GCPs. We take the Beijing-Tianjin- Hebei region as an example to study SAR image hybrid geometric calibration method using 3 TerraSAR-X images, 3 TanDEM-X images and 5 GF-3 images covering more than 235 kilometers in the north-south direction. Geometric calibration of all images is completed using only 5 GCPs. The GPS data extracted from GNSS receiver are used to assess the plane accuracy after calibration. The results after geometric calibration with sparse GCPs show that the geometric positioning accuracy is 3 m for TSX/TDX images and 7.5 m for GF-3 images.

Flight path-driven mitigation of wavefront curvature effects in SAR images

DOEpatents

Doerry, Armin W [Albuquerque, NM

2009-06-23

A wavefront curvature effect associated with a complex image produced by a synthetic aperture radar (SAR) can be mitigated based on which of a plurality of possible flight paths is taken by the SAR when capturing the image. The mitigation can be performed differently for different ones of the flight paths.

Research on Coordinate Transformation Method of Gb-Sar Image Supported by 3d Laser Scanning Technology

NASA Astrophysics Data System (ADS)

Wang, P.; Xing, C.

2018-04-01

In the image plane of GB-SAR, identification of deformation distribution is usually carried out by artificial interpretation. This method requires analysts to have adequate experience of radar imaging and target recognition, otherwise it can easily cause false recognition of deformation target or region. Therefore, it is very meaningful to connect two-dimensional (2D) plane coordinate system with the common three-dimensional (3D) terrain coordinate system. To improve the global accuracy and reliability of the transformation from 2D coordinates of GB-SAR images to local 3D coordinates, and overcome the limitation of traditional similarity transformation parameter estimation method, 3D laser scanning data is used to assist the transformation of GB-SAR image coordinates. A straight line fitting method for calculating horizontal angle was proposed in this paper. After projection into a consistent imaging plane, we can calculate horizontal rotation angle by using the linear characteristics of the structure in radar image and the 3D coordinate system. Aided by external elevation information by 3D laser scanning technology, we completed the matching of point clouds and pixels on the projection plane according to the geometric projection principle of GB-SAR imaging realizing the transformation calculation of GB-SAR image coordinates to local 3D coordinates. Finally, the effectiveness of the method is verified by the GB-SAR deformation monitoring experiment on the high slope of Geheyan dam.

Direct Geolocation of TerraSAR-X Spotlight Mode Image and Error Correction

NASA Astrophysics Data System (ADS)

Zhou, Xiao; Zeng, Qiming; Jiao, Jian; Zhang, Jingfa; Gong, Lixia

2013-01-01

The GERMAN TerraSAR-X mission was launched in June 2007, operating a versatile new-generation SAR sensor in X-band. Its Spotlight mode providing SAR images at very high resolution of about 1m. The product’s specified 3-D geolocation accuracy is tightened to 1m according to the official technical report. However, this accuracy is able to be achieved relies on not only robust mathematical basis of SAR geolocation, but also well knowledge of error sources and their correction. The research focuses on geolocation of TerraSAR-X spotlight image. Mathematical model and resolving algorithms have been analyzed. Several error sources have been researched and corrected especially. The effectiveness and accuracy of the research was verified by the experiment results.

Coastline detection with time series of SAR images

NASA Astrophysics Data System (ADS)

Ao, Dongyang; Dumitru, Octavian; Schwarz, Gottfried; Datcu, Mihai

2017-10-01

For maritime remote sensing, coastline detection is a vital task. With continuous coastline detection results from satellite image time series, the actual shoreline, the sea level, and environmental parameters can be observed to support coastal management and disaster warning. Established coastline detection methods are often based on SAR images and wellknown image processing approaches. These methods involve a lot of complicated data processing, which is a big challenge for remote sensing time series. Additionally, a number of SAR satellites operating with polarimetric capabilities have been launched in recent years, and many investigations of target characteristics in radar polarization have been performed. In this paper, a fast and efficient coastline detection method is proposed which comprises three steps. First, we calculate a modified correlation coefficient of two SAR images of different polarization. This coefficient differs from the traditional computation where normalization is needed. Through this modified approach, the separation between sea and land becomes more prominent. Second, we set a histogram-based threshold to distinguish between sea and land within the given image. The histogram is derived from the statistical distribution of the polarized SAR image pixel amplitudes. Third, we extract continuous coastlines using a Canny image edge detector that is rather immune to speckle noise. Finally, the individual coastlines derived from time series of .SAR images can be checked for changes.

Emergency product generation for disaster management using RISAT and DMSAR quick look SAR processors

NASA Astrophysics Data System (ADS)

Desai, Nilesh; Sharma, Ritesh; Kumar, Saravana; Misra, Tapan; Gujraty, Virendra; Rana, SurinderSingh

2006-12-01

Since last few years, ISRO has embarked upon the development of two complex Synthetic Aperture Radar (SAR) missions, viz. Spaceborne Radar Imaging Satellite (RISAT) and Airborne SAR for Disaster Mangement (DMSAR), as a capacity building measure under country's Disaster Management Support (DMS) Program, for estimating the extent of damage over large areas (~75 Km) and also assess the effectiveness of the relief measures undertaken during natural disasters such as cyclones, epidemics, earthquakes, floods and landslides, forest fires, crop diseases etc. Synthetic Aperture Radar (SAR) has an unique role to play in mapping and monitoring of large areas affected by natural disasters especially floods, owing to its unique capability to see through clouds as well as all-weather imaging capability. The generation of SAR images with quick turn around time is very essential to meet the above DMS objectives. Thus the development of SAR Processors, for these two SAR systems poses considerable challenges and design efforts. Considering the growing user demand and inevitable necessity for a full-fledged high throughput processor, to process SAR data and generate image in real or near-real time, the design and development of a generic SAR Processor has been taken up and evolved, which will meet the SAR processing requirements for both Airborne and Spaceborne SAR systems. This hardware SAR processor is being built, to the extent possible, using only Commercial-Off-The-Shelf (COTS) DSP and other hardware plug-in modules on a Compact PCI (cPCI) platform. Thus, the major thrust has been on working out Multi-processor Digital Signal Processor (DSP) architecture and algorithm development and optimization rather than hardware design and fabrication. For DMSAR, this generic SAR Processor operates as a Quick Look SAR Processor (QLP) on-board the aircraft to produce real time full swath DMSAR images and as a ground based Near-Real Time high precision full swath Processor (NRTP). It will generate full-swath (6 to 75 Kms) DMSAR images in 1m / 3m / 5m / 10m / 30m resolution SAR operating modes. For RISAT mission, this generic Quick Look SAR Processor will be mainly used for browse product generation at NRSA-Shadnagar (SAN) ground receive station. RISAT QLP/NRTP is also proposed to provide an alternative emergency SAR product generation chain. For this, the S/C aux data appended in Onboard SAR Frame Format (x, y, z, x', y', z', roll, pitch, yaw) and predicted orbit from previous days Orbit Determination data will be used. The QLP / NRTP will produce ground range images in real / near real time. For emergency data product generation, additional Off-line tasks like geo-tagging, masking, QC etc needs to be performed on the processed image. The QLP / NRTP would generate geo-tagged images from the annotation data available from the SAR P/L data itself. Since the orbit & attitude information are taken as it is, the location accuracy will be poorer compared to the product generated using ADIF, where smoothened attitude and orbit are made available. Additional tasks like masking, output formatting and Quality checking of the data product will be carried out at Balanagar, NRSA after the image annotated data from QLP / NRTP is sent to Balanagar. The necessary interfaces to the QLP/NRTP for Emergency product generation are also being worked out. As is widely acknowledged, QLP/NRTP for RISAT and DMSAR is an ambitious effort and the technology of future. It is expected that by the middle of next decade, the next generation SAR missions worldwide will have onboard SAR Processors of varying capabilities and generate SAR Data products and Information products onboard instead of SAR raw data. Thus, it is also envisaged that these activities related to QLP/NRTP implementation for RISAT ground segment and DMSAR will be a significant step which will directly feed into the development of onboard real time processing systems for ISRO's future space borne SAR missions. This paper describes the design requirements, configuration details and salient features, apart from highlighting the utility of these Quick Look SAR processors for RISAT and DMSAR, for generation of emergency products for Disaster management.

Detection of Sea Ice and Open Water from RADARSAT-2 Images for Data Assimilation

NASA Astrophysics Data System (ADS)

Komarov, A.; Buehner, M.

2016-12-01

Automated detection of sea ice and open water from SAR data is very important for further assimilation into coupled ocean-sea ice-atmosphere numerical models, such as the Regional Ice-Ocean Prediction System being implemented at the Environment and Climate Change Canada. Conventional classification approaches based on various learning techniques are found to be limited by the fact that they typically do not indicate the level of confidence for ice and water retrievals. Meanwhile, only ice/water retrievals with a very high level of confidence are allowed to be assimilated into the sea ice model to avoid propagating and magnifying errors into the numerical prediction system. In this study we developed a new technique for ice and water detection from dual-polarization RADARSAT-2 HH-HV images which provides the probability of ice/water at a given location. We collected many hundreds of thousands of SAR signatures over various sea ice types (i.e. new, grey, first-year, and multi-year ice) and open water from all available RADARSAT-2 images and the corresponding Canadian Ice Service Image Analysis products over the period from November 2010 to May 2016. Our analysis of the dataset revealed that ice/water separation can be effectively performed in the space of SAR-based variables independent of the incidence angle and noise floor (such as texture measures) and auxiliary Global Environmental Multiscale Model parameters (such as surface wind speed). Choice of the parameters will be specifically discussed in the presentation. An ice probability empirical model as a function of the selected predictors was built in a form of logistic regression, based on the training dataset from 2012 to 2016. The developed ice probability model showed very good performance on the independent testing subset (year 2011). With the ice/water probability threshold of 0.95 reflecting a very high level of confidence, 79% of the testing ice and water samples were classified with the accuracy of 99%. These results are particularly important in light of the upcoming RADARSAT Constellation mission which will drastically increase the amount of SAR data over the Arctic region.

SAR studies in the Yuma Desert, Arizona: Sand penetration, geology, and the detection of military ordnance debris

USGS Publications Warehouse

Schaber, G.G.

1999-01-01

Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also Compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).Synthetic Aperture Radar (SAR) images acquired over part of the Yuma Desert in southwestern Arizona demonstrate the ability of C-band (5.7-cm wavelength), L-band (24.5 cm), and P-band (68 cm) AIRSAR signals to backscatter from increasingly greater depths reaching several meters in blow sand and sandy alluvium. AIRSAR images obtained within the Barry M. Goldwater Bombing and Gunnery Range near Yuma, Arizona, show a total reversal of C- and P-band backscatter contrast (image tone) for three distinct geologic units. This phenomenon results from an increasingly greater depth of radar imaging with increasing radar wavelength. In the case of sandy- and small pebble-alluvium surfaces mantled by up to several meters of blow sand, backscatter increases directly with SAR wavelength as a result of volume scattering from a calcic soil horizon at shallow depth and by volume scattering from the root mounds of healthy desert vegetation that locally stabilize blow sand. AIRSAR images obtained within the military range are also shown to be useful for detecting metallic military ordnance debris that is located either at the surface or covered by tens of centimeters to several meters of blow sand. The degree of detectability of this ordnance increases with SAR wavelength and is clearly maximized on P-band images that are processed in the cross-polarized mode (HV). This effect is attributed to maximum signal penetration at P-band and the enhanced PHV image contrast between the radar-bright ordnance debris and the radar-dark sandy desert. This article focuses on the interpretation of high resolution AIRSAR images but also compares these airborne SAR images with those acquired from spacecraft sensors such as ERS-SAR and Space Radar Laboratory (SIR-C/X-SAR).

Characterization of the range effect in synthetic aperture radar images of concrete specimens for width estimation

NASA Astrophysics Data System (ADS)

Alzeyadi, Ahmed; Yu, Tzuyang

2018-03-01

Nondestructive evaluation (NDE) is an indispensable approach for the sustainability of critical civil infrastructure systems such as bridges and buildings. Recently, microwave/radar sensors are widely used for assessing the condition of concrete structures. Among existing imaging techniques in microwave/radar sensors, synthetic aperture radar (SAR) imaging enables researchers to conduct surface and subsurface inspection of concrete structures in the range-cross-range representation of SAR images. The objective of this paper is to investigate the range effect of concrete specimens in SAR images at various ranges (15 cm, 50 cm, 75 cm, 100 cm, and 200 cm). One concrete panel specimen (water-to-cement ratio = 0.45) of 30-cm-by-30-cm-by-5-cm was manufactured and scanned by a 10 GHz SAR imaging radar sensor inside an anechoic chamber. Scatterers in SAR images representing two corners of the concrete panel were used to estimate the width of the panel. It was found that the range-dependent pattern of corner scatters can be used to predict the width of concrete panels. Also, the maximum SAR amplitude decreases when the range increases. An empirical model was also proposed for width estimation of concrete panels.

Pseudo-color coding method for high-dynamic single-polarization SAR images

NASA Astrophysics Data System (ADS)

Feng, Zicheng; Liu, Xiaolin; Pei, Bingzhi

2018-04-01

A raw synthetic aperture radar (SAR) image usually has a 16-bit or higher bit depth, which cannot be directly visualized on 8-bit displays. In this study, we propose a pseudo-color coding method for high-dynamic singlepolarization SAR images. The method considers the characteristics of both SAR images and human perception. In HSI (hue, saturation and intensity) color space, the method carries out high-dynamic range tone mapping and pseudo-color processing simultaneously in order to avoid loss of details and to improve object identifiability. It is a highly efficient global algorithm.

Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction

NASA Astrophysics Data System (ADS)

Scarnati, Theresa; Gelb, Anne

2018-04-01

In observing multiple synthetic aperture radar (SAR) images of the same scene, it is apparent that the brightness distributions of the images are not smooth, but rather composed of complicated granular patterns of bright and dark spots. Further, these brightness distributions vary from image to image. This salt and pepper like feature of SAR images, called speckle, reduces the contrast in the images and negatively affects texture based image analysis. This investigation uses the variance based joint sparsity reconstruction method for forming SAR images from the multiple SAR images. In addition to reducing speckle, the method has the advantage of being non-parametric, and can therefore be used in a variety of autonomous applications. Numerical examples include reconstructions of both simulated phase history data that result in speckled images as well as the images from the MSTAR T-72 database.

Unmanned Aircraft Systems (UAS) Sensor and Targeting

DTIC Science & Technology

2010-07-27

4.7.1 Objective. The objective of this subtest is to determine the detection performance of the Synthetic Aperture Radar (SAR) with the radar...Detection SAR – Synthetic Aperture Radar 4.7.3 Data Required. Section 5.1 outlines general test data required. The following additional data may...m – meter No. – Number PC – Probability of Classification SAR – Synthetic Aperture Radar 4.8.3 Data Required. Section 5.1 outlines

Real time SAR processing

NASA Technical Reports Server (NTRS)

Premkumar, A. B.; Purviance, J. E.

1990-01-01

A simplified model for the SAR imaging problem is presented. The model is based on the geometry of the SAR system. Using this model an expression for the entire phase history of the received SAR signal is formulated. From the phase history, it is shown that the range and the azimuth coordinates for a point target image can be obtained by processing the phase information during the intrapulse and interpulse periods respectively. An architecture for a VLSI implementation for the SAR signal processor is presented which generates images in real time. The architecture uses a small number of chips, a new correlation processor, and an efficient azimuth correlation process.

Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling.

PubMed

Yu, Ze; Lin, Peng; Xiao, Peng; Kang, Lihong; Li, Chunsheng

2016-07-14

Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath.

Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling

PubMed Central

Yu, Ze; Lin, Peng; Xiao, Peng; Kang, Lihong; Li, Chunsheng

2016-01-01

Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath. PMID:27428974

In the (sub)tropics allergic rhinitis and its impact on asthma classification of allergic rhinitis is more useful than perennial-seasonal classification.

PubMed

Larenas-Linnemann, Désirée; Michels, Alexandra; Dinger, Hanna; Arias-Cruz, Alfredo; Ambriz Moreno, Marichuy; Bedolla Barajas, Martin; Javier, Ruth Cerino; Cid Del Prado, Maria de la Luz; Cruz Moreno, Manuel Alejandro; Vergara, Laura Diego; García Almaráz, Roberto; García-Cobas, Cecilia Y; Garcia Imperial, Daniel Alberto; Muñoz, Rosa Garcia; Hernandez Colín, Dante; Linares Zapien, Francisco Javier; Luna Pech, Jorge Agustín; Matta Campos, Juan Jose; Martinez Jimenez, Norma; Avalos, Miguel Medina; Medina Hernandez, Alejandra; Maldonado, Albero Monteverde; López, Doris Nereida; Pizano Nazara, Luis Julian; Sanchez, Emanuel Ramirez; Ramos López, José Domingo; Rodriguez-Pérez, Noel; Rodriguez Ortiz, Pablo G; Shah-Hosseini, Kijawasch; Mösges, Ralph

2014-01-01

Two different allergic rhinitis (AR) symptom phenotype classifications exist. Treatment recommendations are based on intermittent-persistent (INT-PER) cataloging, but clinical trials still use the former seasonal AR-perennial AR (SAR-PAR) classification. This study was designed to describe how INT-PER, mild-moderate/severe and SAR-PAR of patients seen by allergists are distributed over the different climate zones in a (sub)tropical country and how these phenotypes relate to allergen sensitization patterns. Six climate zones throughout Mexico were determined, based on National Geographic Institute (Instituto Nacional de Estadística y Geografía) data. Subsequent AR patients (2-68 years old) underwent a blinded, standardized skin-prick test and filled out a validated questionnaire phenotyping AR. Five hundred twenty-nine subjects participated in this study. In the tropical zone with 87% house-dust mite sensitization, INT (80.9%; p < 0.001) and PAR (91%; p = 0.04) were more frequent than in the subtropics. In the central high-pollen areas, there was less moderate/severe AR (65.5%; p < 0.005). Frequency of comorbid asthma showed a clear north-south gradient, from 25% in the dry north to 59% in the tropics (p < 0.005). No differences exist in AR cataloging among patients with different sensitization patterns, with two minor exceptions (more PER in tree sensitized and more PAR in mold positives; p < 0.05). In a (sub)tropical country the SAR-PAR classification seems of limited value and bears poor relation with the INT-PER classification. INT is more frequent in the tropical zone. Because PER has been shown to relate to AR severity, clinical trials should select patients based on INT-PER combined with the severity cataloging because these make for a better treatment guide than SAR-PAR.

Preliminary study of near surface detections at geothermal field using optic and SAR imageries

NASA Astrophysics Data System (ADS)

Kurniawahidayati, Beta; Agoes Nugroho, Indra; Syahputra Mulyana, Reza; Saepuloh, Asep

2017-12-01

Current remote sensing technologies shows that surface manifestation of geothermal system could be detected with optical and SAR remote sensing, but to assess target beneath near the surface layer with the surficial method needs a further study. This study conducts a preliminary result using Optic and SAR remote sensing imagery to detect near surface geothermal manifestation at and around Mt. Papandayan, West Java, Indonesia. The data used in this study were Landsat-8 OLI/TIRS for delineating geothermal manifestation prospect area and an Advanced Land Observing Satellite(ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) level 1.1 for extracting lineaments and their density. An assumption was raised that the lineaments correlated with near surface structures due to long L-band wavelength about 23.6 cm. Near surface manifestation prospect area are delineated using visual comparison between Landsat 8 RGB True Colour Composite band 4,3,2 (TCC), False Colour Composite band 5,6,7 (FCC), and lineament density map of ALOS PALSAR. Visual properties of ground object were distinguished from interaction of the electromagnetic radiation and object whether it reflect, scatter, absorb, or and emit electromagnetic radiation based on characteristic of their molecular composition and their macroscopic scale and geometry. TCC and FCC composite bands produced 6 and 7 surface manifestation zones according to its visual classification, respectively. Classified images were then compared to a Normalized Different Vegetation Index (NDVI) to obtain the influence of vegetation at the ground surface to the image. Geothermal area were classified based on vegetation index from NDVI. TCC image is more sensitive to the vegetation than FCC image. The later composite produced a better result for identifying visually geothermal manifestation showed by detail-detected zones. According to lineament density analysis high density area located on the peak of Papandayan overlaid with zone 1 and 2 of FCC. Comparing to the extracted lineament density, we interpreted that the near surface manifestation is located at zone 1 and 2 of FCC image.

Azimuthal resolution degradation due to ocean surface motion in focused arrays and SARS

NASA Astrophysics Data System (ADS)

1990-06-01

During the meeting at WHOI (5-18-90), a discussion arose of the ability of the focused array to simulate the R/v ratios typical of airborne and/or spaceborne SARs. In particular, the ability was questioned of the focused array to yield the same azimuthal resolution, rho, as the SAR. Although the focused array can be sampled to yield the same azimuthal resolution as the SAR, it is likely that the images generated by the focused array will not be identical to those produced by a SAR with the same azimuth resolution. For a true SAR, biases in the Doppler history of azimuthally traveling waves due to their along-track motion will cause shifts in their apparent position. This will cause waves which are physically at one location to shift over several pixel widths in the image. The limited swath width of the focused array will prevent if from observing scattered power from waves falling outside the swath, thus such waves will not affect the image formed within the swath, as would happen in the SAR. Thus, it is likely that the focused array will not yield the same image as a SAR having the same resolution.

Integrating polarimetric synthetic aperture radar and imaging spectrometry for wildland fuel mapping in southern California

Treesearch

P.E. Dennison; D.A. Roberts; J. Regelbrugge; S.L. Ustin

2000-01-01

Polarimetric synthetic aperture radar (SAR) and imaging spectrometry exemplify advanced technologies for mapping wildland fuels in chaparral ecosystems. In this study, we explore the potential of integrating polarimetric SAR and imaging spectrometry for mapping wildland fuels. P-band SAR and ratios containing P-band polarizations are sensitive to variations in stand...

Scattering angle resolved optical coherence tomography for in vivo murine retinal imaging

NASA Astrophysics Data System (ADS)

Gardner, Michael R.; Katta, Nitesh; McElroy, Austin; Baruah, Vikram; Rylander, H. G.; Milner, Thomas E.

2017-02-01

Optical coherence tomography (OCT) retinal imaging contributes to understanding central nervous system (CNS) diseases because the eye is an anatomical "window to the brain" with direct optical access to nonmylenated retinal ganglion cells. However, many CNS diseases are associated with neuronal changes beyond the resolution of standard OCT retinal imaging systems. Though studies have shown the utility of scattering angle resolved (SAR) OCT for particle sizing and detecting disease states ex vivo, a compact SAR-OCT system for in vivo rodent retinal imaging has not previously been reported. We report a fiber-based SAR-OCT system (swept source at 1310 nm +/- 65 nm, 100 kHz scan rate) for mouse retinal imaging with a partial glass window (center aperture) for angular discrimination of backscattered light. This design incorporates a dual-axis MEMS mirror conjugate to the ocular pupil plane and a high collection efficiency objective. A muring retina is imaged during euthanasia, and the proposed SAR-index is examined versus time. Results show a positive correlation between the SAR-index and the sub-cellular hypoxic response of neurons to isoflurane overdose during euthanasia. The proposed SAR-OCT design and image process technique offer a contrast mechanism able to detect sub-resolution neuronal changes for murine retinal imaging.

Development of chemical inhibitors of the SARS coronavirus: viral helicase as a potential target.

PubMed

Keum, Young-Sam; Jeong, Yong-Joo

2012-11-15

Severe acute respiratory syndrome (SARS) was the first pandemic in the 21st century to claim more than 700 lives worldwide. However, effective anti-SARS vaccines or medications are currently unavailable despite being desperately needed to adequately prepare for a possible SARS outbreak. SARS is caused by a novel coronavirus, and one of its components, a viral helicase, is emerging as a promising target for the development of chemical SARS inhibitors. In the following review, we describe the characterization, family classification, and kinetic movement mechanisms of the SARS coronavirus (SCV) helicase-nsP13. We also discuss the recent progress in the identification of novel chemical inhibitors of nsP13 in the context of our recent discovery of the strong inhibition of the SARS helicase by natural flavonoids, myricetin and scutellarein. These compounds will serve as important resources for the future development of anti-SARS medications. Copyright © 2012 Elsevier Inc. All rights reserved.

Discrimination of coastal wetland environments in the Amazon region based on multi-polarized L-band airborne Synthetic Aperture Radar imagery

NASA Astrophysics Data System (ADS)

Souza-Filho, Pedro Walfir M.; Paradella, Waldir R.; Rodrigues, Suzan W. P.; Costa, Francisco R.; Mura, José C.; Gonçalves, Fabrício D.

2011-11-01

This study assessed the use of multi-polarized L-band images for the identification of coastal wetland environments in the Amazon coast region of northern Brazil. Data were acquired with a SAR R99B sensor from the Amazon Surveillance System (SIVAM) on board a Brazilian Air Force jet. Flights took place in the framework of the 2005 MAPSAR simulation campaign, a German-Brazilian feasibility study focusing on a L-band SAR satellite. Information retrieval was based on the recognition of the interaction between a radar signal and shallow-water morphology in intertidal areas, coastal dunes, mangroves, marshes and the coastal plateau. Regarding the performance of polarizations, VV was superior for recognizing intertidal area morphology under low spring tide conditions; HH for mapping coastal environments covered with forest and scrub vegetation such as mangrove and vegetated dunes, and HV was suitable for distinguishing transition zones between mangroves and coastal plateau. The statistical results for the classification maps expressed by kappa index and general accuracy were 83.3% and 0.734 for the multi-polarized color composition (R-HH, G-HV, B-VV), 80.7% and 0.694% for HH, 79.7% and 0.673% for VV, and 77.9% and 0.645% for HV amplitude image. The results indicate that use of multi-polarized L-band SAR is a valuable source of information aiming at the identification and discrimination of distinct geomorphic targets in tropical wetlands.

Automated connectionist-geostatistical classification as an approach to identify sea ice and land ice types, properties and provinces

NASA Astrophysics Data System (ADS)

Goetz-Weiss, L. R.; Herzfeld, U. C.; Trantow, T.; Hunke, E. C.; Maslanik, J. A.; Crocker, R. I.

2016-12-01

An important problem in model-data comparison is the identification of parameters that can be extracted from observational data as well as used in numerical models, which are typically based on idealized physical processes. Here, we present a suite of approaches to characterization and classification of sea ice and land ice types, properties and provinces based on several types of remote-sensing data. Applications will be given to not only illustrate the approach, but employ it in model evaluation and understanding of physical processes. (1) In a geostatistical characterization, spatial sea-ice properties in the Chukchi and Beaufort Sea and in Elsoon Lagoon are derived from analysis of RADARSAT and ERS-2 SAR data. (2) The analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification, which facilitates classification of different sea-ice types. (3) Characteristic sea-ice parameters, as resultant from the classification, can then be applied in model evaluation, as demonstrated for the ridging scheme of the Los Alamos sea ice model, CICE, using high-resolution altimeter and image data collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE). The characteristic parameters chosen in this application are directly related to deformation processes, which also underly the ridging scheme. (4) The method that is capable of the most complex classification tasks is the connectionist-geostatistical classification method. This approach has been developed to identify currently up to 18 different crevasse types in order to map progression of the surge through the complex Bering-Bagley Glacier System, Alaska, in 2011-2014. The analysis utilizes airborne altimeter data and video image data and satellite image data. Results of the crevasse classification are compare to fracture modeling and found to match.

Fusion of Modis and Palsar Principal Component Images Through Curvelet Transform for Land Cover Classification

NASA Astrophysics Data System (ADS)

Singh, Dharmendra; Kumar, Harish

Earth observation satellites provide data that covers different portions of the electromagnetic spectrum at different spatial and spectral resolutions. The increasing availability of information products generated from satellite images are extending the ability to understand the patterns and dynamics of the earth resource systems at all scales of inquiry. In which one of the most important application is the generation of land cover classification from satellite images for understanding the actual status of various land cover classes. The prospect for the use of satel-lite images in land cover classification is an extremely promising one. The quality of satellite images available for land-use mapping is improving rapidly by development of advanced sensor technology. Particularly noteworthy in this regard is the improved spatial and spectral reso-lution of the images captured by new satellite sensors like MODIS, ASTER, Landsat 7, and SPOT 5. For the full exploitation of increasingly sophisticated multisource data, fusion tech-niques are being developed. Fused images may enhance the interpretation capabilities. The images used for fusion have different temporal, and spatial resolution. Therefore, the fused image provides a more complete view of the observed objects. It is one of the main aim of image fusion to integrate different data in order to obtain more information that can be de-rived from each of the single sensor data alone. A good example of this is the fusion of images acquired by different sensors having a different spatial resolution and of different spectral res-olution. Researchers are applying the fusion technique since from three decades and propose various useful methods and techniques. The importance of high-quality synthesis of spectral information is well suited and implemented for land cover classification. More recently, an underlying multiresolution analysis employing the discrete wavelet transform has been used in image fusion. It was found that multisensor image fusion is a tradeoff between the spectral information from a low resolution multi-spectral images and the spatial information from a high resolution multi-spectral images. With the wavelet transform based fusion method, it is easy to control this tradeoff. A new transform, the curvelet transform was used in recent years by Starck. A ridgelet transform is applied to square blocks of detail frames of undecimated wavelet decomposition, consequently the curvelet transform is obtained. Since the ridgelet transform possesses basis functions matching directional straight lines therefore, the curvelet transform is capable of representing piecewise linear contours on multiple scales through few significant coefficients. This property leads to a better separation between geometric details and background noise, which may be easily reduced by thresholding curvelet coefficients before they are used for fusion. The Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instrument provides high radiometric sensitivity (12 bit) in 36 spectral bands ranging in wavelength from 0.4 m to 14.4 m and also it is freely available. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km. In this paper, the band 1 of spatial resolution 250 m and bandwidth 620-670 nm, and band 2, of spatial resolution of 250m and bandwidth 842-876 nm is considered as these bands has special features to identify the agriculture and other land covers. In January 2006, the Advanced Land Observing Satellite (ALOS) was successfully launched by the Japan Aerospace Exploration Agency (JAXA). The Phased Arraytype L-band SAR (PALSAR) sensor onboard the satellite acquires SAR imagery at a wavelength of 23.5 cm (frequency 1.27 GHz) with capabilities of multimode and multipolarization observation. PALSAR can operate in several modes: the fine-beam single (FBS) polarization mode (HH), fine-beam dual (FBD) polariza-tion mode (HH/HV or VV/VH), polarimetric (PLR) mode (HH/HV/VH/VV), and ScanSAR (WB) mode (HH/VV) [15]. These makes PALSAR imagery very attractive for spatially and temporally consistent monitoring system. The Overview of Principal Component Analysis is that the most of the information within all the bands can be compressed into a much smaller number of bands with little loss of information. It allows us to extract the low-dimensional subspaces that capture the main linear correlation among the high-dimensional image data. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. In this paper we have explored the fusion technique for enhancing the land cover classification of low resolution satellite data espe-cially freely available satellite data. For this purpose, we have considered to fuse the PALSAR principal component data with MODIS principal component data. Initially, the MODIS band 1 and band 2 is considered, its principal component is computed. Similarly the PALSAR HH, HV and VV polarized data are considered, and there principal component is also computed. con-sequently, the PALSAR principal component image is fused with MODIS principal component image. The aim of this paper is to analyze the effect of classification accuracy on major type of land cover types like agriculture, water and urban bodies with fusion of PALSAR data to MODIS data. Curvelet transformation has been applied for fusion of these two satellite images and Minimum Distance classification technique has been applied for the resultant fused image. It is qualitatively and visually observed that the overall classification accuracy of MODIS image after fusion is enhanced. This type of fusion technique may be quite helpful in near future to use freely available satellite data to develop monitoring system for different land cover classes on the earth.

Registering coherent change detection products associated with large image sets and long capture intervals

DOEpatents

Perkins, David Nikolaus; Gonzales, Antonio I

2014-04-08

A set of co-registered coherent change detection (CCD) products is produced from a set of temporally separated synthetic aperture radar (SAR) images of a target scene. A plurality of transformations are determined, which transformations are respectively for transforming a plurality of the SAR images to a predetermined image coordinate system. The transformations are used to create, from a set of CCD products produced from the set of SAR images, a corresponding set of co-registered CCD products.

Hardware Development and Error Characterization for the AFIT RAIL SAR System

DTIC Science & Technology

This research is focused on updating the Air Force Institute of Technology (AFIT) Radar Instrumentation Lab (RAIL)Synthetic Aperture Radar ( SAR ...collections from a receiver in motion. Secondly, orthogonal frequency-division multiplexing (OFDM) signals are used to form ( SAR ) images in multiple...experimental and simulation configurations. This research analyses, characterizes and attempts compensation of relevant SAR image error sources, such as Doppler

Developing an interactive teleradiology system for SARS diagnosis

NASA Astrophysics Data System (ADS)

Sun, Jianyong; Zhang, Jianguo; Zhuang, Jun; Chen, Xiaomeng; Yong, Yuanyuan; Tan, Yongqiang; Chen, Liu; Lian, Ping; Meng, Lili; Huang, H. K.

2004-04-01

Severe acute respiratory syndrome (SARS) is a respiratory illness that had been reported in Asia, North America, and Europe in last spring. Most of the China cases of SARS have occurred by infection in hospitals or among travelers. To protect the physicians, experts and nurses from the SARS during the diagnosis and treatment procedures, the infection control mechanisms were built in SARS hospitals. We built a Web-based interactive teleradiology system to assist the radiologists and physicians both in side and out side control area to make image diagnosis. The system consists of three major components: DICOM gateway (GW), Web-based image repository server (Server), and Web-based DICOM viewer (Viewer). This system was installed and integrated with CR, CT and the hospital information system (HIS) in Shanghai Xinhua hospital to provide image-based ePR functions for SARS consultation between the radiologists, physicians and experts inside and out side control area. The both users inside and out side the control area can use the system to process and manipulate the DICOM images interactively, and the system provide the remote control mechanism to synchronize their operations on images and display.

Comparison of JPL-AIRSAR and DLR E-SAR images from the MAC Europe 1991 campaign over testsite Oberpfaffenhofen: Frequency and polarization dependent backscatter variations from agricultural fields

NASA Technical Reports Server (NTRS)

Schmullius, C.; Nithack, J.

1992-01-01

On July 12, the MAC Europe '91 (Multi-Sensor Airborne Campaign) took place over test site Oberpfaffenhofen. The DLR Institute of Radio-Frequency Technology participated with its C-VV, X-VV, and X-HH Experimental Synthetic Aperture Radar (E-SAR). The high resolution E-SAR images with a pixel size between 1 and 2 m and the polarimetric AIRSAR images were analyzed. Using both sensors in combination is a unique opportunity to evaluate SAR images in a frequency range from P- to X-band and to investigate polarimetric information.

Bistatic SAR: Signal Processing and Image Formation.

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

Wahl, Daniel E.; Yocky, David A.

This report describes the significant processing steps that were used to take the raw recorded digitized signals from the bistatic synthetic aperture RADAR (SAR) hardware built for the NCNS Bistatic SAR project to a final bistatic SAR image. In general, the process steps herein are applicable to bistatic SAR signals that include the direct-path signal and the reflected signal. The steps include preprocessing steps, data extraction to for a phase history, and finally, image format. Various plots and values will be shown at most steps to illustrate the processing for a bistatic COSMO SkyMed collection gathered on June 10, 2013more » on Kirtland Air Force Base, New Mexico.« less

Geologic interpretation of Seasat SAR imagery near the Rio Lacantum, Mexico

NASA Technical Reports Server (NTRS)

Rebillard, PH.; Dixon, T.

1984-01-01

A mosaic of the Seasat Synthetic Aperture Radar (SAR) optically processed images over Central America is presented. A SAR image of the Rio Lacantum area (southeastern Mexico) has been digitally processed and its interpretation is presented. The region is characterized by low relief and a dense vegetation canopy. Surface is believed to be indicative of subsurface structural features. The Seasat-SAR system had a steep imaging geometry (incidence angle 23 + or - 3 deg off-nadir) which is favorable for detection of subtle topographic variations. Subtle textural features in the image corresponding to surface topography were enhanced by image processing techniques. A structural and lithologic interpretation of the processed images is presented. Lineaments oriented NE-SW dominate and intersect broad folds trending NW-SE. Distinctive karst topography characterizes one high relief area

An Accurate Co-registration Method for Airborne Repeat-pass InSAR

NASA Astrophysics Data System (ADS)

Dong, X. T.; Zhao, Y. H.; Yue, X. J.; Han, C. M.

2017-10-01

Interferometric Synthetic Aperture Radar (InSAR) technology plays a significant role in topographic mapping and surface deformation detection. Comparing with spaceborne repeat-pass InSAR, airborne repeat-pass InSAR solves the problems of long revisit time and low-resolution images. Due to the advantages of flexible, accurate, and fast obtaining abundant information, airborne repeat-pass InSAR is significant in deformation monitoring of shallow ground. In order to getting precise ground elevation information and interferometric coherence of deformation monitoring from master and slave images, accurate co-registration must be promised. Because of side looking, repeat observing path and long baseline, there are very different initial slant ranges and flight heights between repeat flight paths. The differences of initial slant ranges and flight height lead to the pixels, located identical coordinates on master and slave images, correspond to different size of ground resolution cells. The mismatching phenomenon performs very obvious on the long slant range parts of master image and slave image. In order to resolving the different sizes of pixels and getting accurate co-registration results, a new method is proposed based on Range-Doppler (RD) imaging model. VV-Polarization C-band airborne repeat-pass InSAR images were used in experiment. The experiment result shows that the proposed method leads to superior co-registration accuracy.

Reduction and coding of synthetic aperture radar data with Fourier transforms

NASA Technical Reports Server (NTRS)

Tilley, David G.

1995-01-01

Recently, aboard the Space Radar Laboratory (SRL), the two roles of Fourier Transforms for ocean image synthesis and surface wave analysis have been implemented with a dedicated radar processor to significantly reduce Synthetic Aperture Radar (SAR) ocean data before transmission to the ground. The object was to archive the SAR image spectrum, rather than the SAR image itself, to reduce data volume and capture the essential descriptors of the surface wave field. SAR signal data are usually sampled and coded in the time domain for transmission to the ground where Fourier Transforms are applied both to individual radar pulses and to long sequences of radar pulses to form two-dimensional images. High resolution images of the ocean often contain no striking features and subtle image modulations by wind generated surface waves are only apparent when large ocean regions are studied, with Fourier transforms, to reveal periodic patterns created by wind stress over the surface wave field. Major ocean currents and atmospheric instability in coastal environments are apparent as large scale modulations of SAR imagery. This paper explores the possibility of computing complex Fourier spectrum codes representing SAR images, transmitting the coded spectra to Earth for data archives and creating scenes of surface wave signatures and air-sea interactions via inverse Fourier transformations with ground station processors.

Speckle noise reduction in SAR images ship detection

NASA Astrophysics Data System (ADS)

Yuan, Ji; Wu, Bin; Yuan, Yuan; Huang, Qingqing; Chen, Jingbo; Ren, Lin

2012-09-01

At present, there are two types of method to detect ships in SAR images. One is a direct detection type, detecting ships directly. The other is an indirect detection type. That is, it firstly detects ship wakes, and then seeks ships around wakes. The two types all effect by speckle noise. In order to improve the accuracy of ship detection and get accurate ship and ship wakes parameters, such as ship length, ship width, ship area, the angle of ship wakes and ship outline from SAR images, it is extremely necessary to remove speckle noise in SAR images before data used in various SAR images ship detection. The use of speckle noise reduction filter depends on the specification for a particular application. Some common filters are widely used in speckle noise reduction, such as the mean filter, the median filter, the lee filter, the enhanced lee filter, the Kuan filter, the frost filter, the enhanced frost filter and gamma filter, but these filters represent some disadvantages in SAR image ship detection because of the various types of ship. Therefore, a mathematical function known as the wavelet transform and multi-resolution analysis were used to localize an SAR ocean image into different frequency components or useful subbands, and effectively reduce the speckle in the subbands according to the local statistics within the bands. Finally, the analysis of the statistical results are presented, which demonstrates the advantages and disadvantages of using wavelet shrinkage techniques over standard speckle filters.

The observation of ocean surface phenomena using imagery from the SEASAT synthetic aperture radar: An assessment

NASA Astrophysics Data System (ADS)

Vesecky, John F.; Stewart, Robert H.

1982-04-01

Over the period July 4 to October 10, 1978, the SEASAT synthetic aperture radar (SAR) gathered 23 cm wavelength radar images of some 108 km2 of the earth's surface, mainly of ocean areas, at 25-40 m resolution. Our assessment is in terms of oceanographic and ocean monitoring objectives and is directed toward discovering the proper role of SAR imagery in these areas of interest. In general, SAR appears to have two major and somewhat overlapping roles: first, quantitative measurement of ocean phenomena, like long gravity waves and wind fields, as well as measurement of ships; second, exploratory observations of large-scale ocean phenomena, such as the Gulf Stream and its eddies, internal waves, and ocean fronts. These roles are greatly enhanced by the ability of 23 cm SAR to operate day or night and through clouds. To begin we review some basics of synthetic aperture radar and its implementation on the SEASAT spacecraft. SEASAT SAR imagery of the ocean is fundamentally a map of the radar scattering characteristics of ˜30 cm wavelength ocean waves, distorted in some cases by ocean surface motion. We discuss how wind stress, surface currents, long gravity waves, and surface films modulate the scattering properties of these resonant waves with particular emphasis on the mechanisms that could produce images of long gravity waves. Doppler effects by ocean motion are also briefly described. Measurements of long (wavelength ≳100 m) gravity waves, using SEASAT SAR imagery, are compared with surface measurements during several experiments. Combining these results we find that dominant wavelength and direction are measured by SEASAT SAR within ±12% and ±15°, respectively. However, we note that ocean waves are not always visible in SAR images and discuss detection criteria in terms of wave height, length, and direction. SAR estimates of omnidirectional wave height spectra made by assuming that SAR image intensity is proportional to surface height fluctuations are more similar to corresponding surface measurements of wave height spectra than to wave slope spectra. Because SEASAT SAR images show the radar cross section σ° of ˜30 cm waves (neglecting doppler effects), and because these waves are raised by wind stress on the ocean surface, wind measurements are possible. Comparison between wind speeds estimated from SEASAT SAR imagery and from the SEASAT satellite scatterometer (SASS) agreed to within ±0.7 m s- over a 350-km comparison track and for wind speeds from 2 to 15 m s-. The great potential of SAR wind measurements lies in studying the spatial structure of the wind field over a range of spatial scales of from ≲1 km to ≳100 km. At present, the spatial and temporal structure of ocean wind fields is largely unknown. Because SAR responds to short waves whose energy density is a function of wind stress at the surface rather than wind speed at some distance above the surface, variations in image intensity may also reflect changes in air-sea temperature difference (thus complicating wind measurements by SAR). Because SAR images show the effects of surface current shear, air-sea temperature difference, and surface films through their modulation of the ˜30 cm waves, SEASAT images can be used to locate and study the Gulf Stream and related warm water rings, tidal flows at inlets, internal waves, and slicks resulting from surface films. In many of these applications, SAR provides a remote sensing capability that is complementary to infrared imagery because the two techniques sense largely different properties, namely, surface roughness and temperature. Both stationary ships and moving ships with their attendant wakes are often seen in SAR images. Ship images can be used to estimate ship size, heading, and speed. However, ships known to be in areas imaged by SAR are not always detectable. Clearly, a variety of factors, such as image resolution, ship size, sea state, and winds could affect ship detection. Overall, the role of SAR imagery in oceanography is definitely evolving at this time, but its ultimate role is unclear. We have assessed the ability of SEASAT SAR to measure a variety of ocean phenomena and have commented briefly on applications. In the end, oceanographers and others will have to judge from these capabilities the proper place for SAR in oceanography and remote sensing of the ocean.

Development of a satellite SAR image spectra and altimeter wave height data assimilation system for ERS-1

NASA Technical Reports Server (NTRS)

Hasselmann, Klaus; Hasselmann, Susanne; Bauer, Eva; Bruening, Claus; Lehner, Susanne; Graber, Hans; Lionello, Piero

1988-01-01

The applicability of ERS-1 wind and wave data for wave models was studied using the WAM third generation wave model and SEASAT altimeter, scatterometer and SAR data. A series of global wave hindcasts is made for the surface stress and surface wind fields by assimilation of scatterometer data for the full 96-day SEASAT and also for two wind field analyses for shorter periods by assimilation with the higher resolution ECMWF T63 model and by subjective analysis methods. It is found that wave models respond very sensitively to inconsistencies in wind field analyses and therefore provide a valuable data validation tool. Comparisons between SEASAT SAR image spectra and theoretical SAR spectra derived from the hindcast wave spectra by Monte Carlo simulations yield good overall agreement for 32 cases representing a wide variety of wave conditions. It is concluded that SAR wave imaging is sufficiently well understood to apply SAR image spectra with confidence for wave studies if supported by realistic wave models and theoretical computations of the strongly nonlinear mapping of the wave spectrum into the SAR image spectrum. A closed nonlinear integral expression for this spectral mapping relation is derived which avoids the inherent statistical errors of Monte Carlo computations and may prove to be more efficient numerically.

Relevant Scatterers Characterization in SAR Images

NASA Astrophysics Data System (ADS)

Chaabouni, Houda; Datcu, Mihai

2006-11-01

Recognizing scenes in a single look meter resolution Synthetic Aperture Radar (SAR) images, requires the capability to identify relevant signal signatures in condition of variable image acquisition geometry, arbitrary objects poses and configurations. Among the methods to detect relevant scatterers in SAR images, we can mention the internal coherence. The SAR spectrum splitted in azimuth generates a series of images which preserve high coherence only for particular object scattering. The detection of relevant scatterers can be done by correlation study or Independent Component Analysis (ICA) methods. The present article deals with the state of the art for SAR internal correlation analysis and proposes further extensions using elements of inference based on information theory applied to complex valued signals. The set of azimuth looks images is analyzed using mutual information measures and an equivalent channel capacity is derived. The localization of the "target" requires analysis in a small image window, thus resulting in imprecise estimation of the second order statistics of the signal. For a better precision, a Hausdorff measure is introduced. The method is applied to detect and characterize relevant objects in urban areas.

Condition assessment of corroded steel rebar in free space using synthetic aperture radar images

NASA Astrophysics Data System (ADS)

Ingemi, Christopher M.; Owusu Twumasi, Jones; Litt, Swinderjit; Yu, Tzuyang

2017-04-01

Synthetic aperture radar (SAR) imaging of construction materials offers civil engineers an opportunity to better assess the condition of aging civil infrastructures such as reinforced concrete (RC) structures. Corrosion of steel rebar in RC structures is a major problem responsible for their premature failure and unexpected collapse. In this paper, SAR imaging is applied to the quantitative assessment of corroded steel rebar in free space as the first step toward the use of SAR imaging for subsurface sensing of aging RC structures. A 10 GHz stripmap SAR system was used inside an anechoic chamber. The bandwidth of the radar system was 1.5 GHz. Steel rebar specimens were artificially corroded to different levels by regularly applying a mist of 5% NaCl solution for different durations of time in order to simulate the condition of natural corrosion. Two sizes (No. 3 and No. 4) of steel rebar were used in this research. Different orientations of steel rebar were considered. Corrosion level was determined by measuring the mass loss of corroded steel rebar specimens. From our results, feasibility of SAR images for the condition assessment of corroded steel rebar was experimentally demonstrated. It was found that the presence of surface rust on corroded steel rebar reduces the amplitude in SAR images. The SAR image of corroded steel rebar also exhibited a distribution of SAR amplitudes different from the one of intact steel rebar. In addition, it was also found that there is an optimal range for the condition assessment of corroded steel rebar in free space. In our experiment, the optimal range was determined to be 30.4 cm.

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

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

2015-11-01

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

An iterative approach to optimize change classification in SAR time series data

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

2016-10-01

The detection of changes using remote sensing imagery has become a broad field of research with many approaches for many different applications. Besides the simple detection of changes between at least two images acquired at different times, analyses which aim on the change type or category are at least equally important. In this study, an approach for a semi-automatic classification of change segments is presented. A sparse dataset is considered to ensure the fast and simple applicability for practical issues. The dataset is given by 15 high resolution (HR) TerraSAR-X (TSX) amplitude images acquired over a time period of one year (11/2013 to 11/2014). The scenery contains the airport of Stuttgart (GER) and its surroundings, including urban, rural, and suburban areas. Time series imagery offers the advantage of analyzing the change frequency of selected areas. In this study, the focus is set on the analysis of small-sized high frequently changing regions like parking areas, construction sites and collecting points consisting of high activity (HA) change objects. For each HA change object, suitable features are extracted and a k-means clustering is applied as the categorization step. Resulting clusters are finally compared to a previously introduced knowledge-based class catalogue, which is modified until an optimal class description results. In other words, the subjective understanding of the scenery semantics is optimized by the data given reality. Doing so, an even sparsely dataset containing only amplitude imagery can be evaluated without requiring comprehensive training datasets. Falsely defined classes might be rejected. Furthermore, classes which were defined too coarsely might be divided into sub-classes. Consequently, classes which were initially defined too narrowly might be merged. An optimal classification results when the combination of previously defined key indicators (e.g., number of clusters per class) reaches an optimum.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Knowlton, D. J.; Dean, M. E.

1981-01-01

A set of training statistics for the 30 meter resolution simulated thematic mapper MSS data was generated based on land use/land cover classes. In addition to this supervised data set, a nonsupervised multicluster block of training statistics is being defined in order to compare the classification results and evaluate the effect of the different training selection methods on classification performance. Two test data sets, defined using a stratified sampling procedure incorporating a grid system with dimensions of 50 lines by 50 columns, and another set based on an analyst supervised set of test fields were used to evaluate the classifications of the TMS data. The supervised training data set generated training statistics, and a per point Gaussian maximum likelihood classification of the 1979 TMS data was obtained. The August 1980 MSS data was radiometrically adjusted. The SAR data was redigitized and the SAR imagery was qualitatively analyzed.

Software For Tie-Point Registration Of SAR Data

NASA Technical Reports Server (NTRS)

Rignot, Eric; Dubois, Pascale; Okonek, Sharon; Van Zyl, Jacob; Burnette, Fred; Borgeaud, Maurice

1995-01-01

SAR-REG software package registers synthetic-aperture-radar (SAR) image data to common reference frame based on manual tie-pointing. Image data can be in binary, integer, floating-point, or AIRSAR compressed format. For example, with map of soil characteristics, vegetation map, digital elevation map, or SPOT multispectral image, as long as user can generate binary image to be used by tie-pointing routine and data are available in one of the previously mentioned formats. Written in FORTRAN 77.

A perspective of synthetic aperture radar for remote sensing

NASA Technical Reports Server (NTRS)

Skolnik, M. I.

1978-01-01

The characteristics and capabilities of synthetic aperture radar are discussed so as to identify those features particularly unique to SAR. The SAR and Optical images were compared. The SAR is an example of radar that provides more information about a target than simply its location. It is the spatial resolution and imaging capability of SAR that has made its application of interest, especially from spaceborne platforms. However, for maximum utility to remote sensing, it was proposed that other information be extracted from SAR data, such as the cross section with frequency and polarization.

The Low Backscattering Objects Classification in Polsar Image Based on Bag of Words Model Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Yang, L.; Shi, L.; Li, P.; Yang, J.; Zhao, L.; Zhao, B.

2018-04-01

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

NASA Astrophysics Data System (ADS)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Callegari, Mattia; Marin, Carlo; Notarnicola, Claudia; Carturan, Luca; Covi, Federico; Galos, Stephan; Seppi, Roberto

2016-10-01

In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as glacier ice in a following date) we here propose an innovative post classification correction based on SVM classification probabilities. Optical data, i.e. Landsat-8 and Sentinel-2, have been employed, when available, for training sample collection. Detailed field observations from two glaciers located in the Ortles-Cevedale massif (Eastern Italian Alps) have been employed for validation.

Calibration of a polarimetric imaging SAR

NASA Technical Reports Server (NTRS)

Sarabandi, K.; Pierce, L. E.; Ulaby, F. T.

1991-01-01

Calibration of polarimetric imaging Synthetic Aperture Radars (SAR's) using point calibration targets is discussed. The four-port network calibration technique is used to describe the radar error model. The polarimetric ambiguity function of the SAR is then found using a single point target, namely a trihedral corner reflector. Based on this, an estimate for the backscattering coefficient of the terrain is found by a deconvolution process. A radar image taken by the JPL Airborne SAR (AIRSAR) is used for verification of the deconvolution calibration method. The calibrated responses of point targets in the image are compared both with theory and the POLCAL technique. Also, response of a distributed target are compared using the deconvolution and POLCAL techniques.

A Modified Subpulse SAR Processing Procedure Based on the Range-Doppler Algorithm for Synthetic Wideband Waveforms

PubMed Central

Lim, Byoung-Gyun; Woo, Jea-Choon; Lee, Hee-Young; Kim, Young-Soo

2008-01-01

Synthetic wideband waveforms (SWW) combine a stepped frequency CW waveform and a chirp signal waveform to achieve high range resolution without requiring a large bandwidth or the consequent very high sampling rate. If an efficient algorithm like the range-Doppler algorithm (RDA) is used to acquire the SAR images for synthetic wideband signals, errors occur due to approximations, so the images may not show the best possible result. This paper proposes a modified subpulse SAR processing algorithm for synthetic wideband signals which is based on RDA. An experiment with an automobile-based SAR system showed that the proposed algorithm is quite accurate with a considerable improvement in resolution and quality of the obtained SAR image. PMID:27873984

Science Results from the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR): Progress Report

NASA Technical Reports Server (NTRS)

Evans, Diane L. (Editor); Plaut, Jeffrey (Editor)

1996-01-01

The Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) is the most advanced imaging radar system to fly in Earth orbit. Carried in the cargo bay of the Space Shuttle Endeavour in April and October of 1994, SIR-C/X-SAR simultaneously recorded SAR data at three wavelengths (L-, C-, and X-bands; 23.5, 5.8, and 3.1 cm, respectively). The SIR-C/X-SAR Science Team consists of 53 investigator teams from more than a dozen countries. Science investigations were undertaken in the fields of ecology, hydrology, ecology, and oceanography. This report contains 44 investigator team reports and several additional reports from coinvestigators and other researchers.

Hybrid space-airborne bistatic SAR geometric resolutions

NASA Astrophysics Data System (ADS)

Moccia, Antonio; Renga, Alfredo

2009-09-01

Performance analysis of Bistatic Synthetic Aperture Radar (SAR) characterized by arbitrary geometric configurations is usually complex and time-consuming since system impulse response has to be evaluated by bistatic SAR processing. This approach does not allow derivation of general equations regulating the behaviour of image resolutions with varying the observation geometry. It is well known that for an arbitrary configuration of bistatic SAR there are not perpendicular range and azimuth directions, but the capability to produce an image is not prevented as it depends only on the possibility to generate image pixels from time delay and Doppler measurements. However, even if separately range and Doppler resolutions are good, bistatic SAR geometries can exist in which imaging capabilities are very poor when range and Doppler directions become locally parallel. The present paper aims to derive analytical tools for calculating the geometric resolutions of arbitrary configuration of bistatic SAR. The method has been applied to a hybrid bistatic Synthetic Aperture Radar formed by a spaceborne illuminator and a receiving-only airborne forward-looking Synthetic Aperture Radar (F-SAR). It can take advantage of the spaceborne illuminator to dodge the limitations of monostatic FSAR. Basic modeling and best illumination conditions have been detailed in the paper.

Damage extraction of buildings in the 2015 Gorkha, Nepal earthquake from high-resolution SAR data

NASA Astrophysics Data System (ADS)

Yamazaki, Fumio; Bahri, Rendy; Liu, Wen; Sasagawa, Tadashi

2016-05-01

Satellite remote sensing is recognized as one of the effective tools for detecting and monitoring affected areas due to natural disasters. Since SAR sensors can capture images not only at daytime but also at nighttime and under cloud-cover conditions, they are especially useful at an emergency response period. In this study, multi-temporal high-resolution TerraSAR-X images were used for damage inspection of the Kathmandu area, which was severely affected by the April 25, 2015 Gorkha Earthquake. The SAR images obtained before and after the earthquake were utilized for calculating the difference and correlation coefficient of backscatter. The affected areas were identified by high values of the absolute difference and low values of the correlation coefficient. The post-event high-resolution optical satellite images were employed as ground truth data to verify our results. Although it was difficult to estimate the damage levels for individual buildings, the high resolution SAR images could illustrate their capability in detecting collapsed buildings at emergency response times.

Synthetic aperture radar/LANDSAT MSS image registration

NASA Technical Reports Server (NTRS)

Maurer, H. E. (Editor); Oberholtzer, J. D. (Editor); Anuta, P. E. (Editor)

1979-01-01

Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint.

Impulse Response Shaping for Ultra Wide Band SAR in a Circular Flight Path

NASA Technical Reports Server (NTRS)

Jin, Michael Y.

1996-01-01

An ultra wide band SAR (synthetic aperture radar) has potential applications on imaging underground objects. Flying this SAR in a circular flight path is an efficient way to acquire high resolution images from a localized area. This paper characterizes the impulse response of sucha system. The results indicate that to achieve an image with a more uniformed resolution over the entire imaged area, proper weighting coeficients should be applied to both the principle aperture and the complimentary aperture.

Registration of interferometric SAR images

NASA Technical Reports Server (NTRS)

Lin, Qian; Vesecky, John F.; Zebker, Howard A.

1992-01-01

Interferometric synthetic aperture radar (INSAR) is a new way of performing topography mapping. Among the factors critical to mapping accuracy is the registration of the complex SAR images from repeated orbits. A new algorithm for registering interferometric SAR images is presented. A new figure of merit, the average fluctuation function of the phase difference image, is proposed to evaluate the fringe pattern quality. The process of adjusting the registration parameters according to the fringe pattern quality is optimized through a downhill simplex minimization algorithm. The results of applying the proposed algorithm to register two pairs of Seasat SAR images with a short baseline (75 m) and a long baseline (500 m) are shown. It is found that the average fluctuation function is a very stable measure of fringe pattern quality allowing very accurate registration.

SAR-based change detection using hypothesis testing and Markov random field modelling

NASA Astrophysics Data System (ADS)

Cao, W.; Martinis, S.

2015-04-01

The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

Ground Displacement Measurement of the 2013 Balochistan Earthquake with interferometric TerraSAR-X ScanSAR data

NASA Astrophysics Data System (ADS)

Yague-Martinez, N.; Fielding, E. J.; Haghshenas-Haghighi, M.; Cong, X.; Motagh, M.

2014-12-01

This presentation will address the 24 September 2013 Mw 7.7 Balochistan Earthquake in western Pakistan from the point of view of interferometric processing algorithms of wide-swath TerraSAR-X ScanSAR images. The algorithms are also valid for TOPS acquisition mode, the operational mode of the Sentinel-1A ESA satellite that was successfully launched in April 2014. Spectral properties of burst-mode data and an overview of the interferometric processing steps of burst-mode acquisitions, emphasizing the importance of the co-registration stage, will be provided. A co-registration approach based on incoherent cross-correlation will be presented and applied to seismic scenarios. Moreover geodynamic corrections due to differential atmospheric path delay and differential solid Earth tides are considered to achieve accuracy in the order of several centimeters. We previously derived a 3D displacement map using cross-correlation techniques applied to optical images from Landsat-8 satellite and TerraSAR-X ScanSAR amplitude images. The Landsat-8 cross-correlation measurements cover two horizontal directions, and the TerraSAR-X displacements include both horizontal along-track and slant-range (radar line-of-sight) measurements that are sensitive to vertical and horizontal deformation. It will be justified that the co-seismic displacement map from TerraSAR-X ScanSAR data may be contaminated by postseismic deformation due to the fact that the post-seismic acquisition took place one month after the main shock, confirmed in part by a TerraSAR-X stripmap interferogram (processed with conventional InSAR) covering part of the area starting on 27 September 2013. We have arranged the acquisition of a burst-synchronized stack of TerraSAR-X ScanSAR images over the affected area after the earthquake. It will be possible to apply interferometry to these data to measure the lower magnitude of the expected postseismic displacements. The processing of single interferograms will be discussed. A quicklook of the wrapped differential TerraSAR-X ScanSAR co-seismic interferogram is provided in the attachment (range coverage is 100 km by using 4 subswaths).

Evaluation of C-band SAR data from SAREX 1992: Tapajos study site

NASA Technical Reports Server (NTRS)

Shimabukuro, Yosio Edemir; Filho, Pedro Hernandez; Lee, David Chung Liang; Ahern, F. J.; Paivadossantosfilho, Celio; Rolodealmeida, Rionaldo

1993-01-01

As part of the SAREX'92 (South American Radar Experiment), the Tapajos study site, located in Para State, Brazil was imaged by the Canada Center for Remote Sensing (CCRS) Convair 580 SAR system using a C-band frequency in HH and VV polarization and 3 different imaging modes (nadir, narrow, and wide swath). A preliminary analysis of this dataset is presented. The wide swath C-band HH polarized image was enlarged to 1:100,000 in a photographic form for manual interpretation. This was compared with a vegetation map produced primarily from Landsat Thematic Mapper (TM) data and with single-band and color composite images derived from a decomposition analysis of TM data. The Synthetic Aperture Radar (SAR) image shows well the topography and drainage network defining the different geomorphological units, and canopy texture differences which appear to be related to the size and maturity of the forest canopy. Areas of recent clearing of the primary forest can also be identified on the SAR image. The SAR system appears to be a source of information for monitoring tropical forest which is complementary to the Landsat Thematic Mapper.

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Half-quadratic variational regularization methods for speckle-suppression and edge-enhancement in SAR complex image

NASA Astrophysics Data System (ADS)

Zhao, Xia; Wang, Guang-xin

2008-12-01

Synthetic aperture radar (SAR) is an active remote sensing sensor. It is a coherent imaging system, the speckle is its inherent default, which affects badly the interpretation and recognition of the SAR targets. Conventional methods of removing the speckle is studied usually in real SAR image, which reduce the edges of the images at the same time as depressing the speckle. Morever, Conventional methods lost the information about images phase. Removing the speckle and enhancing the target and edge simultaneously are still a puzzle. To suppress the spckle and enhance the targets and the edges simultaneously, a half-quadratic variational regularization method in complex SAR image is presented, which is based on the prior knowledge of the targets and the edge. Due to the non-quadratic and non- convex quality and the complexity of the cost function, a half-quadratic variational regularization variation is used to construct a new cost function,which is solved by alternate optimization. In the proposed scheme, the construction of the model, the solution of the model and the selection of the model peremeters are studied carefully. In the end, we validate the method using the real SAR data.Theoretic analysis and the experimental results illustrate the the feasibility of the proposed method. Further more, the proposed method can preserve the information about images phase.

Rapid Disaster Analysis based on Remote Sensing: A Case Study about the Tohoku Tsunami Disaster 2011

NASA Astrophysics Data System (ADS)

Yang, C. H.; Soergel, U.; Lanaras, Ch.; Baltsavias, E.; Cho, K.; Remondino, F.; Wakabayashi, H.

2014-09-01

In this study, we present first results of RAPIDMAP, a project funded by European Union in a framework aiming to foster the cooperation of European countries with Japan in R&D. The main objective of RAPIDMAP is to construct a Decision Support System (DSS) based on remote sensing data and WebGIS technologies, where users can easily access real-time information assisting with disaster analysis. In this paper, we present a case study of the Tohoku Tsunami Disaster 2011. We address two approaches namely change detection based on SAR data and co-registration of optical and SAR satellite images. With respect to SAR data, our efforts are subdivided into three parts: (1) initial coarse change detection for entire area, (2) flood area detection, and (3) linearfeature change detection. The investigations are based on pre- and post-event TerraSAR-X images. In (1), two pre- and post-event TerraSAR-X images are accurately co-registered and radiometrically calibrated. Data are fused in a false-color image that provides a quick and rough overview of potential changes, which is useful for initial decision making and identifying areas worthwhile to be analysed further in more depth. However, a bunch of inevitable false alarms appear within the scene caused by speckle, temporal decorrelation, co-registration inaccuracy and so on. In (2), the post-event TerraSAR-X data are used to extract the flood area by using thresholding and morphological approaches. The validated result indicates that using SAR data combining with suitable morphological approaches is a quick and effective way to detect flood area. Except for usage of SAR data, the false-color image composed of optical images are also used to detect flood area for further exploration in this part. In (3), Curvelet filtering is applied in the difference image of pre- and post-event TerraSAR-X images not only to suppress false alarms of irregular-features, but also to enhance the change signals of linear-features (e.g. buildings) in settlements. Afterwards, thresholding is exploited to extract the linear-feature changes. In rapid mapping of disasters various sensors are often employed, including optical and SAR, since they provide complementary information. Such data needs to be analyzed in an integrated fashion and the results from each dataset should be integrated in a GIS with a common coordinate reference system. Thus, if no orthoimages can be generated, the images should be co-registered employing matching of common features. We present results of co-registration between optical (FORMOSAT-2) and TerraSAR-X images based on different matching methods, and also techniques for detecting and eliminating matching errors.

Large Scale Assessment of Radio Frequency Interference Signatures in L-band SAR Data

NASA Astrophysics Data System (ADS)

Meyer, F. J.; Nicoll, J.

2011-12-01

Imagery of L-band Synthetic Aperture Radar (SAR) systems such as the PALSAR sensor on board the Advanced Land Observing Satellite (ALOS) has proven to be a valuable tool for observing environmental changes around the globe. Besides offering 24/7 operability, the L-band frequency provides improved interferometric coherence, and L-band polarimetric data has shown great potential for vegetation monitoring, sea ice classification, and the observation of glaciers and ice sheets. To maximize the benefit of missions such as ALOS PALSAR for environmental monitoring, data consistency and calibration are vital. Unfortunately, radio frequency interference (RFI) signatures from ground-based radar systems regularly impair L-band SAR data quality and consistency. With this study we present a large-scale analysis of typical RFI signatures that are regularly observed in L-band SAR data over the Americas. Through a study of the vast archive of L-band SAR data in the US Government Research Consortium (USGRC) data pool at the Alaska Satellite Facility (ASF) we were able to address the following research goals: 1. Assessment of RFI Signatures in L-band SAR data and their Effects on SAR Data Quality: An analysis of time-frequency properties of RFI signatures in L-band SAR data of the USGRC data pool is presented. It is shown that RFI-filtering algorithms implemented in the operational ALOS PALSAR processor are not sufficient to remove all RFI-related artifacts. In examples, the deleterious effects of RFI on SAR image quality, polarimetric signature, SAR phase, and interferometric coherence are presented. 2. Large-Scale Assessment of Severity, Spatial Distribution, and Temporal Variation of RFI Signatures in L-band SAR data: L-band SAR data in the USGRC data pool were screened for RFI using a custom algorithm. Per SAR frame, the algorithm creates geocoded frame bounding boxes that are color-coded according to RFI intensity and converted to KML files for analysis in Google Earth. From the screening results, parameters such as RFI severity and spatial distribution of RFI were derived. Through a comparison of RFI signatures in older SAR data from JAXA's Japanese Earth Resources Satellite (JERS-1) and recent ALOS PALSAR data, changes in RFI signatures in the Americas were derived, indicating a strong increase of L-band signal contamination over time. 3. An Optimized RFI Filter and its Performance in Data Restoration: An optimized RFI filter has been developed and tested at ASF. The algorithm has proven to be effective in detecting and removing RFI signatures in L-band SAR data and restoring the advertised quality of SAR imagery, polarization, and interferometric phase. The properties of the RFI filter will be described and its performance will be demonstrated in examples. The presented work is a prime example of large-scale research that is made possible by the availability of SAR data through the extensive data archive of the USGRC data pool at ASF.

Decreasing range resolution of a SAR image to permit correction of motion measurement errors beyond the SAR range resolution

DOEpatents

Doerry, Armin W.; Heard, Freddie E.; Cordaro, J. Thomas

2010-07-20

Motion measurement errors that extend beyond the range resolution of a synthetic aperture radar (SAR) can be corrected by effectively decreasing the range resolution of the SAR in order to permit measurement of the error. Range profiles can be compared across the slow-time dimension of the input data in order to estimate the error. Once the error has been determined, appropriate frequency and phase correction can be applied to the uncompressed input data, after which range and azimuth compression can be performed to produce a desired SAR image.

Synthetic aperture radar images of ocean waves, theories of imaging physics and experimental tests

NASA Technical Reports Server (NTRS)

Vesecky, J. F.; Durden, S. L.; Smith, M. P.; Napolitano, D. A.

1984-01-01

The physical mechanism for the synthetic Aperture Radar (SAR) imaging of ocean waves is investigated through the use of analytical models. The models are tested by comparison with data sets from the SEASAT mission and airborne SAR's. Dominant ocean wavelengths from SAR estimates are biased towards longer wavelengths. The quasispecular scattering mechanism agrees with experimental data. The Doppler shift for ship wakes is that of the mean sea surface.

Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration

NASA Astrophysics Data System (ADS)

Wang, Xianmin; Li, Bo; Xu, Qizhi

2016-07-01

The anisotropic scale space (ASS) is often used to enhance the performance of a scale-invariant feature transform (SIFT) algorithm in the registration of synthetic aperture radar (SAR) images. The existing ASS-based methods usually suffer from unstable keypoints and false matches, since the anisotropic diffusion filtering has limitations in reducing the speckle noise from SAR images while building the ASS image representation. We proposed a speckle reducing SIFT match method to obtain stable keypoints and acquire precise matches for the SAR image registration. First, the keypoints are detected in a speckle reducing anisotropic scale space constructed by the speckle reducing anisotropic diffusion, so that speckle noise is greatly reduced and prominent structures of the images are preserved, consequently the stable keypoints can be derived. Next, the probabilistic relaxation labeling approach is employed to establish the matches of the keypoints then the correct match rate of the keypoints is significantly increased. Experiments conducted on simulated speckled images and real SAR images demonstrate the effectiveness of the proposed method.

A new automatic SAR-based flood mapping application hosted on the European Space Agency's grid processing on demand fast access to imagery environment

NASA Astrophysics Data System (ADS)

Hostache, Renaud; Chini, Marco; Matgen, Patrick; Giustarini, Laura

2013-04-01

There is a clear need for developing innovative processing chains based on earth observation (EO) data to generate products supporting emergency response and flood management at a global scale. Here an automatic flood mapping application is introduced. The latter is currently hosted on the Grid Processing on Demand (G-POD) Fast Access to Imagery (Faire) environment of the European Space Agency. The main objective of the online application is to deliver flooded areas using both recent and historical acquisitions of SAR data in an operational framework. It is worth mentioning that the method can be applied to both medium and high resolution SAR images. The flood mapping application consists of two main blocks: 1) A set of query tools for selecting the "crisis image" and the optimal corresponding pre-flood "reference image" from the G-POD archive. 2) An algorithm for extracting flooded areas using the previously selected "crisis image" and "reference image". The proposed method is a hybrid methodology, which combines histogram thresholding, region growing and change detection as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. The method is based on the calibration of a statistical distribution of "open water" backscatter values inferred from SAR images of floods. Change detection with respect to a pre-flood reference image helps reducing over-detection of inundated areas. The algorithms are computationally efficient and operate with minimum data requirements, considering as input data a flood image and a reference image. Stakeholders in flood management and service providers are able to log onto the flood mapping application to get support for the retrieval, from the rolling archive, of the most appropriate pre-flood reference image. Potential users will also be able to apply the implemented flood delineation algorithm. Case studies of several recent high magnitude flooding events (e.g. July 2007 Severn River flood, UK and March 2010 Red River flood, US) observed by high-resolution SAR sensors as well as airborne photography highlight advantages and limitations of the online application. A mid-term target is the exploitation of ESA SENTINEL 1 SAR data streams. In the long term it is foreseen to develop a potential extension of the application for systematically extracting flooded areas from all SAR images acquired on a daily, weekly or monthly basis. On-going research activities investigate the usefulness of the method for mapping flood hazard at global scale using databases of historic SAR remote sensing-derived flood inundation maps.

3D Tomographic SAR Imaging in Densely Vegetated Mountainous Rural Areas in China and Sweden

NASA Astrophysics Data System (ADS)

Feng, L.; Muller, J. P., , Prof

2017-12-01

3D SAR Tomography (TomoSAR) and 4D SAR Differential Tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to create an important new innovation of SAR Interferometry, to unscramble complex scenes with multiple scatterers mapped into the same SAR cell. In addition to this 3-D shape reconstruction and deformation solution in complex urban/infrastructure areas, and recent cryospheric ice investigations, emerging tomographic remote sensing applications include forest applications, e.g. tree height and biomass estimation, sub-canopy topographic mapping, and even search, rescue and surveillance. However, these scenes are characterized by temporal decorrelation of scatterers, orbital, tropospheric and ionospheric phase distortion and an open issue regarding possible height blurring and accuracy losses for TomoSAR applications particularly in densely vegetated mountainous rural areas. Thus, it is important to develop solutions for temporal decorrelation, orbital, tropospheric and ionospheric phase distortion.We report here on 3D imaging (especially in vertical layers) over densely vegetated mountainous rural areas using 3-D SAR imaging (SAR tomography) derived from data stacks of X-band COSMO-SkyMed Spotlight and L band ALOS-1 PALSAR data stacks over Dujiangyan Dam, Sichuan, China and L and P band airborne SAR data (BioSAR 2008 - ESA) in the Krycklan river catchment, Northern Sweden. The new TanDEM-X 12m DEM is used to assist co - registration of all the data stacks over China first. Then, atmospheric correction is being assessed using weather model data such as ERA-I, MERRA, MERRA-2, WRF; linear phase-topography correction and MODIS spectrometer correction will be compared and ionospheric correction methods are discussed to remove tropospheric and ionospheric delay. Then the new TomoSAR method with the TanDEM-X 12m DEM is described to obtain the number of scatterers inside each pixel, the scattering amplitude and phase of each scatterer and finally extract tomograms (imaging), their 3D positions and motion parameters (deformation). A progress report will be shown on these different aspects.This work is partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL.

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

PubMed Central

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-01-01

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE. PMID:27447635

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

PubMed

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-07-19

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.

Apodized RFI filtering of synthetic aperture radar images

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

Doerry, Armin Walter

2014-02-01

Fine resolution Synthetic Aperture Radar (SAR) systems necessarily require wide bandwidths that often overlap spectrum utilized by other wireless services. These other emitters pose a source of Radio Frequency Interference (RFI) to the SAR echo signals that degrades SAR image quality. Filtering, or excising, the offending spectral contaminants will mitigate the interference, but at a cost of often degrading the SAR image in other ways, notably by raising offensive sidelobe levels. This report proposes borrowing an idea from nonlinear sidelobe apodization techniques to suppress interference without the attendant increase in sidelobe levels. The simple post-processing technique is termed Apodized RFImore » Filtering (ARF).« less

Radar transponder apparatus and signal processing technique

DOEpatents

Axline, Jr., Robert M.; Sloan, George R.; Spalding, Richard E.

1996-01-01

An active, phase-coded, time-grating transponder and a synthetic-aperture radar (SAR) and signal processor means, in combination, allow the recognition and location of the transponder (tag) in the SAR image and allow communication of information messages from the transponder to the SAR. The SAR is an illuminating radar having special processing modifications in an image-formation processor to receive an echo from a remote transponder, after the transponder receives and retransmits the SAR illuminations, and to enhance the transponder's echo relative to surrounding ground clutter by recognizing special transponder modulations from phase-shifted from the transponder retransmissions. The remote radio-frequency tag also transmits information to the SAR through a single antenna that also serves to receive the SAR illuminations. Unique tag-modulation and SAR signal processing techniques, in combination, allow the detection and precise geographical location of the tag through the reduction of interfering signals from ground clutter, and allow communication of environmental and status information from said tag to be communicated to said SAR.

Radar transponder apparatus and signal processing technique

DOEpatents

Axline, R.M. Jr.; Sloan, G.R.; Spalding, R.E.

1996-01-23

An active, phase-coded, time-grating transponder and a synthetic-aperture radar (SAR) and signal processor means, in combination, allow the recognition and location of the transponder (tag) in the SAR image and allow communication of information messages from the transponder to the SAR. The SAR is an illuminating radar having special processing modifications in an image-formation processor to receive an echo from a remote transponder, after the transponder receives and retransmits the SAR illuminations, and to enhance the transponder`s echo relative to surrounding ground clutter by recognizing special transponder modulations from phase-shifted from the transponder retransmissions. The remote radio-frequency tag also transmits information to the SAR through a single antenna that also serves to receive the SAR illuminations. Unique tag-modulation and SAR signal processing techniques, in combination, allow the detection and precise geographical location of the tag through the reduction of interfering signals from ground clutter, and allow communication of environmental and status information from said tag to be communicated to said SAR. 4 figs.

Ionospheric Specifications for SAR Interferometry (ISSI)

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Chapman, Bruce D; Freeman, Anthony; Szeliga, Walter; Buckley, Sean M.; Rosen, Paul A.; Lavalle, Marco

2013-01-01

The ISSI software package is designed to image the ionosphere from space by calibrating and processing polarimetric synthetic aperture radar (PolSAR) data collected from low Earth orbit satellites. Signals transmitted and received by a PolSAR are subject to the Faraday rotation effect as they traverse the magnetized ionosphere. The ISSI algorithms combine the horizontally and vertically polarized (with respect to the radar system) SAR signals to estimate Faraday rotation and ionospheric total electron content (TEC) with spatial resolutions of sub-kilometers to kilometers, and to derive radar system calibration parameters. The ISSI software package has been designed and developed to integrate the algorithms, process PolSAR data, and image as well as visualize the ionospheric measurements. A number of tests have been conducted using ISSI with PolSAR data collected from various latitude regions using the phase array-type L-band synthetic aperture radar (PALSAR) onboard Japan Aerospace Exploration Agency's Advanced Land Observing Satellite mission, and also with Global Positioning System data. These tests have demonstrated and validated SAR-derived ionospheric images and data correction algorithms.

Results of the 1989 experiment with a polarimetric multifrequency SAR

NASA Astrophysics Data System (ADS)

Groot, J. S.; Vandenbroek, A. C.

1992-03-01

In August 1989, a single day measurement campaign was conducted with two airborne imaging radars, the Dutch X band Side Looking Airborne Radar (SLAR) and the P/L/C band polarimetric Synthetic Aperture Radar (SAR). Test sites were the Flevopolder and the Veluwe (agricultural and forested areas, respectively) in the Netherlands. Ground activities included the deployment of several calibration devices like different sized trihedrals, dihedrals, and PARC's (active calibrators). An extensive experiment description is presented. Relevant details of the two radar systems and the calibration devices used are given. The calibration of the SLAR and SAR data is described. The calibrated data is used to investigate its potential for the classification of agricultural crop types. Several quantities are extracted from the data, which are used to do this. Examples are the copolarization phase difference distribution, the degree of polarization, and copolarized signatures. It appears to be quite possible to discriminate bare soil fields from vegetated fields using polarimetric quantities like the HH/VV (Horizontal Horizontal/Vertical Vertical) phase difference distribution and the degree of polarization. However, discrimination between fields with different crop types is much more difficult probably due to interference of features not related to the crop type, such as soil moisture, soil roughness, lodging, etc.

Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery

PubMed Central

Taravat, Alireza; Oppelt, Natascha

2014-01-01

Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies. PMID:25474376

Space Radar Image of West Texas - SAR scan

NASA Technical Reports Server (NTRS)

1999-01-01

This radar image of the Midland/Odessa region of West Texas, demonstrates an experimental technique, called ScanSAR, that allows scientists to rapidly image large areas of the Earth's surface. The large image covers an area 245 kilometers by 225 kilometers (152 miles by 139 miles). It was obtained by the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) flying aboard the space shuttle Endeavour on October 5, 1994. The smaller inset image is a standard SIR-C image showing a portion of the same area, 100 kilometers by 57 kilometers (62 miles by 35 miles) and was taken during the first flight of SIR-C on April 14, 1994. The bright spots on the right side of the image are the cities of Odessa (left) and Midland (right), Texas. The Pecos River runs from the top center to the bottom center of the image. Along the left side of the image are, from top to bottom, parts of the Guadalupe, Davis and Santiago Mountains. North is toward the upper right. Unlike conventional radar imaging, in which a radar continuously illuminates a single ground swath as the space shuttle passes over the terrain, a Scansar radar illuminates several adjacent ground swaths almost simultaneously, by 'scanning' the radar beam across a large area in a rapid sequence. The adjacent swaths, typically about 50 km (31 miles) wide, are then merged during ground processing to produce a single large scene. Illumination for this L-band scene is from the top of the image. The beams were scanned from the top of the scene to the bottom, as the shuttle flew from left to right. This scene was acquired in about 30 seconds. A normal SIR-C image is acquired in about 13 seconds. The ScanSAR mode will likely be used on future radar sensors to construct regional and possibly global radar images and topographic maps. The ScanSAR processor is being designed for 1996 implementation at NASA's Alaska SAR Facility, located at the University of Alaska Fairbanks, and will produce digital images from the forthcoming Canadian RADARSAT satellite. Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves, allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI), with the Deutsche Forschungsanstalt fuer Luft und Raumfahrt e.v.(DLR), the major partner in science, operations, and data processing of X-SAR.

Ocean Remote Sensing from Chinese Spaceborne Microwave Sensors

NASA Astrophysics Data System (ADS)

Yang, J.

2017-12-01

GF-3 (GF stands for GaoFen, which means High Resolution in Chinese) is the China's first C band multi-polarization high resolution microwave remote sensing satellite. It was successfully launched on Aug. 10, 2016 in Taiyuan satellite launch center. The synthetic aperture radar (SAR) on board GF-3 works at incidence angles ranging from 20 to 50 degree with several polarization modes including single-polarization, dual-polarization and quad-polarization. GF-3 SAR is also the world's most imaging modes SAR satellite, with 12 imaging modes consisting of some traditional ones like stripmap and scanSAR modes and some new ones like spotlight, wave and global modes. GF-3 SAR is thus a multi-functional satellite for both land and ocean observation by switching the different imaging modes. TG-2 (TG stands for TianGong, which means Heavenly Palace in Chinese) is a Chinese space laboratory which was launched on 15 Sep. 2016 from Jiuquan Satellite Launch Centre aboard a Long March 2F rocket. The onboard Interferometric Imaging Radar Altimeter (InIRA) is a new generation radar altimeter developed by China and also the first on orbit wide swath imaging radar altimeter, which integrates interferometry, synthetic aperture, and height tracking techniques at small incidence angles and a swath of 30 km. The InIRA was switch on to acquire data during this mission on 22 September. This paper gives some preliminary results for the quantitative remote sensing of ocean winds and waves from the GF-3 SAR and the TG-2 InIRA. The quantitative analysis and ocean wave spectra retrieval have been given from the SAR imagery. The image spectra which contain ocean wave information are first estimated from image's modulation using fast Fourier transform. Then, the wave spectra are retrieved from image spectra based on Hasselmann's classical quasi-linear SAR-ocean wave mapping model and the estimation of three modulation transfer functions (MTFs) including tilt, hydrodynamic and velocity bunching modulation. The wind speed is retrieved from InIRA data using a Ku-band low incidence backscatter model (KuLMOD), which relates the backscattering coefficients to the wind speeds and incidence angles. The ocean wave spectra are retrieved linearly from image spectra which extracted first from InIRA data, using a similar procedure for GF-3 SAR data.

Reducing Speckle In One-Look SAR Images

NASA Technical Reports Server (NTRS)

Nathan, K. S.; Curlander, J. C.

1990-01-01

Local-adaptive-filter algorithm incorporated into digital processing of synthetic-aperture-radar (SAR) echo data to reduce speckle in resulting imagery. Involves use of image statistics in vicinity of each picture element, in conjunction with original intensity of element, to estimate brightness more nearly proportional to true radar reflectance of corresponding target. Increases ratio of signal to speckle noise without substantial degradation of resolution common to multilook SAR images. Adapts to local variations of statistics within scene, preserving subtle details. Computationally simple. Lends itself to parallel processing of different segments of image, making possible increased throughput.

Estimating IMU heading error from SAR images.

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

Doerry, Armin Walter

Angular orientation errors of the real antenna for Synthetic Aperture Radar (SAR) will manifest as undesired illumination gradients in SAR images. These gradients can be measured, and the pointing error can be calculated. This can be done for single images, but done more robustly using multi-image methods. Several methods are provided in this report. The pointing error can then be fed back to the navigation Kalman filter to correct for problematic heading (yaw) error drift. This can mitigate the need for uncomfortable and undesired IMU alignment maneuvers such as S-turns.

Preliminary study of internal wave effects to chlorophyll distribution in the Lombok Strait and adjacent areas

NASA Astrophysics Data System (ADS)

Arvelyna, Yessy; Oshima, Masaki

2005-01-01

This paper studies the effect of internal wave in the Lombok Strait to chlorophyll distribution in the surrounded areas using ERS SAR, ASTER, SeaWiFS and AVHRR-NOAA images data during 1996-2004 periods. The observation results shows that the internal waves were propagated to the south and the north of strait and mostly occurred during transitional season from dry to wet and wet season (rainy season) between September to December when the layers are strongly stratified. Wavelet transform of image using Meyer wavelet analysis is applied for internal wave detection in ERS SAR and ASTER images, for symmetric extension of data at the image boundaries, to prevent discontinuities by a periodic wrapping of data in fast algorithm and space-saving code. Internal wave created elongated pattern in detail and approximation of image from level 2 to 5 and retained value between 2-4.59 times compared to sea surface, provided accuracy in classification over than 80%. In segmentation process, the Canny edge detector is applied on the approximation image at level two to derive internal wave signature in image. The proposed method can extract the internal wave signature, maintain the continuity of crest line while reduce small strikes from noise. The segmentation result, i.e. the length between crest and trough, is used to compute the internal wave induced current using Korteweg-de Vries (KdV) equation. On ERS SAR data contains surface signature of internal wave (2001/8/20), we calculated that internal wave propagation speed was 1.2 m/s and internal wave induced current was 0.56 m/s, respectively. From the observation of ERS SAR and SeaWiFS images data, we found out that the distribution of maximum chlorophyll area at southern coastline off Bali Island when strong internal wave induced current occurred in south of the Lombok Strait was distributed further to westward, i.e. from 9.25°-10.25°LS, 115°-116.25°SE to 8.8°-10.7°LS, 114.5°-116°SE, and surface chlorophyll concentration near coastal area, i.e. area 8.8°-9.25° LS, 114.5°-115°SE, increased. The preliminary result of this study concludes that the internal waves presumably affect chlorophyll distribution to westward (from 9.25°-10.25°LS, 115°-116.25°SE to 8.8°-10.7°LS, 114.5°-116°SE) in the south coast off Bali Island and increase surface chlorophyll concentration near coastal area (8.8°-9.25° LS, 114.5°-115°SE).

SAR Speckle Noise Reduction Using Wiener Filter

NASA Technical Reports Server (NTRS)

Joo, T. H.; Held, D. N.

1983-01-01

Synthetic aperture radar (SAR) images are degraded by speckle. A multiplicative speckle noise model for SAR images is presented. Using this model, a Wiener filter is derived by minimizing the mean-squared error using the known speckle statistics. Implementation of the Wiener filter is discussed and experimental results are presented. Finally, possible improvements to this method are explored.

Temporal Data Fusion Approaches to Remote Sensing-Based Wetland Classification

NASA Astrophysics Data System (ADS)

Montgomery, Joshua S. M.

This thesis investigates the ecology of wetlands and associated classification in prairie and boreal environments of Alberta, Canada, using remote sensing technology to enhance classification of wetlands in the province. Objectives of the thesis are divided into two case studies, 1) examining how satellite borne Synthetic Aperture Radar (SAR), optical (RapidEye & SPOT) can be used to evaluate surface water trends in a prairie pothole environment (Shepard Slough); and 2) investigating a data fusion methodology combining SAR, optical and Lidar data to characterize wetland vegetation and surface water attributes in a boreal environment (Utikuma Regional Study Area (URSA)). Surface water extent and hydroperiod products were derived from SAR data, and validated using optical imagery with high accuracies (76-97% overall) for both case studies. High resolution Lidar Digital Elevation Models (DEM), Digital Surface Models (DSM), and Canopy Height Model (CHM) products provided the means for data fusion to extract riparian vegetation communities and surface water; producing model accuracies of (R2 0.90) for URSA, and RMSE of 0.2m to 0.7m at Shepard Slough when compared to field and optical validation data. Integration of Alberta and Canadian wetland classifications systems used to classify and determine economic value of wetlands into the methodology produced thematic maps relevant for policy and decision makers for potential wetland monitoring and policy development.

SAR imaging and hydrodynamic analysis of ocean bottom topographic waves

NASA Astrophysics Data System (ADS)

Zheng, Quanan; Li, Li; Guo, Xiaogang; Ge, Yong; Zhu, Dayong; Li, Chunyan

2006-09-01

The satellite synthetic aperture radar (SAR) images display wave-like patterns of the ocean bottom topographic features at the south outlet of Taiwan Strait (TS). Field measurements indicate that the most TS water body is vertically stratified. However, SAR imaging models available were developed for homogeneous waters. Hence explaining SAR imaging mechanisms of bottom features in a stratified ocean is beyond the scope of those models. In order to explore these mechanisms and to determine the quantitative relations between the SAR imagery and the bottom features, a two-dimensional, three-layer ocean model with sinusoidal bottom topographic features is developed. Analytical solutions and inferences of the momentum equations of the ocean model lead to the following conditions. (1) In the lower layer, the topography-induced waves (topographic waves hereafter) exist in the form of stationary waves, which satisfy a lower boundary resonance condition σ = kC0, here σ is an angular frequency of the stationary waves, k is a wavenumber of bottom topographic corrugation, and C0 is a background current speed. (2) As internal waves, the topographic waves may propagate vertically to the upper layer with an unchanged wavenumber k, if a frequency relation N3 < σ < N2 is satisfied, here N2 and N3 are the Brunt-Wäisälä frequencies of middle layer and upper layer, respectively. (3) The topographic waves are extremely amplified if an upper layer resonance condition is satisfied. The SAR image of topographic waves is derived on the basis of current-modulated small wave spectra. The results indicate that the topographic waves on SAR images have the same wavelength of bottom topographic corrugation, and the imagery brightness peaks are either inphase or antiphase with respect to the topographic corrugation, depending on a sign of a coupling factor. These theoretical predictions are verified by field observations. The results of this study provide a physical basis for quantitative interpretation of SAR images of bottom topographic waves in the stratified ocean.

Layover and shadow detection based on distributed spaceborne single-baseline InSAR

NASA Astrophysics Data System (ADS)

Huanxin, Zou; Bin, Cai; Changzhou, Fan; Yun, Ren

2014-03-01

Distributed spaceborne single-baseline InSAR is an effective technique to get high quality Digital Elevation Model. Layover and Shadow are ubiquitous phenomenon in SAR images because of geometric relation of SAR imaging. In the signal processing of single-baseline InSAR, the phase singularity of Layover and Shadow leads to the phase difficult to filtering and unwrapping. This paper analyzed the geometric and signal model of the Layover and Shadow fields. Based on the interferometric signal autocorrelation matrix, the paper proposed the signal number estimation method based on information theoretic criteria, to distinguish Layover and Shadow from normal InSAR fields. The effectiveness and practicability of the method proposed in the paper are validated in the simulation experiments and theoretical analysis.

Monitoring of precursor landslide surface deformation using InSAR image in Kuchi-Sakamoto, Shizuoka Prefecture, Japan

NASA Astrophysics Data System (ADS)

Sato, H. P.; Nakajima, H.; Nakano, T.; Daimaru, H.

2014-12-01

Synthetic Aperture Radar (SAR) is the technique to obtain ground surface images using microwave that is emitted from and received on the antenna. The Kuchi-Sakamoto area, 2.2 km2 in precipitous mountains, central Japan, has suffered from frequent landslides, and slow landslide surface deformation has been monitored by on-site extensometer; however, such the monitoring method cannot detect the deformation in the whole area. Because satellite InSAR is effective tool to monitor slow landslide suface deformation, it is a promising tool for detecting precursor deformation and preparing effective measures against serious landslide disasters. In this study Advanced Land Observing Satellite (ALOS) / Phased Array type L-band SAR (PALSAR) data were used, and InSAR images were produced from the PALSAR data that were observed between 5 Sep 2008 and 21 Oct 2008 (from descending orbit) and between 20 Jul 2008 and 7 Sep 2009 (from ascending orbit). InSAR image from descending orbit was found to detect clear precursor landslide surface deformation on a slope; however, InSAR image on ascending orbit did not always detect clear precursor deformation. It is thought to be related with atmospheric moisture condition, length of observation baseline and so on. Furthermore, after phase unwrapping on InSAR images, 2.5-dimensional deformation was analized. This analysis needed both ascending and descending InSAR images and culculated quasi east-west deformation component (Figs. (a) and (b)) and quasi up-down deformation component (Figs. (c) and (d)). The resulting 2.5D calculation gave westward deformation and mixture of upward and downward deformations on the precursor landslide surface deformation slope (blue circles in Figs. (c) and (d)), where remarkable disrupted deep landslide occurred during Nov 2012 and 25 Jun 2013, judging from result of airborne LiDAR survey and field survey; the occurrence date is not precisely identified. The figure remains the issue that eliminating "real" precursor deformation from other candidate deformations. Preparation of this paper was supported by part of Individual Research Fund in College of Humanities and Sciences, Nihon University and part of Grants-in-Aid for Scientific Research, Challenging Exploratory (#25560185, Principal Investigator: Dr. Hiromu Daimaru).

Efficient thermal noise removal of Sentinel-1 image and its impacts on sea ice applications

NASA Astrophysics Data System (ADS)

Park, Jeong-Won; Korosov, Anton; Babiker, Mohamed

2017-04-01

Wide swath SAR observation from several spaceborne SAR missions played an important role in studying sea ice in the polar region. Sentinel 1A and 1B are producing dual-polarization observation data with the highest temporal resolution ever. For a proper use of dense time-series, radiometric properties must be qualified. Thermal noise is often neglected in many sea ice applications, but is impacting seriously the utility of dual-polarization SAR data. Sentinel-1 TOPSAR image intensity is disturbed by additive thermal noise particularly in cross-polarization channel. Although ESA provides calibrated noise vectors for noise power subtraction, residual noise contribution is significant considering relatively narrow backscattering distribution of cross-polarization channel. In this study, we investigate the noise characteristics and propose an efficient method for noise reduction based on three types of correction: azimuth de-scalloping, noise scaling, and inter-swath power balancing. The core idea is to find optimum correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over homogeneous region and to combine them with scalloping gain for reconstruction of complete two-dimensional noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The resulting correction coefficients determined by extensive experiments showed different noise characteristics for different Instrument Processing Facility (IPF) versions of Level 1 product generation. Even after thermal noise subtraction, the image still suffers from residual noise, which distorts local statistics. Since this residual noise depends on local signal-to-noise ratio, it can be compensated by variance normalization with coefficients determined from an empirical model. Denoising improved not only visual interpretability but also performances in SAR intensity-based sea ice applications. Results from two applications showed the effectiveness of the proposed method: feature tracking based sea ice drift and texture analysis based sea ice classification. For feature tracking, large spatial asymmetry of keypoint distribution caused by higher noise level in the nearest subswath was decresed so that the matched features to be selected evenly in space. For texture analysis, inter-subswath texture differences caused by different noise equivalent sigma zero were normalized so that the texture features estimated in any subswath have similar value with those in other subswaths.

Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

PubMed Central

Huang, Yulin; Pei, Jifang; Zhang, Qian; Gu, Qin; Yang, Jianyu

2018-01-01

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods. PMID:29652863

Improved GO/PO method and its application to wideband SAR image of conducting objects over rough surface

NASA Astrophysics Data System (ADS)

Jiang, Wang-Qiang; Zhang, Min; Nie, Ding; Jiao, Yong-Chang

2018-04-01

To simulate the multiple scattering effect of target in synthetic aperture radar (SAR) image, the hybrid method GO/PO method, which combines the geometrical optics (GO) and physical optics (PO), is employed to simulate the scattering field of target. For ray tracing is time-consuming, the Open Graphics Library (OpenGL) is usually employed to accelerate the process of ray tracing. Furthermore, the GO/PO method is improved for the simulation in low pixel situation. For the improved GO/PO method, the pixels are arranged corresponding to the rectangular wave beams one by one, and the GO/PO result is the sum of the contribution values of all the rectangular wave beams. To get high-resolution SAR image, the wideband echo signal is simulated which includes information of many electromagnetic (EM) waves with different frequencies. Finally, the improved GO/PO method is used to simulate the SAR image of targets above rough surface. And the effects of reflected rays and the size of pixel matrix on the SAR image are also discussed.

Are We Underestimating the Significance of Pedicle Screw Misplacement?

PubMed

Sarwahi, Vishal; Wendolowski, Stephen F; Gecelter, Rachel C; Amaral, Terry; Lo, Yungtai; Wollowick, Adam L; Thornhill, Beverly

2016-05-01

A retrospective review of charts, x-rays (XRs) and computed tomography (CT) scans was performed. To evaluate the accuracy of pedicle screw placement using a novel classification system to determine potentially significant screw misplacement. The accuracy rate of pedicle screw (PS) placement varies from 85% to 95% in the literature. This demonstrates technical ability but does not represent the impact of screw misplacement on individual patients. This study quantifies the rate of screw misplacement on a per-patient basis to highlight its effect on potential morbidity. A retrospective review of charts, XRs and low-dose CT scans of 127 patients who underwent spinal fusion with pedicle screws for spinal deformity was performed. Screws were divided into four categories: screws at risk (SAR), indeterminate misplacements (IMP), benign misplacements (BMP), accurately placed (AP). A total of 2724 screws were placed in 127 patients. A total of 2396 screws were placed accurately (87.96%). A total of 247 screws (9.07%) were BMP, 52 (1.91%) were IMP, and 29 (1.06%) were considered SAR. Per-patient analysis showed 23 (18.11%) of patients had all screws AP. Thirty-five (27.56%) had IMP and 18 (14.17%) had SAR. Risk factor analysis showed smaller Cobb angles increased likelihood of all screws being AP. Sub-analysis of adolescent idiopathic scoliotic patients showed no curve or patient characteristic that correlated with IMP or SAR. Over 40% of patients had screws with either some/major concern. Overall reported screw misplacement is low, but it does not reflect the potential impact on patient morbidity. Per-patient analysis reveals more concerning numbers toward screw misplacement. With increasing pedicle screw usage, the number of patients with misplaced screws will likely increase proportionally. Better strategies need to be devised for evaluation of screw placement, including establishment of a national database of deformity surgery, use of intra-operative image guidance, and reevaluation of postoperative low-dose CT imaging. 3.

Mathematical modeling and SAR simulation multifunction SAR technology efforts

NASA Technical Reports Server (NTRS)

Griffin, C. R.; Estes, J. M.

1981-01-01

The orbital SAR (synthetic aperture radar) simulation data was used in several simulation efforts directed toward advanced SAR development. Efforts toward simulating an operational radar, simulation of antenna polarization effects, and simulation of SAR images at serveral different wavelengths are discussed. Avenues for improvements in the orbital SAR simulation and its application to the development of advanced digital radar data processing schemes are indicated.

Digital SAR processing using a fast polynomial transform

NASA Technical Reports Server (NTRS)

Butman, S.; Lipes, R.; Rubin, A.; Truong, T. K.

1981-01-01

A new digital processing algorithm based on the fast polynomial transform is developed for producing images from Synthetic Aperture Radar data. This algorithm enables the computation of the two dimensional cyclic correlation of the raw echo data with the impulse response of a point target, thereby reducing distortions inherent in one dimensional transforms. This SAR processing technique was evaluated on a general-purpose computer and an actual Seasat SAR image was produced. However, regular production runs will require a dedicated facility. It is expected that such a new SAR processing algorithm could provide the basis for a real-time SAR correlator implementation in the Deep Space Network.

SAR/LANDSAT image registration study

NASA Technical Reports Server (NTRS)

Murphrey, S. W. (Principal Investigator)

1978-01-01

The author has identified the following significant results. Temporal registration of synthetic aperture radar data with LANDSAT-MSS data is both feasible (from a technical standpoint) and useful (from an information-content viewpoint). The greatest difficulty in registering aircraft SAR data to corrected LANDSAT-MSS data is control-point location. The differences in SAR and MSS data impact the selection of features that will serve as a good control points. The SAR and MSS data are unsuitable for automatic computer correlation of digital control-point data. The gray-level data can not be compared by the computer because of the different response characteristics of the MSS and SAR images.

Differential Shift Estimation in the Absence of Coherence: Performance Analysis and Benefits of Polarimetry

NASA Astrophysics Data System (ADS)

Villano, Michelangelo; Papathanassiou, Konstantinos P.

2011-03-01

The estimation of the local differential shift between synthetic aperture radar (SAR) images has proven to be an effective technique for monitoring glacier surface motion. As images acquired over glaciers by short wavelength SAR systems, such as TerraSAR-X, often suffer from a lack of coherence, image features have to be exploited for the shift estimation (feature-tracking).The present paper addresses feature-tracking with special attention to the feasibility requirements and the achievable accuracy of the shift estimation. In particular, the dependence of the performance on image characteristics, such as texture parameters, signal-to-noise ratio (SNR) and resolution, as well as on processing techniques (despeckling, normalised cross-correlation versus maximum likelihood estimation) is analysed by means of Monte-Carlo simulations. TerraSAR-X data acquired over the Helheim glacier, Greenland, and the Aletsch glacier, Switzerland, have been processed to validate the simulation results.Feature-tracking can benefit of the availability of fully-polarimetric data. As some image characteristics, in fact, are polarisation-dependent, the selection of an optimum polarisation leads to improved performance. Furthermore, fully-polarimetric SAR images can be despeckled without degrading the resolution, so that additional (smaller-scale) features can be exploited.

Visualizing characteristics of ocean data collected during the Shuttle Imaging Radar-B experiment

NASA Technical Reports Server (NTRS)

Tilley, David G.

1991-01-01

Topographic measurements of sea surface elevation collected by the Surface Contour Radar (SCR) during NASA's Shuttle Imaging Radar (SIR-B) experiment are plotted as three dimensional surface plots to observe wave height variance along the track of a P-3 aircraft. Ocean wave spectra were computed from rotating altimeter measurements acquired by the Radar Ocean Wave Spectrometer (ROWS). Fourier power spectra computed from SIR-B synthetic aperture radar (SAR) images of the ocean are compared to ROWS surface wave spectra. Fourier inversion of SAR spectra, after subtraction of spectral noise and modeling of wave height modulation, yields topography similar to direct measurements made by SCR. Visual perspectives on the SCR and SAR ocean data are compared. Threshold distinctions between surface elevation and texture modulations of SAR data are considered within the context of a dynamic statistical model of rough surface scattering. The result of these endeavors is insight as to the physical mechanism governing the imaging of ocean waves with SAR.

Acousto-optic time- and space-integrating spotlight-mode SAR processor

NASA Astrophysics Data System (ADS)

Haney, Michael W.; Levy, James J.; Michael, Robert R., Jr.

1993-09-01

The technical approach and recent experimental results for the acousto-optic time- and space- integrating real-time SAR image formation processor program are reported. The concept overcomes the size and power consumption limitations of electronic approaches by using compact, rugged, and low-power analog optical signal processing techniques for the most computationally taxing portions of the SAR imaging problem. Flexibility and performance are maintained by the use of digital electronics for the critical low-complexity filter generation and output image processing functions. The results include a demonstration of the processor's ability to perform high-resolution spotlight-mode SAR imaging by simultaneously compensating for range migration and range/azimuth coupling in the analog optical domain, thereby avoiding a highly power-consuming digital interpolation or reformatting operation usually required in all-electronic approaches.

GF-3 SAR Image Despeckling Based on the Improved Non-Local Means Using Non-Subsampled Shearlet Transform

NASA Astrophysics Data System (ADS)

Shi, R.; Sun, Z.

2018-04-01

GF-3 synthetic aperture radar (SAR) images are rich in information and have obvious sparse features. However, the speckle appears in the GF-3 SAR images due to the coherent imaging system and it hinders the interpretation of images seriously. Recently, Shearlet is applied to the image processing with its best sparse representation. A new Shearlet-transform-based method is proposed in this paper based on the improved non-local means. Firstly, the logarithmic operation and the non-subsampled Shearlet transformation are applied to the GF-3 SAR image. Secondly, in order to solve the problems that the image details are smoothed overly and the weight distribution is affected by the speckle, a new non-local means is used for the transformed high frequency coefficient. Thirdly, the Shearlet reconstruction is carried out. Finally, the final filtered image is obtained by an exponential operation. Experimental results demonstrate that, compared with other despeckling methods, the proposed method can suppress the speckle effectively in homogeneous regions and has better capability of edge preserving.

Advances in optical information processing IV; Proceedings of the Meeting, Orlando, FL, Apr. 18-20, 1990

NASA Astrophysics Data System (ADS)

Pape, Dennis R.

1990-09-01

The present conference discusses topics in optical image processing, optical signal processing, acoustooptic spectrum analyzer systems and components, and optical computing. Attention is given to tradeoffs in nonlinearly recorded matched filters, miniature spatial light modulators, detection and classification using higher-order statistics of optical matched filters, rapid traversal of an image data base using binary synthetic discriminant filters, wideband signal processing for emitter location, an acoustooptic processor for autonomous SAR guidance, and sampling of Fresnel transforms. Also discussed are an acoustooptic RF signal-acquisition system, scanning acoustooptic spectrum analyzers, the effects of aberrations on acoustooptic systems, fast optical digital arithmetic processors, information utilization in analog and digital processing, optical processors for smart structures, and a self-organizing neural network for unsupervised learning.

Fisheries imaging radar surveillance test /FIRST/ - Bering Sea test

NASA Technical Reports Server (NTRS)

Woods, E. G.; Ivey, J. H.

1977-01-01

A joint NOAA, U.S. Coast Guard and NASA program is being conducted to determine if a synthetic aperture radar (SAR) system, such as planned for NASA's SEASAT, can be useful in monitoring fishing vessels within the newly established 200-mile fishing limit. As part of this program, data gathering field operations were conducted over concentrations of foreign fishing vessels in the Bering Sea off Alaska in April 1976. The Jet Propulsion Laboratory developed synthetic aperture L-band radar which was flown aboard the NASA Convair 990 aircraft, with a Coast Guard cutter and C-130 aircraft simultaneously gathering data to provide both radar imagery and sea truth information on the vessels being imaged. Results indicate that synthetic aperture radar systems have potential for all weather detection, enumeration and classification of fishing vessels.

FIREX mission requirements document for nonrenewable resources

NASA Technical Reports Server (NTRS)

Dixon, T.; Carsey, F.

1982-01-01

The proposed mission requirements and a proposed experimental program for satellite synthetic aperture radar (SAR) system named FIREX (Free-Flying Imaging Radar Experiment) for nonrenewable resources is described. The recommended spacecraft minimum SAR system is a C-band imager operating in four modes: (1) low look angle HH-polarized; (2) intermediate look angle, HH-polarized; (3) intermediate look angle, IIV-polarized; and (4) high look angle HH-polarized. This SAR system is complementary to other future spaceborne imagers such as the Thematic Mapper on LANDSAT-D. A near term aircraft SAR based research program is outlined which addresses specific mission design issues such as preferred incidence angles or polarizations for geologic targets of interest.

A comparative study on methods of improving SCR for ship detection in SAR image

NASA Astrophysics Data System (ADS)

Lang, Haitao; Shi, Hongji; Tao, Yunhong; Ma, Li

2017-10-01

Knowledge about ship positions plays a critical role in a wide range of maritime applications. To improve the performance of ship detector in SAR image, an effective strategy is improving the signal-to-clutter ratio (SCR) before conducting detection. In this paper, we present a comparative study on methods of improving SCR, including power-law scaling (PLS), max-mean and max-median filter (MMF1 and MMF2), method of wavelet transform (TWT), traditional SPAN detector, reflection symmetric metric (RSM), scattering mechanism metric (SMM). The ability of SCR improvement to SAR image and ship detection performance associated with cell- averaging CFAR (CA-CFAR) of different methods are evaluated on two real SAR data.

Development of Wind Speed Retrieval from Cross-Polarization Chinese Gaofen-3 Synthetic Aperture Radar in Typhoons

PubMed Central

Yuan, Xinzhe; Sun, Jian; Zhou, Wei; Zhang, Qingjun

2018-01-01

The purpose of our work is to determine the feasibility and effectiveness of retrieving sea surface wind speeds from C-band cross-polarization (herein vertical-horizontal, VH) Chinese Gaofen-3 (GF-3) SAR images in typhoons. In this study, we have collected three GF-3 SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode during the summer of 2017 from the China Sea, which includes the typhoons Noru, Doksuri and Talim. These images were collocated with wind simulations at 0.12° grids from a numeric model, called the Regional Assimilation and Prediction System-Typhoon model (GRAPES-TYM). Recent research shows that GRAPES-TYM has a good performance for typhoon simulation in the China Sea. Based on the dataset, the dependence of wind speed and of radar incidence angle on normalized radar cross (NRCS) of VH-polarization GF-3 SAR have been investigated, after which an empirical algorithm for wind speed retrieval from VH-polarization GF-3 SAR was tuned. An additional four VH-polarization GF-3 SAR images in three typhoons, Noru, Hato and Talim, were investigated in order to validate the proposed algorithm. SAR-derived winds were compared with measurements from Windsat winds at 0.25° grids with wind speeds up to 40 m/s, showing a 5.5 m/s root mean square error (RMSE) of wind speed and an improved RMSE of 5.1 m/s wind speed was achieved compared with the retrieval results validated against GRAPES-TYM winds. It is concluded that the proposed algorithm is a promising potential technique for strong wind retrieval from cross-polarization GF-3 SAR images without encountering a signal saturation problem. PMID:29385068

Calibrating a hydraulic model using water levels derived from time series high-resolution Radarsat-2 synthetic aperture radar images and elevation data

NASA Astrophysics Data System (ADS)

Trudel, M.; Desrochers, N.; Leconte, R.

2017-12-01

Knowledge of water extent (WE) and level (WL) of rivers is necessary to calibrate and validate hydraulic models and thus to better simulate and forecast floods. Synthetic aperture radar (SAR) has demonstrated its potential for delineating water bodies, as backscattering of water is much lower than that of other natural surfaces. The ability of SAR to obtain information despite cloud cover makes it an interesting tool for temporal monitoring of water bodies. The delineation of WE combined with a high-resolution digital terrain model (DTM) allows extracting WL. However, most research using SAR data to calibrate hydraulic models has been carried out using one or two images. The objectives of this study is to use WL derived from time series high resolution Radarsat-2 SAR images for the calibration of a 1-D hydraulic model (HEC-RAS). Twenty high-resolution (5 m) Radarsat-2 images were acquired over a 40 km reach of the Athabasca River, in northern Alberta, Canada, between 2012 and 2016, covering both low and high flow regimes. A high-resolution (2m) DTM was generated combining information from LIDAR data and bathymetry acquired between 2008 and 2016 by boat surveying. The HEC-RAS model was implemented on the Athabasca River to simulate WL using cross-sections spaced by 100 m. An image histogram thresholding method was applied on each Radarsat-2 image to derive WE. WE were then compared against each cross-section to identify those were the slope of the banks is not too abrupt and therefore amenable to extract WL. 139 observations of WL at different locations along the river reach and with streamflow measurements were used to calibrate the HEC-RAS model. The RMSE between SAR-derived and simulated WL is under 0.35 m. Validation was performed using in situ observations of WL measured in 2008, 2012 and 2016. The RMSE between the simulated water levels calibrated with SAR images and in situ observations is less than 0.20 m. In addition, a critical success index (CSI) was performed to compare the WE simulated by HEC-RAS and that derived from SARs images. The CSI is higher than 0.85 for each date, which means that simulated WE is highly similar to the WE derived from SARs images. Thereby, the results of our analysis indicate that calibration of a hydraulic model can be performed from WL derived from time series of high-resolution SAR images.

Exploitation of Digital Surface Models Generated from WORLDVIEW-2 Data for SAR Simulation Techniques

NASA Astrophysics Data System (ADS)

Ilehag, R.; Auer, S.; d'Angelo, P.

2017-05-01

GeoRaySAR, an automated SAR simulator developed at DLR, identifies buildings in high resolution SAR data by utilizing geometric knowledge extracted from digital surface models (DSMs). Hitherto, the simulator has utilized DSMs generated from LiDAR data from airborne sensors with pre-filtered vegetation. Discarding the need for pre-optimized model input, DSMs generated from high resolution optical data (acquired with WorldView-2) are used for the extraction of building-related SAR image parts in this work. An automatic preprocessing of the DSMs has been developed for separating buildings from elevated vegetation (trees, bushes) and reducing the noise level. Based on that, automated simulations are triggered considering the properties of real SAR images. Locations in three cities, Munich, London and Istanbul, were chosen as study areas to determine advantages and limitations related to WorldView-2 DSMs as input for GeoRaySAR. Beyond, the impact of the quality of the DSM in terms of building extraction is evaluated as well as evaluation of building DSM, a DSM only containing buildings. The results indicate that building extents can be detected with DSMs from optical satellite data with various success, dependent on the quality of the DSM as well as on the SAR imaging perspective.

Change detection in a time series of polarimetric SAR data by an omnibus test statistic and its factorization (Conference Presentation)

NASA Astrophysics Data System (ADS)

Nielsen, Allan A.; Conradsen, Knut; Skriver, Henning

2016-10-01

Test statistics for comparison of real (as opposed to complex) variance-covariance matrices exist in the statistics literature [1]. In earlier publications we have described a test statistic for the equality of two variance-covariance matrices following the complex Wishart distribution with an associated p-value [2]. We showed their application to bitemporal change detection and to edge detection [3] in multilook, polarimetric synthetic aperture radar (SAR) data in the covariance matrix representation [4]. The test statistic and the associated p-value is described in [5] also. In [6] we focussed on the block-diagonal case, we elaborated on some computer implementation issues, and we gave examples on the application to change detection in both full and dual polarization bitemporal, bifrequency, multilook SAR data. In [7] we described an omnibus test statistic Q for the equality of k variance-covariance matrices following the complex Wishart distribution. We also described a factorization of Q = R2 R3 … Rk where Q and Rj determine if and when a difference occurs. Additionally, we gave p-values for Q and Rj. Finally, we demonstrated the use of Q and Rj and the p-values to change detection in truly multitemporal, full polarization SAR data. Here we illustrate the methods by means of airborne L-band SAR data (EMISAR) [8,9]. The methods may be applied to other polarimetric SAR data also such as data from Sentinel-1, COSMO-SkyMed, TerraSAR-X, ALOS, and RadarSat-2 and also to single-pol data. The account given here closely follows that given our recent IEEE TGRS paper [7]. Selected References [1] Anderson, T. W., An Introduction to Multivariate Statistical Analysis, John Wiley, New York, third ed. (2003). [2] Conradsen, K., Nielsen, A. A., Schou, J., and Skriver, H., "A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing 41(1): 4-19, 2003. [3] Schou, J., Skriver, H., Nielsen, A. A., and Conradsen, K., "CFAR edge detector for polarimetric SAR images," IEEE Transactions on Geoscience and Remote Sensing 41(1): 20-32, 2003. [4] van Zyl, J. J. and Ulaby, F. T., "Scattering matrix representation for simple targets," in Radar Polarimetry for Geoscience Applications, Ulaby, F. T. and Elachi, C., eds., Artech, Norwood, MA (1990). [5] Canty, M. J., Image Analysis, Classification and Change Detection in Remote Sensing,with Algorithms for ENVI/IDL and Python, Taylor & Francis, CRC Press, third revised ed. (2014). [6] Nielsen, A. A., Conradsen, K., and Skriver, H., "Change detection in full and dual polarization, single- and multi-frequency SAR data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(8): 4041-4048, 2015. [7] Conradsen, K., Nielsen, A. A., and Skriver, H., "Determining the points of change in time series of polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing 54(5), 3007-3024, 2016. [9] Christensen, E. L., Skou, N., Dall, J., Woelders, K., rgensen, J. H. J., Granholm, J., and Madsen, S. N., "EMISAR: An absolutely calibrated polarimetric L- and C-band SAR," IEEE Transactions on Geoscience and Remote Sensing 36: 1852-1865 (1998).

Investigating the use of the dual-polarized and large incident angle of SAR data for mapping the fluvial and aeolian deposits

NASA Astrophysics Data System (ADS)

Gaber, Ahmed; Amarah, Bassam A.; Abdelfattah, Mohamed; Ali, Sarah

2017-12-01

Mapping the spatial distributions of the fluvial deposits in terms of particles size as well as imaging the near-surface features along the non-vegetated aeolian sand-sheets, provides valuable geological information. Thus this work aims at investigating the contribution of the dual-polarization SAR data in classifying and mapping the surface sediments as well as investigating the effect of the radar incident-angle on improving the images of the hidden features under the desert sand cover. For mapping the fluvial deposits, the covariance matrix ([C2]) using four dual-polarized ALOS/PALSAR-1 scenes cover the Wadi El Matulla, East Qena, Egypt were generated. This [C2] matrix was used to generate a supervised classification map with three main classes (gravel, gravel/sand and sand). The polarimetric scattering response, spectral reflectance and temperatures brightness of these 3 classes were extracted. However for the aeolian deposits investigation, two Radarsat-1 and three full-polarimetric ALOS/PALSAR-1 images, which cover the northwestern sandy part of Sinai, Egypt were calibrated, filtered, geocoded and ingested in a GIS database to image the near-surface features. The fluvial mapping results show that the values of the radar backscattered coefficient (σ°) and the degree of randomness of the obtained three classes are increasing respectively by increasing their grain size. Moreover, the large incident angle (θi = 39.7) of the Radarsat-1 image has revealed a meandering buried stream under the sand sheet of the northwestern part of Sinai. Such buried stream does not appear in the other optical, SRTM and SAR dataset. The main reason is the enhanced contrast between the low backscattered return from the revealed meandering stream and the surroundings as a result of the increased backscattering intensity, which is related to the relatively large incident angle along the undulated surface of the study area. All archaeological observations support the existence of paleo-fresh water lagoon at the northwestern corner of the study area, which might have been the discharge lagoon of the revealed hidden stream.

A novel ship CFAR detection algorithm based on adaptive parameter enhancement and wake-aided detection in SAR images

NASA Astrophysics Data System (ADS)

Meng, Siqi; Ren, Kan; Lu, Dongming; Gu, Guohua; Chen, Qian; Lu, Guojun

2018-03-01

Synthetic aperture radar (SAR) is an indispensable and useful method for marine monitoring. With the increase of SAR sensors, high resolution images can be acquired and contain more target structure information, such as more spatial details etc. This paper presents a novel adaptive parameter transform (APT) domain constant false alarm rate (CFAR) to highlight targets. The whole method is based on the APT domain value. Firstly, the image is mapped to the new transform domain by the algorithm. Secondly, the false candidate target pixels are screened out by the CFAR detector to highlight the target ships. Thirdly, the ship pixels are replaced by the homogeneous sea pixels. And then, the enhanced image is processed by Niblack algorithm to obtain the wake binary image. Finally, normalized Hough transform (NHT) is used to detect wakes in the binary image, as a verification of the presence of the ships. Experiments on real SAR images validate that the proposed transform does enhance the target structure and improve the contrast of the image. The algorithm has a good performance in the ship and ship wake detection.

Process for combining multiple passes of interferometric SAR data

DOEpatents

Bickel, Douglas L.; Yocky, David A.; Hensley, Jr., William H.

2000-11-21

Interferometric synthetic aperture radar (IFSAR) is a promising technology for a wide variety of military and civilian elevation modeling requirements. IFSAR extends traditional two dimensional SAR processing to three dimensions by utilizing the phase difference between two SAR images taken from different elevation positions to determine an angle of arrival for each pixel in the scene. This angle, together with the two-dimensional location information in the traditional SAR image, can be transformed into geographic coordinates if the position and motion parameters of the antennas are known accurately.

Polarimetric SAR calibration experiment using active radar calibrators

NASA Astrophysics Data System (ADS)

Freeman, Anthony; Shen, Yuhsyen; Werner, Charles L.

1990-03-01

Active radar calibrators are used to derive both the amplitude and phase characteristics of a multichannel polarimetric SAR from the complex image data. Results are presented from an experiment carried out using the NASA/JPL DC-8 aircraft SAR over a calibration site at Goldstone, California. As part of the experiment, polarimetric active radar calibrators (PARCs) with adjustable polarization signatures were deployed. Experimental results demonstrate that the PARCs can be used to calibrate polarimetric SAR images successfully. Restrictions on the application of the PARC calibration procedure are discussed.

Polarimetric SAR calibration experiment using active radar calibrators

NASA Technical Reports Server (NTRS)

Freeman, Anthony; Shen, Yuhsyen; Werner, Charles L.

1990-01-01

Active radar calibrators are used to derive both the amplitude and phase characteristics of a multichannel polarimetric SAR from the complex image data. Results are presented from an experiment carried out using the NASA/JPL DC-8 aircraft SAR over a calibration site at Goldstone, California. As part of the experiment, polarimetric active radar calibrators (PARCs) with adjustable polarization signatures were deployed. Experimental results demonstrate that the PARCs can be used to calibrate polarimetric SAR images successfully. Restrictions on the application of the PARC calibration procedure are discussed.

Segmentation Of Polarimetric SAR Data

NASA Technical Reports Server (NTRS)

Rignot, Eric J. M.; Chellappa, Rama

1994-01-01

Report presents one in continuing series of studies of segmentation of polarimetric synthetic-aperture-radar, SAR, image data into regions. Studies directed toward refinement of method of automated analysis of SAR data.

Playback system designed for X-Band SAR

NASA Astrophysics Data System (ADS)

Yuquan, Liu; Changyong, Dou

2014-03-01

SAR(Synthetic Aperture Radar) has extensive application because it is daylight and weather independent. In particular, X-Band SAR strip map, designed by Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, provides high ground resolution images, at the same time it has a large spatial coverage and a short acquisition time, so it is promising in multi-applications. When sudden disaster comes, the emergency situation acquires radar signal data and image as soon as possible, in order to take action to reduce loss and save lives in the first time. This paper summarizes a type of X-Band SAR playback processing system designed for disaster response and scientific needs. It describes SAR data workflow includes the payload data transmission and reception process. Playback processing system completes signal analysis on the original data, providing SAR level 0 products and quick image. Gigabit network promises radar signal transmission efficiency from recorder to calculation unit. Multi-thread parallel computing and ping pong operation can ensure computation speed. Through gigabit network, multi-thread parallel computing and ping pong operation, high speed data transmission and processing meet the SAR radar data playback real time requirement.

Global Boreal Forest Mapping with JERS-1: North America

NASA Technical Reports Server (NTRS)

Williams, Cynthia L.; McDonald, Kyle; Chapman, Bruce

2000-01-01

Collaborative effort is underway to map boreal forests worldwide using L-band, single polarization Synthetic Aperture Radar (SAR) imagery from the Japanese Earth Resources (JERS-1) satellite. Final products of the North American Boreal Forest Mapping Project will include two continental scale radar mosaics and supplementary multitemporal mosaics for Alaska, central Canada, and eastern Canada. For selected sites, we are also producing local scale (100 km x 100 km) and regional scale maps (1000 km x 1000 km). As with the nearly completed Amazon component of the Global Rain Forest Mapping project, SAR imagery, radar image mosaics and SAR-derived texture image products will be available to the scientific community on the World Wide Web. Image acquisition for this project has been completed and processing and image interpretation is underway at the Alaska SAR Facility.

Multistatic synthetic aperture radar image formation.

PubMed

Krishnan, V; Swoboda, J; Yarman, C E; Yazici, B

2010-05-01

In this paper, we consider a multistatic synthetic aperture radar (SAR) imaging scenario where a swarm of airborne antennas, some of which are transmitting, receiving or both, are traversing arbitrary flight trajectories and transmitting arbitrary waveforms without any form of multiplexing. The received signal at each receiving antenna may be interfered by the scattered signal due to multiple transmitters and additive thermal noise at the receiver. In this scenario, standard bistatic SAR image reconstruction algorithms result in artifacts in reconstructed images due to these interferences. In this paper, we use microlocal analysis in a statistical setting to develop a filtered-backprojection (FBP) type analytic image formation method that suppresses artifacts due to interference while preserving the location and orientation of edges of the scene in the reconstructed image. Our FBP-type algorithm exploits the second-order statistics of the target and noise to suppress the artifacts due to interference in a mean-square sense. We present numerical simulations to demonstrate the performance of our multistatic SAR image formation algorithm with the FBP-type bistatic SAR image reconstruction algorithm. While we mainly focus on radar applications, our image formation method is also applicable to other problems arising in fields such as acoustic, geophysical and medical imaging.

Analysis of Benefits and Pitfalls of Satellite SAR for Coastal Area Monitoring

NASA Astrophysics Data System (ADS)

Nunziata, F.; Buono, A.; Mgliaccio, M.; Li, X.; Wei, Y.

2016-08-01

This study aims at describing the outcomes of the Dragon-3 project no. 10689. The undertaken activities deal with coastal area monitoring and they include sea pollution and coastline extraction. The key remote sensing tool is the Synthetic Aperture Radar (SAR) that provides fine resolution images of the microwave reflectivity of the observed scene. However, the interpretation of SAR images is not at all straightforward and all the above-mentioned coastal area applications cannot be easily addressed using single-polarization SAR. Hence, the main outcome of this project is investigating the capability of multi-polarization SAR measurements to generate added-vale product in the frame of coastal area management.

Digital SAR processing using a fast polynomial transform

NASA Technical Reports Server (NTRS)

Truong, T. K.; Lipes, R. G.; Butman, S. A.; Reed, I. S.; Rubin, A. L.

1984-01-01

A new digital processing algorithm based on the fast polynomial transform is developed for producing images from Synthetic Aperture Radar data. This algorithm enables the computation of the two dimensional cyclic correlation of the raw echo data with the impulse response of a point target, thereby reducing distortions inherent in one dimensional transforms. This SAR processing technique was evaluated on a general-purpose computer and an actual Seasat SAR image was produced. However, regular production runs will require a dedicated facility. It is expected that such a new SAR processing algorithm could provide the basis for a real-time SAR correlator implementation in the Deep Space Network. Previously announced in STAR as N82-11295

Processor architecture for airborne SAR systems

NASA Technical Reports Server (NTRS)

Glass, C. M.

1983-01-01

Digital processors for spaceborne imaging radars and application of the technology developed for airborne SAR systems are considered. Transferring algorithms and implementation techniques from airborne to spaceborne SAR processors offers obvious advantages. The following topics are discussed: (1) a quantification of the differences in processing algorithms for airborne and spaceborne SARs; and (2) an overview of three processors for airborne SAR systems.

TerraSAR-X mission

NASA Astrophysics Data System (ADS)

Werninghaus, Rolf

2004-01-01

The TerraSAR-X is a German national SAR- satellite system for scientific and commercial applications. It is the continuation of the scientifically and technologically successful radar missions X-SAR (1994) and SRTM (2000) and will bring the national technology developments DESA and TOPAS into operational use. The space segment of TerraSAR-X is an advanced high-resolution X-Band radar satellite. The system design is based on a sound market analysis performed by Infoterra. The TerraSAR-X features an advanced high-resolution X-Band Synthetic Aperture Radar based on the active phased array technology which allows the operation in Spotlight-, Stripmap- and ScanSAR Mode with various polarizations. It combines the ability to acquire high resolution images for detailed analysis as well as wide swath images for overview applications. In addition, experimental modes like the Dual Receive Antenna Mode allow for full-polarimetric imaging as well as along track interferometry, i.e. moving target identification. The Ground Segment is optimized for flexible response to (scientific and commercial) User requests and fast image product turn-around times. The TerraSAR-X mission will serve two main goals. The first goal is to provide the strongly supportive scientific community with multi-mode X-Band SAR data. The broad spectrum of scientific application areas include Hydrology, Geology, Climatology, Oceanography, Environmental Monitoring and Disaster Monitoring as well as Cartography (DEM Generation) and Interferometry. The second goal is the establishment of a commercial EO-market in Europe which is driven by Infoterra. The commercial goal is the development of a sustainable EO-business so that the e.g. follow-on systems can be completely financed by industry from the profit. Due to its commercial potential, the TerraSAR-X project will be implemented based on a public-private partnership with the Astrium GmbH. This paper will describe first the mission objectives as well as the project organisation and major milestones. Then an overview on the satellite as well as the SAR instrument is given followed by a description of the system design. Finally the principle layout of the TerraSAR-X Ground Segment and some remarks on the European context are presented.

The Use of Satellite and Aircraft SAR to Detect and Chart Hazards to Navigation.

DTIC Science & Technology

1983-08-01

CLASSIFICATION OF THIS PAGE (W~ho D~Fiamwdj REPOT DCUMNTATON AGEREAD INSTRUCTIONSREPOT DCUMNTATON AGEBEFORE COMPLETING FORM I REPORT NUMBER 1 OVT ACCESSION AO. 3...Science Data Center is thanked for providing copies of selected SIR-A imagery. av L i _ _ _ .* . -_i [RIM RADAR DIVISION TABLE OF CONTENTS 1 ...INTRODUCTION ..................................................... 1 2. EXECUTIVE SUMMARY ................................................ 5 3. SURVEY OF SAR

Satellite Remote Sensing of Ocean Winds, Surface Waves and Surface Currents during the Hurricanes

NASA Astrophysics Data System (ADS)

Zhang, G.; Perrie, W. A.; Liu, G.; Zhang, L.

2017-12-01

Hurricanes over the ocean have been observed by spaceborne aperture radar (SAR) since the first SAR images were available in 1978. SAR has high spatial resolution (about 1 km), relatively large coverage and capability for observations during almost all-weather, day-and-night conditions. In this study, seven C-band RADARSAT-2 dual-polarized (VV and VH) ScanSAR wide images from the Canadian Space Agency (CSA) Hurricane Watch Program in 2017 are collected over five hurricanes: Harvey, Irma, Maria, Nate, and Ophelia. We retrieve the ocean winds by applying our C-band Cross-Polarization Coupled-Parameters Ocean (C-3PO) wind retrieval model [Zhang et al., 2017, IEEE TGRS] to the SAR images. Ocean waves are estimated by applying a relationship based on the fetch- and duration-limited nature of wave growth inside hurricanes [Hwang et al., 2016; 2017, J. Phys. Ocean.]. We estimate the ocean surface currents using the Doppler Shift extracted from VV-polarized SAR images [Kang et al., 2016, IEEE TGRS]. C-3PO model is based on theoretical analysis of ocean surface waves and SAR microwave backscatter. Based on the retrieved ocean winds, we estimate the hurricane center locations, maxima wind speeds, and radii of the five hurricanes by adopting the SHEW model (Symmetric Hurricane Estimates for Wind) by Zhang et al. [2017, IEEE TGRS]. Thus, we investigate possible relations between hurricane structures and intensities, and especially some possible effects of the asymmetrical characteristics on changes in the hurricane intensities, such as the eyewall replacement cycle. The three SAR images of Ophelia include the north coast of Ireland and east coast of Scotland allowing study of ocean surface currents respond to the hurricane. A system of methods capable of observing marine winds, surface waves, and surface currents from satellites is of value, even if these data are only available in near real-time or from SAR-related satellite images. Insight into high resolution ocean winds, waves and currents in hurricanes can be useful for intensity prediction, which has had relatively few improvements in the past 25 years. In 2018 RADARSAT Constellation Mission will be launched, increasing SAR coverage by 10×, allowing increased observations during the next hurricane season.

Sea ice type maps from Alaska synthetic aperture radar facility imagery: An assessment

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

Multiband radar characterization of forest biomes

NASA Technical Reports Server (NTRS)

Dobson, M. Craig; Ulaby, Fawwaz T.

1990-01-01

The utility of airborne and orbital SAR in classification, assessment, and monitoring of forest biomes is investigated through analysis of orbital synthetic aperature radar (SAR) and multifrequency and multipolarized airborne SAR imagery relying on image tone and texture. Preliminary airborne SAR experiments and truck-mounted scatterometer observations demonstrated that the three dimensional structural complexity of a forest, and the various scales of temporal dynamics in the microwave dielectric properties of both trees and the underlying substrate would severely limit empirical or semi-empirical approaches. As a consequence, it became necessary to develop a more profound understanding of the electromagnetic properties of a forest scene and their temporal dynamics through controlled experimentation coupled with theoretical development and verification. The concatenation of various models into a physically-based composite model treating the entire forest scene became the major objective of the study as this is the key to development of a series of robust retrieval algorithms for forest biophysical properties. In order to verify the performance of the component elements of the composite model, a series of controlled laboratory and field experiments were undertaken to: (1) develop techniques to measure the microwave dielectric properties of vegetation; (2) relate the microwave dielectric properties of vegetation to more readily measured characteristics such as density and moisture content; (3) calculate the radar cross-section of leaves, and cylinders; (4) improve backscatter models for rough surfaces; and (5) relate attenuation and phase delays during propagation through canopies to canopy properties. These modeling efforts, as validated by the measurements, were incorporated within a larger model known as the Michigan Microwave Canopy Scattering (MIMICS) Model.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

Spacecraft on-board SAR image generation for EOS-type missions

NASA Technical Reports Server (NTRS)

Liu, K. Y.; Arens, W. E.; Assal, H. M.; Vesecky, J. F.

1987-01-01

Spacecraft on-board synthetic aperture radar (SAR) image generation is an extremely difficult problem because of the requirements for high computational rates (usually on the order of Giga-operations per second), high reliability (some missions last up to 10 years), and low power dissipation and mass (typically less than 500 watts and 100 Kilograms). Recently, a JPL study was performed to assess the feasibility of on-board SAR image generation for EOS-type missions. This paper summarizes the results of that study. Specifically, it proposes a processor architecture using a VLSI time-domain parallel array for azimuth correlation. Using available space qualifiable technology to implement the proposed architecture, an on-board SAR processor having acceptable power and mass characteristics appears feasible for EOS-type applications.

Tie Points Extraction for SAR Images Based on Differential Constraints

NASA Astrophysics Data System (ADS)

Xiong, X.; Jin, G.; Xu, Q.; Zhang, H.

2018-04-01

Automatically extracting tie points (TPs) on large-size synthetic aperture radar (SAR) images is still challenging because the efficiency and correct ratio of the image matching need to be improved. This paper proposes an automatic TPs extraction method based on differential constraints for large-size SAR images obtained from approximately parallel tracks, between which the relative geometric distortions are small in azimuth direction and large in range direction. Image pyramids are built firstly, and then corresponding layers of pyramids are matched from the top to the bottom. In the process, the similarity is measured by the normalized cross correlation (NCC) algorithm, which is calculated from a rectangular window with the long side parallel to the azimuth direction. False matches are removed by the differential constrained random sample consensus (DC-RANSAC) algorithm, which appends strong constraints in azimuth direction and weak constraints in range direction. Matching points in the lower pyramid images are predicted with the local bilinear transformation model in range direction. Experiments performed on ENVISAT ASAR and Chinese airborne SAR images validated the efficiency, correct ratio and accuracy of the proposed method.

Three-dimensional brain MRI for DBS patients within ultra-low radiofrequency power limits.

PubMed

Sarkar, Subhendra N; Papavassiliou, Efstathios; Hackney, David B; Alsop, David C; Shih, Ludy C; Madhuranthakam, Ananth J; Busse, Reed F; La Ruche, Susan; Bhadelia, Rafeeque A

2014-04-01

For patients with deep brain stimulators (DBS), local absorbed radiofrequency (RF) power is unknown and is much higher than what the system estimates. We developed a comprehensive, high-quality brain magnetic resonance imaging (MRI) protocol for DBS patients utilizing three-dimensional (3D) magnetic resonance sequences at very low RF power. Six patients with DBS were imaged (10 sessions) using a transmit/receive head coil at 1.5 Tesla with modified 3D sequences within ultra-low specific absorption rate (SAR) limits (0.1 W/kg) using T2 , fast fluid-attenuated inversion recovery (FLAIR) and T1 -weighted image contrast. Tissue signal and tissue contrast from the low-SAR images were subjectively and objectively compared with routine clinical images of six age-matched controls. Low-SAR images of DBS patients demonstrated tissue contrast comparable to high-SAR images and were of diagnostic quality except for slightly reduced signal. Although preliminary, we demonstrated diagnostic quality brain MRI with optimized, volumetric sequences in DBS patients within very conservative RF safety guidelines offering a greater safety margin. © 2014 International Parkinson and Movement Disorder Society.

Extraction of lead and ridge characteristics from SAR images of sea ice

NASA Technical Reports Server (NTRS)

Vesecky, John F.; Smith, Martha P.; Samadani, Ramin

1990-01-01

Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.

Assessment of Cropping System Diversity in the Fergana Valley Through Image Fusion of Landsat 8 and SENTINEL-1

NASA Astrophysics Data System (ADS)

Dimov, D.; Kuhn, J.; Conrad, C.

2016-06-01

In the transitioning agricultural societies of the world, food security is an essential element of livelihood and economic development with the agricultural sector very often being the major employment factor and income source. Rapid population growth, urbanization, pollution, desertification, soil degradation and climate change pose a variety of threats to a sustainable agricultural development and can be expressed as agricultural vulnerability components. Diverse cropping patterns may help to adapt the agricultural systems to those hazards in terms of increasing the potential yield and resilience to water scarcity. Thus, the quantification of crop diversity using indices like the Simpson Index of Diversity (SID) e.g. through freely available remote sensing data becomes a very important issue. This however requires accurate land use classifications. In this study, the focus is set on the cropping system diversity of garden plots, summer crop fields and orchard plots which are the prevalent agricultural systems in the test area of the Fergana Valley in Uzbekistan. In order to improve the accuracy of land use classification algorithms with low or medium resolution data, a novel processing chain through the hitherto unique fusion of optical and SAR data from the Landsat 8 and Sentinel-1 platforms is proposed. The combination of both sensors is intended to enhance the object's textural and spectral signature rather than just to enhance the spatial context through pansharpening. It could be concluded that the Ehlers fusion algorithm gave the most suitable results. Based on the derived image fusion different object-based image classification algorithms such as SVM, Naïve Bayesian and Random Forest were evaluated whereby the latter one achieved the highest classification accuracy. Subsequently, the SID was applied to measure the diversification of the three main cropping systems.

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

NASA Technical Reports Server (NTRS)

Degrandi, G.; Lavalle, C.; Degroof, H.; Sieber, A.

1992-01-01

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted.

Ocean-ice interaction in the marginal ice zone using synthetic aperture radar imagery

NASA Technical Reports Server (NTRS)

Liu, Antony K.; Peng, Chich Y.; Weingartner, Thomas J.

1994-01-01

Ocean-ice interaction processes in the marginal ice zone (MIZ) by wind, waves, and mesoscale features, such as up/downwelling and eddies are studied using Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) images and an ocean-ice interaction model. A sequence of seven SAR images of the MIZ in the Chukchi Sea with 3 or 6 days interval are investigated for ice edge advance/retreat. Simultaneous current measurements from the northeast Chukchi Sea, as well as the Barrow wind record, are used to interpret the MIZ dynamics. SAR spectra of waves in ice and ocean waves in the Bering and Chukchi Sea are compared for the study of wave propagation and dominant SAR imaging mechanism. By using the SAR-observed ice edge configuration and wind and wave field in the Chukchi Sea as inputs, a numerical simulation has been performed with the ocean-ice interaction model. After 3 days of wind and wave forcing the resulting ice edge configuration, eddy formation, and flow velocity field are shown to be consistent with SAR observations.

Design and realization of an active SAR calibrator for TerraSAR-X

NASA Astrophysics Data System (ADS)

Dummer, Georg; Lenz, Rainer; Lutz, Benjamin; Kühl, Markus; Müller-Glaser, Klaus D.; Wiesbeck, Werner

2005-10-01

TerraSAR-X is a new earth observing satellite which will be launched in spring 2006. It carries a high resolution X-band SAR sensor. For high image data quality, accurate ground calibration targets are necessary. This paper describes a novel system concept for an active and highly integrated, digitally controlled SAR system calibrator. A total of 16 active transponder and receiver systems and 17 receiver only systems will be fabricated for a calibration campaign. The calibration units serve for absolute radiometric calibration of the SAR image data. Additionally, they are equipped with an extra receiver path for two dimensional satellite antenna pattern recognition. The calibrator is controlled by a dedicated digital Electronic Control Unit (ECU). The different voltages needed by the calibrator and the ECU are provided by the third main unit called Power Management Unit (PMU).

Acoustic Characterization of Soil

DTIC Science & Technology

1996-03-28

modified SAR imaging algorithm. Page 26 Final Report In the acoustic subsurface imaging scenario, the "object" to be imaged (i.e., cultural artifacts... subsurface imaging scenario. To combat this potential difficulty we can utilize a new SAR imaging algorithm (Lee et al., 1996) derived from a geophysics...essentially a transmit plane wave. This is a cost-effective means to evaluate the feasibility of subsurface imaging . A more complete (and costly

Methods of evaluating the effects of coding on SAR data

NASA Technical Reports Server (NTRS)

Dutkiewicz, Melanie; Cumming, Ian

1993-01-01

It is recognized that mean square error (MSE) is not a sufficient criterion for determining the acceptability of an image reconstructed from data that has been compressed and decompressed using an encoding algorithm. In the case of Synthetic Aperture Radar (SAR) data, it is also deemed to be insufficient to display the reconstructed image (and perhaps error image) alongside the original and make a (subjective) judgment as to the quality of the reconstructed data. In this paper we suggest a number of additional evaluation criteria which we feel should be included as evaluation metrics in SAR data encoding experiments. These criteria have been specifically chosen to provide a means of ensuring that the important information in the SAR data is preserved. The paper also presents the results of an investigation into the effects of coding on SAR data fidelity when the coding is applied in (1) the signal data domain, and (2) the image domain. An analysis of the results highlights the shortcomings of the MSE criterion, and shows which of the suggested additional criterion have been found to be most important.

Space Radar Image of West Texas - SAR Scan

NASA Image and Video Library

1999-04-15

This radar image of the Midland/Odessa region of West Texas, demonstrates an experimental technique, called ScanSAR, that allows scientists to rapidly image large areas of the Earth's surface. The large image covers an area 245 kilometers by 225 kilometers (152 miles by 139 miles). It was obtained by the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) flying aboard the space shuttle Endeavour on October 5, 1994. The smaller inset image is a standard SIR-C image showing a portion of the same area, 100 kilometers by 57 kilometers (62 miles by 35 miles) and was taken during the first flight of SIR-C on April 14, 1994. The bright spots on the right side of the image are the cities of Odessa (left) and Midland (right), Texas. The Pecos River runs from the top center to the bottom center of the image. Along the left side of the image are, from top to bottom, parts of the Guadalupe, Davis and Santiago Mountains. North is toward the upper right. Unlike conventional radar imaging, in which a radar continuously illuminates a single ground swath as the space shuttle passes over the terrain, a Scansar radar illuminates several adjacent ground swaths almost simultaneously, by "scanning" the radar beam across a large area in a rapid sequence. The adjacent swaths, typically about 50 km (31 miles) wide, are then merged during ground processing to produce a single large scene. Illumination for this L-band scene is from the top of the image. The beams were scanned from the top of the scene to the bottom, as the shuttle flew from left to right. This scene was acquired in about 30 seconds. A normal SIR-C image is acquired in about 13 seconds. The ScanSAR mode will likely be used on future radar sensors to construct regional and possibly global radar images and topographic maps. The ScanSAR processor is being designed for 1996 implementation at NASA's Alaska SAR Facility, located at the University of Alaska Fairbanks, and will produce digital images from the forthcoming Canadian RADARSAT satellite. http://photojournal.jpl.nasa.gov/catalog/PIA01787

Resolution Enhancement Algorithm for Spaceborn SAR Based on Hanning Function Weighted Sidelobe Suppression

NASA Astrophysics Data System (ADS)

Li, C.; Zhou, X.; Tang, D.; Zhu, Z.

2018-04-01

Resolution and sidelobe are mutual restrict for SAR image. Usually sidelobe suppression is based on resolution reduction. This paper provide a method for resolution enchancement using sidelobe opposition speciality of hanning window and SAR image. The method can keep high resolution on the condition of sidelobe suppression. Compare to traditional method, this method can enchance 50 % resolution when sidelobe is -30dB.

JPL Researcher Bruce Chapman at an AirSAR station aboard NASA's DC-8 flying laboratory during the AirSAR 2004 campaign

NASA Image and Video Library

2004-03-03

JPL Researcher Bruce Chapman at an AirSAR station aboard NASA's DC-8 flying laboratory during the AirSAR 2004 campaign. AirSAR 2004 is a three-week expedition by an international team of scientists that will use an all-weather imaging tool, called the Airborne Synthetic Aperture Radar (AirSAR), in a mission ranging from the tropical rain forests of Central America to frigid Antarctica.

Time domain SAR raw data simulation using CST and image focusing of 3D objects

NASA Astrophysics Data System (ADS)

Saeed, Adnan; Hellwich, Olaf

2017-10-01

This paper presents the use of a general purpose electromagnetic simulator, CST, to simulate realistic synthetic aperture radar (SAR) raw data of three-dimensional objects. Raw data is later focused in MATLAB using range-doppler algorithm. Within CST Microwave Studio a replica of TerraSAR-X chirp signal is incident upon a modeled Corner Reflector (CR) whose design and material properties are identical to that of the real one. Defining mesh and other appropriate settings reflected wave is measured at several distant points within a line parallel to the viewing direction. This is analogous to an array antenna and is synthesized to create a long aperture for SAR processing. The time domain solver in CST is based on the solution of differential form of Maxwells equations. Exported data from CST is arranged into a 2-d matrix of axis range and azimuth. Hilbert transform is applied to convert the real signal to complex data with phase information. Range compression, range cell migration correction (RCMC), and azimuth compression are applied in time domain to obtain the final SAR image. This simulation can provide valuable information to clarify which real world objects cause images suitable for high accuracy identification in the SAR images.

Simultaneous electroencephalography-functional MRI at 3 T: an analysis of safety risks imposed by performing anatomical reference scans with the EEG equipment in place.

PubMed

Nöth, Ulrike; Laufs, Helmut; Stoermer, Robert; Deichmann, Ralf

2012-03-01

To describe heating effects to be expected in simultaneous electroencephalography (EEG) and magnetic resonance imaging (MRI) when deviating from the EEG manufacturer's instructions; to test which anatomical MRI sequences have a sufficiently low specific absorption rate (SAR) to be performed with the EEG equipment in place; and to suggest precautions to reduce the risk of heating. Heating was determined in vivo below eight EEG electrodes, using both head and body coil transmission and sequences covering the whole range of SAR values. Head transmit coil: temperature increases were below 2.2°C for low SAR sequences, but reached 4.6°C (one subject, clavicle) for high SAR sequences; the equilibrium temperature T(eq) remained below 39°C. Body transmit coil: temperature increases were higher and more frequent over subjects and electrodes, with values below 2.6°C for low SAR sequences, reaching 6.9°C for high SAR sequences (T8 electrode) with T(eq) exceeding a critical level of 40°C. Anatomical imaging should be based on T1-weighted sequences (FLASH, MPRAGE, MDEFT) with an SAR below values for functional MRI sequences based on gradient echo planar imaging. Anatomical sequences with a high SAR can pose a significant risk, which is reduced by using head coil transmission. Copyright © 2011 Wiley-Liss, Inc.

Computational efficient unsupervised coastline detection from single-polarization 1-look SAR images of complex coastal environments

NASA Astrophysics Data System (ADS)

Garzelli, Andrea; Zoppetti, Claudia; Pinelli, Gianpaolo

2017-10-01

Coastline detection in synthetic aperture radar (SAR) images is crucial in many application fields, from coastal erosion monitoring to navigation, from damage assessment to security planning for port facilities. The backscattering difference between land and sea is not always documented in SAR imagery, due to the severe speckle noise, especially in 1-look data with high spatial resolution, high sea state, or complex coastal environments. This paper presents an unsupervised, computationally efficient solution to extract the coastline acquired by only one single-polarization 1-look SAR image. Extensive tests on Spotlight COSMO-SkyMed images of complex coastal environments and objective assessment demonstrate the validity of the proposed procedure which is compared to state-of-the-art methods through visual results and with an objective evaluation of the distance between the detected and the true coastline provided by regional authorities.

Research on Multi-Temporal PolInSAR Modeling and Applications

NASA Astrophysics Data System (ADS)

Hong, Wen; Pottier, Eric; Chen, Erxue

2014-11-01

In the study of theory and processing methodology, we apply accurate topographic phase to the Freeman-Durden decomposition for PolInSAR data. On the other hand, we present a TomoSAR imaging method based on convex optimization regularization theory. The target decomposition and reconstruction performance will be evaluated by multi-temporal Land P-band fully polarimetric images acquired in BioSAR campaigns. In the study of hybrid Quad-Pol system performance, we analyse the expression of range ambiguity to signal ratio (RASR) in this architecture. Simulations are used to testify its advantage in the improvement of range ambiguities.

Research on Multi-Temporal PolInSAR Modeling and Applications

NASA Astrophysics Data System (ADS)

Hong, Wen; Pottier, Eric; Chen, Erxue

2014-11-01

In the study of theory and processing methodology, we apply accurate topographic phase to the Freeman- Durden decomposition for PolInSAR data. On the other hand, we present a TomoSAR imaging method based on convex optimization regularization theory. The target decomposition and reconstruction performance will be evaluated by multi-temporal L- and P-band fully polarimetric images acquired in BioSAR campaigns. In the study of hybrid Quad-Pol system performance, we analyse the expression of range ambiguity to signal ratio (RASR) in this architecture. Simulations are used to testify its advantage in the improvement of range ambiguities.

Non-Reporting Ship Traffic in the Western Indian Ocean

NASA Astrophysics Data System (ADS)

Greidanus, Harm; Santamaria, Carlos; Alvarez, Marlene; Krause, Detmar; Stasolla, Mattia; Vachon, Paris W.

2016-08-01

AIS ship position reporting data from up to 17 satellites and several coastal locations covering the Western Indian Ocean were collected during a period of one year, that ended 15 Sep 2015. In addition, 1,361 satellite SAR images that were acquired over the region in the same timeframe, were analysed for ship detection. The major part of these were Sentinel-1 images that were analysed fully automatically, yielding 11,510 ship detections that were deemed reliable. Correlating these detections with the reporting ship traffic indicates that, overall, fully one-third of the ships detected with satellite SAR are not reporting on AIS. Some of the analysed SAR data was subjected to manual verification. This concerned data from TerraSAR-X, RADARSAT-2, COSMO-SkyMed, and ALOS-2- PALSAR of various image modes, plus some of the Sentinel-1 images. This confirmed the quoted average for the fraction of non-reporting ships. However, within the overall average there are large geographical variations, besides variations with image resolution.

Image Change Detection via Ensemble Learning

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

Martin, Benjamin W; Vatsavai, Raju

2013-01-01

The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work,more » we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.« less

High Resolution Rapid Revisits Insar Monitoring of Surface Deformation

NASA Astrophysics Data System (ADS)

Singhroy, V.; Li, J.; Charbonneau, F.

2014-12-01

Monitoring surface deformation on strategic energy and transportation corridors requires high resolution spatial and temporal InSAR images for mitigation and safety purposes. High resolution air photos, lidar and other satellite images are very useful in areas where the landslides can be fatal. Recently, radar interferometry (InSAR) techniques using more rapid revisit images from several radar satellites are increasingly being used in active deformation monitoring. The Canadian RADARSAT Constellation (RCM) is a three-satellite mission that will provide rapid revisits of four days interferometric (InSAR) capabilities that will be very useful for complex deformation monitoring. For instance, the monitoring of surface deformation due to permafrost activity, complex rock slide motion and steam assisted oil extraction will benefit from this new rapid revisit capability. This paper provide examples of how the high resolution (1-3 m) rapid revisit InSAR capabilities will improve our monitoring of surface deformation and provide insights in understanding triggering mechanisms. We analysed over a hundred high resolution InSAR images over a two year period on three geologically different sites with various configurations of topography, geomorphology, and geology conditions. We show from our analysis that the more frequent InSAR acquisitions are providing more information in understanding the rates of movement and failure process of permafrost triggered retrogressive thaw flows; the complex motion of an asymmetrical wedge failure of an active rock slide and the identification of over pressure zones related to oil extraction using steam injection. Keywords: High resolution, InSAR, rapid revisits, triggering mechanisms, oil extraction.

Radarsat-1 and ERS InSAR analysis over southeastern coastal Louisiana: Implications for mapping water-level changes beneath swamp forests

USGS Publications Warehouse

Lu, Z.; Kwoun, Oh-Ig

2008-01-01

Detailed analysis of C-band European Remote Sensing 1 and 2 (ERS-1/ERS-2) and Radarsat-1 interferometric synthetic aperture radar (InSAR) imagery was conducted to study water-level changes of coastal wetlands of southeastern Louisiana. Radar backscattering and InSAR coherence suggest that the dominant radar backscattering mechanism for swamp forest and saline marsh is double-bounce backscattering, implying that InSAR images can be used to estimate water-level changes with unprecedented spatial details. On the one hand, InSAR images suggest that water-level changes over the study site can be dynamic and spatially heterogeneous and cannot be represented by readings from sparsely distributed gauge stations. On the other hand, InSAR phase measurements are disconnected by structures and other barriers and require absolute water-level measurements from gauge stations or other sources to convert InSAR phase values to absolute water-level changes. ?? 2006 IEEE.

Urban Monitoring Based on SENTINEL-1 Data Using Permanent Scatterer Interferometry and SAR Tomography

NASA Astrophysics Data System (ADS)

Crosetto, M.; Budillon, A.; Johnsy, A.; Schirinzi, G.; Devanthéry, N.; Monserrat, O.; Cuevas-González, M.

2018-04-01

A lot of research and development has been devoted to the exploitation of satellite SAR images for deformation measurement and monitoring purposes since Differential Interferometric Synthetic Apertura Radar (InSAR) was first described in 1989. In this work, we consider two main classes of advanced DInSAR techniques: Persistent Scatterer Interferometry and Tomographic SAR. Both techniques make use of multiple SAR images acquired over the same site and advanced procedures to separate the deformation component from the other phase components, such as the residual topographic component, the atmospheric component, the thermal expansion component and the phase noise. TomoSAR offers the advantage of detecting either single scatterers presenting stable proprieties over time (Persistent Scatterers) and multiple scatterers interfering within the same range-azimuth resolution cell, a significant improvement for urban areas monitoring. This paper addresses a preliminary inter-comparison of the results of both techniques, for a test site located in the metropolitan area of Barcelona (Spain), where interferometric Sentinel-1 data were analysed.

Target-Oriented High-Resolution SAR Image Formation via Semantic Information Guided Regularizations

NASA Astrophysics Data System (ADS)

Hou, Biao; Wen, Zaidao; Jiao, Licheng; Wu, Qian

2018-04-01

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image. However, since the simple prior of low level features are insufficient to describe different semantic contents in the image, this type of regularizer will be incapable of distinguishing between the target of interest and unconcerned background clutters. As a consequence, the features belonging to the target and clutters are simultaneously affected in the generated image without concerning their underlying semantic labels. To address this problem, we propose a novel semantic information guided framework for target oriented SAR image formation, which aims at enhancing the interested target scatters while suppressing the background clutters. Firstly, we develop a new semantics-specific regularizer for image formation by exploiting the statistical properties of different semantic categories in a target scene SAR image. In order to infer the semantic label for each pixel in an unsupervised way, we moreover induce a novel high-level prior-driven regularizer and some semantic causal rules from the prior knowledge. Finally, our regularized framework for image formation is further derived as a simple iteratively reweighted $\\ell_1$ minimization problem which can be conveniently solved by many off-the-shelf solvers. Experimental results demonstrate the effectiveness and superiority of our framework for SAR image formation in terms of target enhancement and clutters suppression, compared with the state of the arts. Additionally, the proposed framework opens a new direction of devoting some machine learning strategies to image formation, which can benefit the subsequent decision making tasks.

Analysis of Multipath Pixels in SAR Images

NASA Astrophysics Data System (ADS)

Zhao, J. W.; Wu, J. C.; Ding, X. L.; Zhang, L.; Hu, F. M.

2016-06-01

As the received radar signal is the sum of signal contributions overlaid in one single pixel regardless of the travel path, the multipath effect should be seriously tackled as the multiple bounce returns are added to direct scatter echoes which leads to ghost scatters. Most of the existing solution towards the multipath is to recover the signal propagation path. To facilitate the signal propagation simulation process, plenty of aspects such as sensor parameters, the geometry of the objects (shape, location, orientation, mutual position between adjacent buildings) and the physical parameters of the surface (roughness, correlation length, permittivity)which determine the strength of radar signal backscattered to the SAR sensor should be given in previous. However, it's not practical to obtain the highly detailed object model in unfamiliar area by field survey as it's a laborious work and time-consuming. In this paper, SAR imaging simulation based on RaySAR is conducted at first aiming at basic understanding of multipath effects and for further comparison. Besides of the pre-imaging simulation, the product of the after-imaging, which refers to radar images is also taken into consideration. Both Cosmo-SkyMed ascending and descending SAR images of Lupu Bridge in Shanghai are used for the experiment. As a result, the reflectivity map and signal distribution map of different bounce level are simulated and validated by 3D real model. The statistic indexes such as the phase stability, mean amplitude, amplitude dispersion, coherence and mean-sigma ratio in case of layover are analyzed with combination of the RaySAR output.

Real-Time Spaceborne Synthetic Aperture Radar Float-Point Imaging System Using Optimized Mapping Methodology and a Multi-Node Parallel Accelerating Technique

PubMed Central

Li, Bingyi; Chen, Liang; Yu, Wenyue; Xie, Yizhuang; Bian, Mingming; Zhang, Qingjun; Pang, Long

2018-01-01

With the development of satellite load technology and very large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. A key goal of the on-board SAR imaging system design is to achieve high real-time processing performance under severe size, weight, and power consumption constraints. This paper presents a multi-node prototype system for real-time SAR imaging processing. We decompose the commonly used chirp scaling (CS) SAR imaging algorithm into two parts according to the computing features. The linearization and logic-memory optimum allocation methods are adopted to realize the nonlinear part in a reconfigurable structure, and the two-part bandwidth balance method is used to realize the linear part. Thus, float-point SAR imaging processing can be integrated into a single Field Programmable Gate Array (FPGA) chip instead of relying on distributed technologies. A single-processing node requires 10.6 s and consumes 17 W to focus on 25-km swath width, 5-m resolution stripmap SAR raw data with a granularity of 16,384 × 16,384. The design methodology of the multi-FPGA parallel accelerating system under the real-time principle is introduced. As a proof of concept, a prototype with four processing nodes and one master node is implemented using a Xilinx xc6vlx315t FPGA. The weight and volume of one single machine are 10 kg and 32 cm × 24 cm × 20 cm, respectively, and the power consumption is under 100 W. The real-time performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging. PMID:29495637

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.

PubMed

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

PubMed Central

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

JPL Researcher Tim Miller at the primary AirSAR station aboard NASA's DC-8 flying laboratory during the AirSAR 2004 campaign

NASA Image and Video Library

2004-03-03

JPL Researcher Tim Miller at the primary AirSAR station aboard NASA's DC-8 flying laboratory during the AirSAR 2004 campaign. AirSAR 2004 is a three-week expedition by an international team of scientists that will use an all-weather imaging tool, called the Airborne Synthetic Aperture Radar (AirSAR), in a mission ranging from the tropical rain forests of Central America to frigid Antarctica.

Rock type discrimination and structural analysis with LANDSAT and Seasat data: San Rafael swell, Utah

NASA Technical Reports Server (NTRS)

Stewart, H. E.; Blom, R.; Abrams, M.; Daily, M.

1980-01-01

Satellite synthetic aperture radar (SAR) images is evaluated in terms of its geologic applications. The benchmark to which the SAR images are compared is LANDSAT, used both for structural and lithologic interpretations.

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

NASA Astrophysics Data System (ADS)

Gao, Yu-Fei; Gui, Guan; Cong, Xun-Chao; Yang, Yue; Zou, Yan-Bin; Wan, Qun

2017-12-01

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

On The Spatial Homogeneity Of The Wave Spectra In Deep Water Employing ERS-2 SAR Precision Image

NASA Astrophysics Data System (ADS)

Violante-Carvalho, Nelson; Robinson, Ian; Gommenginger, Christine; Carvalho, Luiz Mariano; Goldstein, Brunno

2010-04-01

Using wave spectra extracted from image mode ERS-2 SAR, the spatial homogeneity of the wave field in deep water is investigated against directional buoy measurements. From the 100 x 100 km image, several small images of 6.4 x 6.4 km are selected and the wave spectra are computed. The locally disturbed wind velocity pat- tern, caused by the sheltering effect of large mountains near the coast, translates into the selected SAR image as regions of higher and lower wind speed. Assuming that a swell component is uniform over the whole image, SAR wave spectra retrieved from the sheltered and non-sheltered areas are intercompared. Any difference between them could be related to a possible interaction between wind sea and swell, since the wind sea part of the spectrum would be slightly different due to the different wind speeds. The results show that there is no significative variation, and apparently there is no clear difference in the swell spectra despite the different wind sea components.

Doppler synthetic aperture radar interferometry: a novel SAR interferometry for height mapping using ultra-narrowband waveforms

NASA Astrophysics Data System (ADS)

Yazıcı, Birsen; Son, Il-Young; Cagri Yanik, H.

2018-05-01

This paper introduces a new and novel radar interferometry based on Doppler synthetic aperture radar (Doppler-SAR) paradigm. Conventional SAR interferometry relies on wideband transmitted waveforms to obtain high range resolution. Topography of a surface is directly related to the range difference between two antennas configured at different positions. Doppler-SAR is a novel imaging modality that uses ultra-narrowband continuous waves (UNCW). It takes advantage of high resolution Doppler information provided by UNCWs to form high resolution SAR images. We introduce the theory of Doppler-SAR interferometry. We derive an interferometric phase model and develop the equations of height mapping. Unlike conventional SAR interferometry, we show that the topography of a scene is related to the difference in Doppler frequency between two antennas configured at different velocities. While the conventional SAR interferometry uses range, Doppler and Doppler due to interferometric phase in height mapping; Doppler-SAR interferometry uses Doppler, Doppler-rate and Doppler-rate due to interferometric phase in height mapping. We demonstrate our theory in numerical simulations. Doppler-SAR interferometry offers the advantages of long-range, robust, environmentally friendly operations; low-power, low-cost, lightweight systems suitable for low-payload platforms, such as micro-satellites; and passive applications using sources of opportunity transmitting UNCW.

Digital elevation model generation from satellite interferometric synthetic aperture radar: Chapter 5

USGS Publications Warehouse

Lu, Zhong; Dzurisin, Daniel; Jung, Hyung-Sup; Zhang, Lei; Lee, Wonjin; Lee, Chang-Wook

2012-01-01

An accurate digital elevation model (DEM) is a critical data set for characterizing the natural landscape, monitoring natural hazards, and georeferencing satellite imagery. The ideal interferometric synthetic aperture radar (InSAR) configuration for DEM production is a single-pass two-antenna system. Repeat-pass single-antenna satellite InSAR imagery, however, also can be used to produce useful DEMs. DEM generation from InSAR is advantageous in remote areas where the photogrammetric approach to DEM generation is hindered by inclement weather conditions. There are many sources of errors in DEM generation from repeat-pass InSAR imagery, for example, inaccurate determination of the InSAR baseline, atmospheric delay anomalies, and possible surface deformation because of tectonic, volcanic, or other sources during the time interval spanned by the images. This chapter presents practical solutions to identify and remove various artifacts in repeat-pass satellite InSAR images to generate a high-quality DEM.

Ground settlement monitoring from temporarily persistent scatterers between two SAR acquisitions

USGS Publications Warehouse

Lei, Z.; Xiaoli, D.; Guangcai, F.; Zhong, L.

2009-01-01

We present an improved differential interferometric synthetic aperture radar (DInSAR) analysis method that measures motions of scatterers whose phases are stable between two SAR acquisitions. Such scatterers are referred to as temporarily persistent scatterers (TPS) for simplicity. Unlike the persistent scatterer InSAR (PS-InSAR) method that relies on a time-series of interferograms, the new algorithm needs only one interferogram. TPS are identified based on pixel offsets between two SAR images, and are specially coregistered based on their estimated offsets instead of a global polynomial for the whole image. Phase unwrapping is carried out based on an algorithm for sparse data points. The method is successfully applied to measure the settlement in the Hong Kong Airport area. The buildings surrounded by vegetation were successfully selected as TPS and the tiny deformation signal over the area was detected. ??2009 IEEE.

SAR image formation with azimuth interpolation after azimuth transform

DOEpatents

Doerry,; Armin W. , Martin; Grant D. , Holzrichter; Michael, W [Albuquerque, NM

2008-07-08

Two-dimensional SAR data can be processed into a rectangular grid format by subjecting the SAR data to a Fourier transform operation, and thereafter to a corresponding interpolation operation. Because the interpolation operation follows the Fourier transform operation, the interpolation operation can be simplified, and the effect of interpolation errors can be diminished. This provides for the possibility of both reducing the re-grid processing time, and improving the image quality.

Sea ice radar signatures from ERS-1 SAR during late Summer and Fall in the Beaufort and Chukchi Seas

NASA Technical Reports Server (NTRS)

Holt, Benjamin; Cunningham, Glenn; Kwok, Ron

1993-01-01

A study which examines ERS-1 C band SAR (Synthetic Aperture Radar) imagery of sea ice obtained in the Beaufort and Chukchi Seas from mid Summer through Fall freeze up and early Winter in 1991 is presented. Radar backscatter statistics of sea ice were obtained from the imagery, using common floes tracked through consecutive repeat images whenever possible. During the Summer months, strong fluctuations in ice signatures of several dB are observed over 2 to 3 day periods, which are found to be closely related to air temperature excursions above and below freezing that alters the phase of the ice surface. As air temperatures drop steadily below freezing in the Fall, the signatures of the pack ice increase in brightness and become more stable with time. Multiyear ice is distinguished from rough and smooth first year ice. There are also variations in the multiyear signatures with latitude. Large variations are seen in new ice and open water contained within leads which results in ambiguous classification.

Fast iterative censoring CFAR algorithm for ship detection from SAR images

NASA Astrophysics Data System (ADS)

Gu, Dandan; Yue, Hui; Zhang, Yuan; Gao, Pengcheng

2017-11-01

Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.

A novel multi-band SAR data technique for fully automatic oil spill detection in the ocean

NASA Astrophysics Data System (ADS)

Del Frate, Fabio; Latini, Daniele; Taravat, Alireza; Jones, Cathleen E.

2013-10-01

With the launch of the Italian constellation of small satellites for the Mediterranean basin observation COSMO-SkyMed and the German TerraSAR-X missions, the delivery of very high-resolution SAR data to observe the Earth day or night has remarkably increased. In particular, also taking into account other ongoing missions such as Radarsat or those no longer working such as ALOS PALSAR, ERS-SAR and ENVISAT the amount of information, at different bands, available for users interested in oil spill analysis has become highly massive. Moreover, future SAR missions such as Sentinel-1 are scheduled for launch in the very next years while additional support can be provided by Uninhabited Aerial Vehicle (UAV) SAR systems. Considering the opportunity represented by all these missions, the challenge is to find suitable and adequate image processing multi-band procedures able to fully exploit the huge amount of data available. In this paper we present a new fast, robust and effective automated approach for oil-spill monitoring starting from data collected at different bands, polarizations and spatial resolutions. A combination of Weibull Multiplicative Model (WMM), Pulse Coupled Neural Network (PCNN) and Multi-Layer Perceptron (MLP) techniques is proposed for achieving the aforementioned goals. One of the most innovative ideas is to separate the dark spot detection process into two main steps, WMM enhancement and PCNN segmentation. The complete processing chain has been applied to a data set containing C-band (ERS-SAR, ENVISAT ASAR), X-band images (Cosmo-SkyMed and TerraSAR-X) and L-band images (UAVSAR) for an overall number of more than 200 images considered.

Remote sensing with spaceborne synthetic aperture imaging radars: A review

NASA Technical Reports Server (NTRS)

Cimino, J. B.; Elachi, C.

1983-01-01

A review is given of remote sensing with Spaceborne Synthetic Aperture Radars (SAR's). In 1978, a spaceborne SA was flown on the SEASAT satellite. It acquired high resulution images over many regions in North America and the North Pacific. The acquired data clearly demonstrate the capability of spaceborne SARs to: image and track polar ice floes; image ocean surface patterns including swells, internal waves, current boundaries, weather boundaries and vessels; and image land features which are used to acquire information about the surface geology and land cover. In 1981, another SAR was flown on the second shuttle flight. This Shuttle Imaging Radar (SIR-A) acquired land and ocean images over many areas around the world. The emphasis of the SIR-A experiment was mainly toward geologic mapping. Some of the key results of the SIR-A experiment are given.

Compact time- and space-integrating SAR processor: performance analysis

NASA Astrophysics Data System (ADS)

Haney, Michael W.; Levy, James J.; Michael, Robert R., Jr.; Christensen, Marc P.

1995-06-01

Progress made during the previous 12 months toward the fabrication and test of a flight demonstration prototype of the acousto-optic time- and space-integrating real-time SAR image formation processor is reported. Compact, rugged, and low-power analog optical signal processing techniques are used for the most computationally taxing portions of the SAR imaging problem to overcome the size and power consumption limitations of electronic approaches. Flexibility and performance are maintained by the use of digital electronics for the critical low-complexity filter generation and output image processing functions. The results reported for this year include tests of a laboratory version of the RAPID SAR concept on phase history data generated from real SAR high-resolution imagery; a description of the new compact 2D acousto-optic scanner that has a 2D space bandwidth product approaching 106 sports, specified and procured for NEOS Technologies during the last year; and a design and layout of the optical module portion of the flight-worthy prototype.

Semi-physical Simulation of the Airborne InSAR based on Rigorous Geometric Model and Real Navigation Data

NASA Astrophysics Data System (ADS)

Changyong, Dou; Huadong, Guo; Chunming, Han; yuquan, Liu; Xijuan, Yue; Yinghui, Zhao

2014-03-01

Raw signal simulation is a useful tool for the system design, mission planning, processing algorithm testing, and inversion algorithm design of Synthetic Aperture Radar (SAR). Due to the wide and high frequent variation of aircraft's trajectory and attitude, and the low accuracy of the Position and Orientation System (POS)'s recording data, it's difficult to quantitatively study the sensitivity of the key parameters, i.e., the baseline length and inclination, absolute phase and the orientation of the antennas etc., of the airborne Interferometric SAR (InSAR) system, resulting in challenges for its applications. Furthermore, the imprecise estimation of the installation offset between the Global Positioning System (GPS), Inertial Measurement Unit (IMU) and the InSAR antennas compounds the issue. An airborne interferometric SAR (InSAR) simulation based on the rigorous geometric model and real navigation data is proposed in this paper, providing a way for quantitatively studying the key parameters and for evaluating the effect from the parameters on the applications of airborne InSAR, as photogrammetric mapping, high-resolution Digital Elevation Model (DEM) generation, and surface deformation by Differential InSAR technology, etc. The simulation can also provide reference for the optimal design of the InSAR system and the improvement of InSAR data processing technologies such as motion compensation, imaging, image co-registration, and application parameter retrieval, etc.

Precipitation evidences on X-Band Synthetic Aperture Radar imagery: an approach for quantitative detection and estimation

NASA Astrophysics Data System (ADS)

Mori, Saverio; Marzano, Frank S.; Montopoli, Mario; Pulvirenti, Luca; Pierdicca, Nazzareno

2017-04-01

Spaceborne synthetic aperture radars (SARs) operating at L-band and above are nowadays a well-established tool for Earth remote sensing; among the numerous civil applications we can indicate flood areas detection and monitoring, earthquakes analysis, digital elevation model production, land use monitoring and classification. Appealing characteristics of this kind of instruments is the high spatial resolution ensured in almost all-weather conditions and with a reasonable duty cycle and coverage. This result has achieved by the by the most recent generation of SAR missions, which moreover allow polarimetric observation of the target. Nevertheless, atmospheric clouds, in particular the precipitating ones, can significantly affect the signal backscattered from the ground surface (e.g. Ferrazzoli and Schiavon, 1997), on both amplitude and phase, with effects increasing with the operating frequency. In this respect, proofs are given by several recent works (e.g. Marzano et al., 2010, Baldini et al., 2014) using X-Band SAR data by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions. On the other hand, this sensitivity open interesting perspectives towards the SAR observation, and eventually quantification, of precipitations. In this respect, a proposal approach for X-SARs precipitation maps production and cloud masking arise from our work. Cloud masking allows detection of precipitation compromised areas. Respect precipitation maps, satellite X-SARs offer the unique possibility to ingest within flood forecasting model precipitation data at the catchment scale. This aspect is particularly innovative, even if work has been done the late years, and some aspects need to still address. Our developed processing framework allows, within the cloud masking stage, distinguishing flooded areas, precipitating clouds together with permanent water bodies, all appearing dark in the SAR image. The procedure is mainly based on image segmentation techniques and fuzzy logic (e.g. Pulvirenti et al. 2014 and Mori et al. 2012); ancillary data, such as local incident angle and land cover, are used. This stage is necessary to tune the precipitation map stage and to avoid severe misinterpretations on the precipitation map routines. The second stage consist of estimating the local cloud attenuation. Finally the precipitation map is estimated, using the the retrieval algorithm developed by Marzano et al. (2011), applied only to pixels where rain is known to be present. Within the FP7 project EartH2Observe we have applied this methodology to 14 study cases, acquired within TSX and CSK missions over Italy and United States. This choice allows analysing both hurricane-like intense events and continental mid-latitude precipitations, with the possibility to verify and validate the proposed methodology through the available weather radar networks. Moreover it allows in same extent analysing the contribution of orography and quality of ancillary data (i.e. landcover). In this work we will discuss the results obtained until now in terms of improved rain cell localization and precipitation quantification.

Advanced InSAR imaging for dune mapping

NASA Astrophysics Data System (ADS)

Havivi, Shiran; August, Yitzhak; Blumberg, Dan G.; Rotman, Stanley R.

2015-04-01

Aeolian morphologies are formed in the presence of sufficient wind energy and available particles. These processes occur naturally or are further enhanced or reduced by human intervention. The dimensions of change are dependent primarily on the wind energy and surface properties. Since the 1970's, remote sensing imagery both optical and radar, are used for documentation and interpretation of the geomorphologic changes of sand dunes. Remote sensing studies of Aeolian morphologies is mostly useful to document major changes, yet, subtle changes, occurring in a period of days or months in scales of centimeters, are very difficult to detect in imagery. Interferometric Synthetic Aperture Radar (InSAR) is an imaging technique for measuring Earth's surface topography and deformation. InSAR images are produced by measuring the radar phase difference between two separated antennas that view the same surface area. Classical InSAR is based on high coherence between two images or more. The output (interferogram) can show subtle changes with an accuracy of several millimeters to centimeters. Very little work has been done on measuring or identifying the changes in dunes using InSAR. The reason is that dunes tend to be less coherent than firm, stable, surfaces. This research aims to demonstrate how interferometric decorrelation, or, coherence change detection, can be used for identifying dune instability. We hypothesize and demonstrate that the loss of radar coherence over time on dunes can be used as an indication of the dune's instability. When SAR images are acquired at sufficiently close intervals one can measure the time it takes to lose coherence and associate this time with geomorphic stability. To achieve our goals, the Nitzanim coastal dunes along the Mediterranean, 40 km south of Tel-Aviv, Israel, were chosen as a case study. The dunes in this area are of varying levels of stability and vegetation cover and have been monitored meteorologically, geomorphologically and extensively in the field. High resolution TerraSAR-X (TSX) images, covering the entire research area were acquired for the period of October 2011 to July 2012 (15 images in total). All images were co-registreted, the first image was used as the master image. A coherence index was calculated for all the images. Analysis was performed in GIS software. The results display minor changes (coherence index in range of 0.4-0.65) on dune crests depending on the dune location relative to its distance from the sea and distance from the city. In addition, field results indicate erosion / deposition of sand in a cumulatively amount of approximately 30mm annually. The results of this study confirm that it is possible to monitor subtle changes in dunes and to identify dune stability or instability, only by the use of SAR images.

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory.

PubMed

Zhou, Rui; Sun, Jinping; Hu, Yuxin; Qi, Yaolong

2018-01-31

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory

PubMed Central

Zhou, Rui; Hu, Yuxin; Qi, Yaolong

2018-01-01

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm. PMID:29385059

Performance of Scattering Matrix Decomposition and Color Spaces for Synthetic Aperture Radar Imagery

DTIC Science & Technology

2010-03-01

Color Spaces and Synthetic Aperture Radar (SAR) Multicolor Imaging. 15 2.3.1 Colorimetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.2...III. Decomposition Techniques on SAR Polarimetry and Colorimetry applied to SAR Imagery...space polarimetric SAR systems. Colorimetry is also introduced in this chapter, presenting the fundamentals of the RGB and CMY color spaces, defined for

Nonrigid synthetic aperture radar and optical image coregistration by combining local rigid transformations using a Kohonen network.

PubMed

Salehpour, Mehdi; Behrad, Alireza

2017-10-01

This study proposes a new algorithm for nonrigid coregistration of synthetic aperture radar (SAR) and optical images. The proposed algorithm employs point features extracted by the binary robust invariant scalable keypoints algorithm and a new method called weighted bidirectional matching for initial correspondence. To refine false matches, we assume that the transformation between SAR and optical images is locally rigid. This property is used to refine false matches by assigning scores to matched pairs and clustering local rigid transformations using a two-layer Kohonen network. Finally, the thin plate spline algorithm and mutual information are used for nonrigid coregistration of SAR and optical images.

Ship Detection Using High Resolution Satellite Imagery and Space-Based AIS

NASA Astrophysics Data System (ADS)

Hannevik, Tonje Nanette; Skauen, Andreas N.; Olsen, R. B.

2013-03-01

This paper presents a trial carried out in the Malangen area close to Tromsø city in the north of Norway in September 2010. High resolution Synthetic Aperture Radar (SAR) images from RADARSAT-2 were used to analyse how SAR images and cooperative reporting can be combined. Data from the Automatic Identification System, both land-based and space-based, have been used to identify detected vessels in the SAR images. The paper presents results of ship detection in high resolution RADARSAT-2 Standard Quad-Pol images, and how these results together with land-based and space-based AIS can be used. Some examples of tracking of vessels are also shown.

SAR image segmentation using skeleton-based fuzzy clustering

NASA Astrophysics Data System (ADS)

Cao, Yun Yi; Chen, Yan Qiu

2003-06-01

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

Multiresolution MAP despeckling of SAR images based on locally adaptive generalized Gaussian pdf modeling.

PubMed

Argenti, Fabrizio; Bianchi, Tiziano; Alparone, Luciano

2006-11-01

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori MIAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation.

An all-optronic synthetic aperture lidar

NASA Astrophysics Data System (ADS)

Turbide, Simon; Marchese, Linda; Terroux, Marc; Babin, François; Bergeron, Alain

2012-09-01

Synthetic Aperture Radar (SAR) is a mature technology that overcomes the diffraction limit of an imaging system's real aperture by taking advantage of the platform motion to coherently sample multiple sections of an aperture much larger than the physical one. Synthetic Aperture Lidar (SAL) is the extension of SAR to much shorter wavelengths (1.5 μm vs 5 cm). This new technology can offer higher resolution images in day or night time as well as in certain adverse conditions. It could be a powerful tool for Earth monitoring (ship detection, frontier surveillance, ocean monitoring) from aircraft, unattended aerial vehicle (UAV) or spatial platforms. A continuous flow of high-resolution images covering large areas would however produce a large amount of data involving a high cost in term of post-processing computational time. This paper presents a laboratory demonstration of a SAL system complete with image reconstruction based on optronic processing. This differs from the more traditional digital approach by its real-time processing capability. The SAL system is discussed and images obtained from a non-metallic diffuse target at ranges up to 3m are shown, these images being processed by a real-time optronic SAR processor origiinally designed to reconstruct SAR images from ENVISAT/ASAR data.

Observation of sea-ice dynamics using synthetic aperture radar images: Automated analysis

NASA Technical Reports Server (NTRS)

Vesecky, John F.; Samadani, Ramin; Smith, Martha P.; Daida, Jason M.; Bracewell, Ronald N.

1988-01-01

The European Space Agency's ERS-1 satellite, as well as others planned to follow, is expected to carry synthetic-aperture radars (SARs) over the polar regions beginning in 1989. A key component in utilization of these SAR data is an automated scheme for extracting the sea-ice velocity field from a time sequence of SAR images of the same geographical region. Two techniques for automated sea-ice tracking, image pyramid area correlation (hierarchical correlation) and feature tracking, are described. Each technique is applied to a pair of Seasat SAR sea-ice images. The results compare well with each other and with manually tracked estimates of the ice velocity. The advantages and disadvantages of these automated methods are pointed out. Using these ice velocity field estimates it is possible to construct one sea-ice image from the other member of the pair. Comparing the reconstructed image with the observed image, errors in the estimated velocity field can be recognized and a useful probable error display created automatically to accompany ice velocity estimates. It is suggested that this error display may be useful in segmenting the sea ice observed into regions that move as rigid plates of significant ice velocity shear and distortion.

An evaluation of processing InSAR Sentinel-1A/B data for correlation of mining subsidence with mining induced tremors in the Upper Silesian Coal Basin (Poland)

NASA Astrophysics Data System (ADS)

Krawczyk, Artur; Grzybek, Radosław

2018-01-01

The Satellite Radar Interferometry is one of the common methods that allow to measure the land subsidence caused by the underground black coal excavation. The interferometry images processed from the repeat-pass Synthetic Aperture Radar (SAR) systems give the spatial image of the terrain subjected to the surface subsidence over mining areas. Until now, the InSAR methods using data from the SAR Systems like ERS-1/ERS-2 and Envisat-1 were limited to a repeat-pass cycle of 35-day only. Recently, the ESA launched Sentinel-1A and 1B, and together they can provide the InSAR coverage in a 6-day repeat cycle. The studied area was the Upper Silesian Coal Basin in Poland, where the underground coal mining causes continuous subsidence of terrain surface and mining tremors (mine-induced seismicity). The main problem was with overlapping the subsidence caused by the mining exploitation with the epicentre tremors. Based on the Sentinel SAR images, research was done in regard to the correlation between the short term ground subsidence range border and the mine-induced seismicity epicentres localisation.

Separated Component-Based Restoration of Speckled SAR Images

DTIC Science & Technology

2013-01-01

unsupervised change detection from SAR amplitude imagery,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2972–2982, Oct. 2006. [5] F. Argenti , T...Sens., vol. 40, no. 10, pp. 2196–2212, Oct. 2002. [13] F. Argenti and L. Alparone, “Speckle removal from SAR images in the undecimated wavelet domain...iterative thresh- olding algorithm for linear inverse problems with a sparsity con- straint,” Commun . Pure Appl. Math., vol. 57, no. 11, pp. 1413

Flood Extent Delineation by Thresholding Sentinel-1 SAR Imagery Based on Ancillary Land Cover Information

NASA Astrophysics Data System (ADS)

Liang, J.; Liu, D.

2017-12-01

Emergency responses to floods require timely information on water extents that can be produced by satellite-based remote sensing. As SAR image can be acquired in adverse illumination and weather conditions, it is particularly suitable for delineating water extent during a flood event. Thresholding SAR imagery is one of the most widely used approaches to delineate water extent. However, most studies apply only one threshold to separate water and dry land without considering the complexity and variability of different dry land surface types in an image. This paper proposes a new thresholding method for SAR image to delineate water from other different land cover types. A probability distribution of SAR backscatter intensity is fitted for each land cover type including water before a flood event and the intersection between two distributions is regarded as a threshold to classify the two. To extract water, a set of thresholds are applied to several pairs of land cover types—water and urban or water and forest. The subsets are merged to form the water distribution for the SAR image during or after the flooding. Experiments show that this land cover based thresholding approach outperformed the traditional single thresholding by about 5% to 15%. This method has great application potential with the broadly acceptance of the thresholding based methods and availability of land cover data, especially for heterogeneous regions.

Detecting and monitoring UCG subsidence with InSAR

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

Mellors, R J; Foxall, W; Yang, X

2012-03-23

The use of interferometric synthetic aperture radar (InSAR) to measure surface subsidence caused by Underground Coal Gasification (UCG) is tested. InSAR is a remote sensing technique that uses Synthetic Aperture Radar images to make spatial images of surface deformation and may be deployed from satellite or an airplane. With current commercial satellite data, the technique works best in areas with little vegetation or farming activity. UCG subsidence is generally caused by roof collapse, which adversely affects UCG operations due to gas loss and is therefore important to monitor. Previous studies have demonstrated the usefulness of InSAR in measuring surface subsidencemore » related to coal mining and surface deformation caused by a coal mining roof collapse in Crandall Canyon, Utah is imaged as a proof-of-concept. InSAR data is collected and processed over three known UCG operations including two pilot plants (Majuba, South Africa and Wulanchabu, China) and an operational plant (Angren, Uzbekistan). A clear f eature showing approximately 7 cm of subsidence is observed in the UCG field in Angren. Subsidence is not observed in the other two areas, which produce from deeper coal seams and processed a smaller volume. The results show that in some cases, InSAR is a useful tool to image UCG related subsidence. Data from newer satellites and improved algorithms will improve effectiveness.« less

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

PubMed Central

You, Hongjian

2018-01-01

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach. PMID:29364194

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.

PubMed

An, Quanzhi; Pan, Zongxu; You, Hongjian

2018-01-24

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

Detecting Subsidence Along a High Speed Railway by Ultrashort Baseline TCP-InSAR with High Resolution Images

NASA Astrophysics Data System (ADS)

Dai, K. R.; Liu, G. X.; Yu, B.; Jia, H. G.; Ma, D. Y.; Wang, X. W.

2013-10-01

A High Speed Railway goes across Wuqing district of Tianjin, China. Historical studies showed that the land subsidence of this area was very serious, which would give rise to huge security risk to the high speed railway. For detecting the detailed subsidence related to the high speed railway, we use the multi-temporal InSAR (MT-InSAR) technique to extract regional scale subsidence of Wuqing district. Take it into consideration that Wuqing district is a suburban region with large area of low coherence farmland, we select the temporarily coherent point InSAR (TCP-InSAR) approach for MT-InSAR analysis. The TCP-InSAR is a potential approach for detecting land subsidence in low coherence areas as it can identify and analysis coherent points between just two images and can acquire a reliable solution without conventional phase unwrapping. This paper extended the TCP-InSAR with use of ultrashort spatial baseline (USB) interferograms. As thetopographic effects are negligible in the USB interferograms, an external digital elevation model (DEM) is no longer needed in interferometric processing, and the parameters needed to be estimated were simplified at the same time. With use of 17 TerraSAR-X (TSX) images acquired from 2009 to 2010 over Wuqing district, the annual subsidence rates along the high speed railway were derived by the USB-TCPInSAR approach. Two subsidence funnels were found at ShuangJie town and around Wuqing Station with subsidence rate of -17 ∼ -27 mm/year and -7 ∼ -17 mm/year, respectively. The subsidence rates derived by USB-TCPInSAR were compared with those derived by the conventional TCP-InSAR that uses an external DEM for differential interferometry. The mean and the standard deviation of the differences between two types of results at 370697 TCPs are -4.43 × 10-6 mm/year and ±1.4673 mm/year, respectively. Further comparison with the subsidence results mentioned in several other studies were made, which shows good consistencies. The results verify that even without using a DEM the USB-TCPInSAR method can detect land subsidence accurately in flat areas.

Compatibility of Army Systems with Anthropometric Characteristics of Female Soldiers

DTIC Science & Technology

1997-09-01

M, Industrial Security Manual, Section 11-19 or DoD 5200.1-R, Information Security Program Regulation, Chapter IX. For Unclassified/ Limited ...of female soldiers. Participation was limited to female soldiers whose height did not exceed 5’ 5", the 5th percentile value of male soldiers’ height...CLASSIFICATION tffcMSSIFIED 18. SECURITY CLASSIFICATION uffdLÄS^FIED 19. SECURITY CLASSIFICATION ST*—FIED 20. LIMITATION OF ABSTRACT SAR

Spaceborne synthetic aperture radar signal processing using FPGAs

NASA Astrophysics Data System (ADS)

Sugimoto, Yohei; Ozawa, Satoru; Inaba, Noriyasu

2017-10-01

Synthetic Aperture Radar (SAR) imagery requires image reproduction through successive signal processing of received data before browsing images and extracting information. The received signal data records of the ALOS-2/PALSAR-2 are stored in the onboard mission data storage and transmitted to the ground. In order to compensate the storage usage and the capacity of transmission data through the mission date communication networks, the operation duty of the PALSAR-2 is limited. This balance strongly relies on the network availability. The observation operations of the present spaceborne SAR systems are rigorously planned by simulating the mission data balance, given conflicting user demands. This problem should be solved such that we do not have to compromise the operations and the potential of the next-generation spaceborne SAR systems. One of the solutions is to compress the SAR data through onboard image reproduction and information extraction from the reproduced images. This is also beneficial for fast delivery of information products and event-driven observations by constellation. The Emergence Studio (Sōhatsu kōbō in Japanese) with Japan Aerospace Exploration Agency is developing evaluation models of FPGA-based signal processing system for onboard SAR image reproduction. The model, namely, "Fast L1 Processor (FLIP)" developed in 2016 can reproduce a 10m-resolution single look complex image (Level 1.1) from ALOS/PALSAR raw signal data (Level 1.0). The processing speed of the FLIP at 200 MHz results in twice faster than CPU-based computing at 3.7 GHz. The image processed by the FLIP is no way inferior to the image processed with 32-bit computing in MATLAB.

SAR imaging of ocean waves - Theory

NASA Technical Reports Server (NTRS)

Jain, A.

1981-01-01

A SAR imaging integral for a rough surface is derived. Aspects of distributed target imaging and questions of ocean-wave imaging are considered. A description is presented of the results of analyses which are performed on aircraft and a spacecraft data in order to gain an understanding of the SAR imaging of ocean waves. The analyzed data illustrate the effect of radar resolution on the images of azimuthally traveling waves, the dependence of image distortion on the angle which the waves make with the radar flight path, and the dependence of the focusing parameter of the radar matched filter on the ocean wave period for azimuthally traveling waves. A dependence of ocean-wave modulation on significant wave height is also observed. The observed dependence of the modulations of azimuth waves on radar resolution is in contradiction to the hypothesis that these modulations are caused mainly by velocity bunching.

Upper ocean fine-scale features in synthetic aperture radar imagery. Part I: Simultaneous satellite and in-situ measurements

NASA Astrophysics Data System (ADS)

Soloviev, A.; Maingot, C.; Matt, S.; Fenton, J.; Lehner, S.; Brusch, S.; Perrie, W. A.; Zhang, B.

2011-12-01

The new generation of synthetic aperture radar (SAR) satellites provides high resolution images that open new opportunities for identifying and studying fine features in the upper ocean. The problem is, however, that SAR images of the sea surface can be affected by atmospheric phenomena (rain cells, fronts, internal waves, etc.). Implementation of in-situ techniques in conjunction with SAR is instrumental for discerning the origin of features on the image. This work is aimed at the interpretation of natural and artificial features in SAR images. These features can include fresh water lenses, sharp frontal interfaces, internal wave signatures, as well as slicks of artificial and natural origin. We have conducted field experiments in the summer of 2008 and 2010 and in the spring of 2011 to collect in-situ measurements coordinated with overpasses of the TerraSAR-X, RADARSAT-2, ALOS PALSAR, and COSMO SkyMed satellites. The in-situ sensors deployed in the Straits of Florida included a vessel-mounted sonar and CTD system to record near-surface data on stratification and frontal boundaries, a bottom-mounted Nortek AWAC system to gather information on currents and directional wave spectra, an ADCP mooring at a 240 m isobath, and a meteorological station. A nearby NOAA NEXRAD Doppler radar station provided a record of rainfall in the area. Controlled releases of menhaden fish oil were performed from our vessel before several satellite overpasses in order to evaluate the effect of surface active materials on visibility of sea surface features in SAR imagery under different wind-wave conditions. We found evidence in the satellite images of rain cells, squall lines, internal waves of atmospheric and possibly oceanic origin, oceanic frontal interfaces and submesoscale eddies, as well as anthropogenic signatures of ships and their wakes, and near-shore surface slicks. The combination of satellite imagery and coordinated in-situ measurements was helpful in interpreting fine-scale features on the sea surface observed in the SAR images and, in some cases, linking them to thermohaline features in the upper ocean. Finally, we have been able to reproduce SAR signatures of freshwater plumes and sharp frontal interfaces interacting with wind stress, as well as internal waves by combining hydrodynamic simulations with a radar imaging algorithm. The modeling results are presented in a companion paper (Matt et al., 2011).

Space Radar Image of Kilauea, Hawaii

NASA Technical Reports Server (NTRS)

1994-01-01

Data acquired on April 13, 1994 and on October 4, 1994 from the X-band Synthetic Aperture Radar on board the space shuttle Endeavour were used to generate interferometric fringes, which were overlaid on the X-SAR image of Kilauea. The volcano is centered in this image at 19.58 degrees north latitude and 155.55 degrees west longitude. The image covers about 9 kilometers by 13 kilometers (5.6 miles by 8 miles). The X-band fringes correspond clearly to the expected topographic image. The yellow line indicates the area below which was used for the three-dimensional image using altitude lines. The yellow rectangular frame fences the area for the final topographic image. Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves, allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI), with the Deutsche Forschungsanstalt fuer Luft und Raumfahrt e.V.(DLR), the major partner in science, operations and data processing of X-SAR. The Instituto Ricerca Elettromagnetismo Componenti Elettronici (IRECE) at the University of Naples was a partner in interferometry analysis.

Generation and assessment of turntable SAR data for the support of ATR development

NASA Astrophysics Data System (ADS)

Cohen, Marvin N.; Showman, Gregory A.; Sangston, K. James; Sylvester, Vincent B.; Gostin, Lamar; Scheer, C. Ruby

1998-10-01

Inverse synthetic aperture radar (ISAR) imaging on a turntable-tower test range permits convenient generation of high resolution two-dimensional images of radar targets under controlled conditions for testing SAR image processing and for supporting automatic target recognition (ATR) algorithm development. However, turntable ISAR images are often obtained under near-field geometries and hence may suffer geometric distortions not present in airborne SAR images. In this paper, turntable data collected at Georgia Tech's Electromagnetic Test Facility are used to begin to assess the utility of two- dimensional ISAR imaging algorithms in forming images to support ATR development. The imaging algorithms considered include a simple 2D discrete Fourier transform (DFT), a 2-D DFT with geometric correction based on image domain resampling, and a computationally-intensive geometric matched filter solution. Images formed with the various algorithms are used to develop ATR templates, which are then compared with an eye toward utilization in an ATR algorithm.

Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation

PubMed Central

Nitti, Davide O.; Bovenga, Fabio; Chiaradia, Maria T.; Greco, Mario; Pinelli, Gianpaolo

2015-01-01

This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimate UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system. PMID:26225977

Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation.

PubMed

Nitti, Davide O; Bovenga, Fabio; Chiaradia, Maria T; Greco, Mario; Pinelli, Gianpaolo

2015-07-28

This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimated UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system.

Sparse representation based SAR vehicle recognition along with aspect angle.

PubMed

Xing, Xiangwei; Ji, Kefeng; Zou, Huanxin; Sun, Jixiang

2014-01-01

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

NASA Astrophysics Data System (ADS)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Ship Speed Retrieval From Single Channel TerraSAR-X Data

NASA Astrophysics Data System (ADS)

Soccorsi, Matteo; Lehner, Susanne

2010-04-01

A method to estimate the speed of a moving ship is presented. The technique, introduced in Kirscht (1998), is extended to marine application and validated on TerraSAR-X High-Resolution (HR) data. The generation of a sequence of single-look SAR images from a single- channel image corresponds to an image time series with reduced resolution. This allows applying change detection techniques on the time series to evaluate the velocity components in range and azimuth of the ship. The evaluation of the displacement vector of a moving target in consecutive images of the sequence allows the estimation of the azimuth velocity component. The range velocity component is estimated by evaluating the variation of the signal amplitude during the sequence. In order to apply the technique on TerraSAR-X Spot Light (SL) data a further processing step is needed. The phase has to be corrected as presented in Eineder et al. (2009) due to the SL acquisition mode; otherwise the image sequence cannot be generated. The analysis, when possible validated by the Automatic Identification System (AIS), was performed in the framework of the ESA project MARISS.

Enhancing the Accessibility and Utility of UAVSAR L-band SAR Data

NASA Astrophysics Data System (ADS)

Atwood, D.; Arko, S. A.; Gens, R.; Sanches, R. R.

2011-12-01

The UAVSAR instrument, developed at NASA Jet Propulsion Lab, is a reconfigurable L-band, quad-polarimetric Synthetic Aperture Radar (SAR) developed specifically for repeat-track differential interferometry (InSAR). It offers resolution of approximately 5m and swaths greater than 16 km. Although designed to be flown aboard a UAV (Uninhabited Aerial Vehicle), it is currently being flown aboard a Gulfstream III in an ambitious set of campaigns around the world. The current archive from 2009 contains data from more than 100 missions from North America, Central America, the Caribbean, and Greenland. Compared with most SAR data from satellites, UAVSAR offers higher resolution, full-polarimetry, and an impressive noise floor. For scientists, these datasets present wonderful opportunities for understanding Earth processes and developing new algorithms for information extraction. Yet despite the diverse range of coverage, UAVSAR is still relatively under-utilized. In its capacity as the NASA SAR DAAC, the Alaska Satellite Facility (ASF) is interested in expanding recognition of this data and serving data products that can be readily downloaded into a Geographic Information System (GIS) environment. Two hurdles exist: one is the large size of the data products and the second is the format of the data. The data volumes are in excess of several GB; presenting slow downloads and overwhelming many software programs. Secondly, while the data is appropriately formatted for expert users, it may prove challenging for scientists who have not previously worked with SAR. This paper will address ways that ASF is working to reduce data volume while maintaining the integrity of the data. At the same time, the creation of value-added products that permit immediate visualization in a GIS environment will be described. Conversion of the UAVSAR polarimetric data to radiometrically terrain-corrected Pauli images in a GeoTIFF format will permit researchers to understand the scattering mechanisms that characterize various land cover classes in their study areas. Specific examples of UAVSAR polarimetric classifications will be used to demonstrate the benefit of the UAVSAR products for Earth science projects.

SAR-based sea traffic monitoring: a reliable approach for maritime surveillance

NASA Astrophysics Data System (ADS)

Renga, Alfredo; Graziano, Maria D.; D'Errico, M.; Moccia, A.; Cecchini, A.

2011-11-01

Maritime surveillance problems are drawing the attention of multiple institutional actors. National and international security agencies are interested in matters like maritime traffic security, maritime pollution control, monitoring migration flows and detection of illegal fishing activities. Satellite imaging is a good way to identify ships but, characterized by large swaths, it is likely that the imaged scenes contain a large number of ships, with the vast majority, hopefully, performing legal activities. Therefore, the imaging system needs a supporting system which identifies legal ships and limits the number of potential alarms to be further monitored by patrol boats or aircrafts. In this framework, spaceborne Synthetic Aperture Radar (SAR) sensors, terrestrial AIS and the ongoing satellite AIS systems can represent a great potential synergy for maritime security. Starting from this idea the paper develops different designs for an AIS constellation able to reduce the time lag between SAR image and AIS data acquisition. An analysis of SAR-based ship detection algorithms is also reported and candidate algorithms identified.

An Evaluation of ALOS Data in Disaster Applications

NASA Astrophysics Data System (ADS)

Igarashi, Tamotsu; Igarashi, Tamotsu; Furuta, Ryoich; Ono, Makoto

ALOS is the advanced land observing satellite, providing image data from onboard sensors; PRISM, AVNIR-2 and PALSAR. PRISM is the sensor of panchromatic stereo, high resolution three-line-scanner to characterize the earth surface. The accuracy of position in image and height of Digital Surface Model (DSM) are high, therefore the geographic information extraction is improved in the field of disaster applications with providing images of disaster area. Especially pan-sharpened 3D image composed with PRISM and the four-band visible near-infrared radiometer AVNIR-2 data is expected to provide information to understand the geographic and topographic feature. PALSAR is the advanced multi-functional synthetic aperture radar (SAR) operated in L-band, appropriate for the use of land surface feature characterization. PALSAR has many improvements from JERS-1/SAR, such as high sensitivity, having high resolution, polarimetric and scan SAR observation modes. PALSAR is also applicable for SAR interferometry processing. This paper describes the evaluation of ALOS data characteristic from the view point of disaster applications, through some exercise applications.

SAR imaging of vortex ship wakes. Volume 3: An overview of pre-ERS-1 observations and models

NASA Astrophysics Data System (ADS)

Skoeelv, Aage; Wahl, Terje

1991-05-01

The visibility of dark turbulent wakes in Synthetic Aperture Radar (SAR) imagery is focused upon. An overview of various wake observations prior to ERS-1 is given. This includes images from Seasat and airborne SAR as well as photographic observations. Different turbulent wake models and simulation, schemes are reviewed. The requirements for a complete turbulent wake model are discussed, and from results available, some conclusions are drawn with respect to possible ERS-1 applications.

Polarimetric SAR Models for Oil Fields Monitoring in China Seas

NASA Astrophysics Data System (ADS)

Buono, A.; Nunziata, F.; Li, X.; Wei, Y.; Ding, X.

2014-11-01

In this study, physical-based models for polarimetric Synthetic Aperture Radar (SAR) oil fields monitoring are proposed. They all share a physical rationale relying on the different scattering mechanisms that characterize a free sea surface, an oil slick-covered sea surface, and a metallic target. In fact, sea surface scattering is well modeled by a Bragg-like behaviour, while a strong departure from Bragg scattering is in place when dealing with oil slicks and targets. Furthermore, the proposed polarimetric models aim at addressing simultaneously target and oil slick detection, providing useful extra information with respect to single-pol SAR data in order to approach oil discrimination and classification. Experiments undertaken over East and South China Sea from actual C-band RadarSAT-2 full-pol SAR data witness the soundness of the proposed rationale.

Polarimetric SAR Models for Oil Fields Monitoring in China Seas

NASA Astrophysics Data System (ADS)

Buono, A.; Nunziata, F.; Li, X.; Wei, Y.; Ding, X.

2014-11-01

In this study, physical-based models for polarimetric Synthetic Aperture Radar (SAR) oil fields monitoring are proposed. They all share a physical rationale relying on the different scattering mechanisms that characterize a free sea surface, an oil slick-covered sea surface, and a metallic target. In fact, sea surface scattering is well modeled by a Bragg-like behaviour, while a strong departure from Bragg scattering is in place when dealing with oil slicks and targets. Furthermore, the proposed polarimetric models aim at addressing simultaneously target and oil slick detection, providing useful extra information with respect to single-pol SAR data in order to approach oil discrimination and classification.Experiments undertaken over East and South China Sea from actual C-band RadarSAT-2 full-pol SAR data witness the soundness of the proposed rationale.

Interferometric synthetic aperture radar imagery of the Gulf Stream

NASA Technical Reports Server (NTRS)

Ainsworth, T. L.; Cannella, M. E.; Jansen, R. W.; Chubb, S. R.; Carande, R. E.; Foley, E. W.; Goldstein, R. M.; Valenzuela, G. R.

1993-01-01

The advent of interferometric synthetic aperture radar (INSAR) imagery brought to the ocean remote sensing field techniques used in radio astronomy. Whilst details of the interferometry differ between the two fields, the basic idea is the same: Use the phase information arising from positional differences of the radar receivers and/or transmitters to probe remote structures. The interferometric image is formed from two complex synthetic aperture radar (SAR) images. These two images are of the same area but separated in time. Typically the time between these images is very short -- approximately 50 msec for the L-band AIRSAR (Airborne SAR). During this short period the radar scatterers on the ocean surface do not have time to significantly decorrelate. Hence the two SAR images will have the same amplitude, since both obtain the radar backscatter from essentially the same object. Although the ocean surface structure does not significantly decorrelate in 50 msec, surface features do have time to move. It is precisely the translation of scattering features across the ocean surface which gives rise to phase differences between the two SAR images. This phase difference is directly proportional to the range velocity of surface scatterers. The constant of proportionality is dependent upon the interferometric mode of operation.

Phi-s correlation and dynamic time warping - Two methods for tracking ice floes in SAR images

NASA Technical Reports Server (NTRS)

Mcconnell, Ross; Kober, Wolfgang; Kwok, Ronald; Curlander, John C.; Pang, Shirley S.

1991-01-01

The authors present two algorithms for performing shape matching on ice floe boundaries in SAR (synthetic aperture radar) images. These algorithms quickly produce a set of ice motion and rotation vectors that can be used to guide a pixel value correlator. The algorithms match a shape descriptor known as the Phi-s curve. The first algorithm uses normalized correlation to match the Phi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates ice floe deformation. Some empirical data on the performance of the algorithms on Seasat SAR images are presented.

Estimation of the Above Ground Biomass of Tropical Forests using Polarimetric and Tomographic SAR Data Acquired at P Band and 3-D Imaging Techniques

NASA Astrophysics Data System (ADS)

Ferro-Famil, L.; El Hajj Chehade, B.; Ho Tong Minh, D.; Tebaldini, S.; LE Toan, T.

2016-12-01

Developing and improving methods to monitor forest biomass in space and time is a timely challenge, especially for tropical forests, for which SAR imaging at larger wavelength presents an interesting potential. Nevertheless, directly estimating tropical forest biomass from classical 2-D SAR images may reveal a very complex and ill-conditioned problem, since a SAR echo is composed of numerous contributions, whose features and importance depend on many geophysical parameters, such has ground humidity, roughness, topography… that are not related to biomass. Recent studies showed that SAR modes of diversity, i.e. polarimetric intensity ratios or interferometric phase centers, do not fully resolve this under-determined problem, whereas Pol-InSAR tree height estimates may be related to biomass through allometric relationships, with, in general over tropical forests, significant levels of uncertainty and lack of robustness. In this context, 3-D imaging using SAR tomography represents an appealing solution at larger wavelengths, for which wave penetration properties ensures a high quality mapping of a tropical forest reflectivity in the vertical direction. This paper presents a series of studies led, in the frame of the preparation of the next ESA mission BIOMASS, on the estimation of biomass over a tropical forest in French Guiana, using Polarimetric SAR Tomographic (Pol-TomSAR) data acquired at P band by ONERA. It is then shown that Pol-TomoSAR significantly improves the retrieval of forest above ground biomass (AGB) in a high biomass forest (200 up to 500 t/ha), with an error of only 10% at 1.5-ha resolution using a reflectivity estimates sampled at a predetermined elevation. The robustness of this technique is tested by applying the same approach over another site, and results show a similar relationship between AGB and tomographic reflectivity over both sites. The excellent ability of Pol-TomSAR to retrieve both canopy top heights and ground topography with an error of the order of 2m compared to LiDAR estimates, is then used to generalize this tomographic technique by selecting in an adaptive way the height at which reflectivity is estimated. Results indicate that this generalized techniques reduces the estimation error to values inferior to 10% and improve the representativity of the obtained AGB maps.

Research on Inversion Models for Forest Height Estimation Using Polarimetric SAR Interferometry

NASA Astrophysics Data System (ADS)

Zhang, L.; Duan, B.; Zou, B.

2017-09-01

The forest height is an important forest resource information parameter and usually used in biomass estimation. Forest height extraction with PolInSAR is a hot research field of imaging SAR remote sensing. SAR interferometry is a well-established SAR technique to estimate the vertical location of the effective scattering center in each resolution cell through the phase difference in images acquired from spatially separated antennas. The manipulation of PolInSAR has applications ranging from climate monitoring to disaster detection especially when used in forest area, is of particular interest because it is quite sensitive to the location and vertical distribution of vegetation structure components. However, some of the existing methods can't estimate forest height accurately. Here we introduce several available inversion models and compare the precision of some classical inversion approaches using simulated data. By comparing the advantages and disadvantages of these inversion methods, researchers can find better solutions conveniently based on these inversion methods.

Operational shoreline mapping with high spatial resolution radar and geographic processing

USGS Publications Warehouse

Rangoonwala, Amina; Jones, Cathleen E; Chi, Zhaohui; Ramsey, Elijah W.

2017-01-01

A comprehensive mapping technology was developed utilizing standard image processing and available GIS procedures to automate shoreline identification and mapping from 2 m synthetic aperture radar (SAR) HH amplitude data. The development used four NASA Uninhabited Aerial Vehicle SAR (UAVSAR) data collections between summer 2009 and 2012 and a fall 2012 collection of wetlands dominantly fronted by vegetated shorelines along the Mississippi River Delta that are beset by severe storms, toxic releases, and relative sea-level rise. In comparison to shorelines interpreted from 0.3 m and 1 m orthophotography, the automated GIS 10 m alongshore sampling found SAR shoreline mapping accuracy to be ±2 m, well within the lower range of reported shoreline mapping accuracies. The high comparability was obtained even though water levels differed between the SAR and photography image pairs and included all shorelines regardless of complexity. The SAR mapping technology is highly repeatable and extendable to other SAR instruments with similar operational functionality.

Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

NASA Technical Reports Server (NTRS)

Freeman, A.; Kramer, C.; Alves, M.; Chapman, B.

1994-01-01

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

SAR image change detection using watershed and spectral clustering

NASA Astrophysics Data System (ADS)

Niu, Ruican; Jiao, L. C.; Wang, Guiting; Feng, Jie

2011-12-01

A new method of change detection in SAR images based on spectral clustering is presented in this paper. Spectral clustering is employed to extract change information from a pair images acquired on the same geographical area at different time. Watershed transform is applied to initially segment the big image into non-overlapped local regions, leading to reduce the complexity. Experiments results and system analysis confirm the effectiveness of the proposed algorithm.

On the appropriate feature for general SAR image registration

NASA Astrophysics Data System (ADS)

Li, Dong; Zhang, Yunhua

2012-09-01

An investigation to the appropriate feature for SAR image registration is conducted. The commonly-used features such as tie points, Harris corner, the scale invariant feature transform (SIFT), and the speeded up robust feature (SURF) are comprehensively evaluated in terms of several criteria such as the geometrical invariance of feature, the extraction speed, the localization accuracy, the geometrical invariance of descriptor, the matching speed, the robustness to decorrelation, and the flexibility to image speckling. It is shown that SURF outperforms others. It is particularly indicated that SURF has good flexibility to image speckling because the Fast-Hessian detector of SURF has a potential relation with the refined Lee filter. It is recommended to perform SURF on the oversampled image with unaltered sampling step so as to improve the subpixel registration accuracy and speckle immunity. Thus SURF is more appropriate and competent for general SAR image registration.

Analysis of Wind and Sea State in SAR data of Hurricanes

NASA Astrophysics Data System (ADS)

Hoja, D.; Schulz-Stellenfleth, J.; Lehner, S.; Horstmann, J.

2003-04-01

Spaceborne synthetic aperture radar (SAR) is still the only instrument providing directional ocean wave and in addition surface wind information on a global and continuous basis. Operating in ASAR wave mode ENVISAT, launched in 2002, provides 10 km x 5 km SAR images every 100 km along the orbit. These SAR data continue and expand the SAR era of the European Remote Sensing satellites ERS-1 and ERS-2, which have acquired similar SAR data since 1991 on a global basis. To not only use the official ERS SAR wave mode product, which consists only of the SAR image power spectrum, but also the full SAR image information a subset of 27 days globally distributed ERS-2 SAR raw data were processed to single look complex SAR imagettes using the BSAR processor developed at the German Aerospace Center. These data have the same format as the official ESA product for ENVISAT ASAR wave mode data. This subset of 34,000 ERS-2 SAR imagettes was used to develop and validate algorithms for wind and wave retrieval, which are also applicable to ENVISAT ASAR wave mode data. The time frame of the dataset covers several tropical cyclones in the Atlantic Ocean of which hurricane Fran has been investigated in detail together with additional data available from scatterometers, buoys and weather centers. Hurricane Fran was active from August 23 to September 8, 1996. During this time, hurricane Fran developed near the African coast and progressed over the North Atlantic Ocean. Landfall occurred on September 5, 1996 at the coast of North Carolina, USA. Fran was part of a whole series of tropical cyclones travelling about the same course in a short time. The wind is extracted from SAR imagery and compared to results of the numerical model output provided by the European Center for Medium-Range Weather Forecast (ECMWF) and co-located ERS-2 scatterometer measurements. The Swell and wind sea systems generated by the tropical cyclones are measured using SAR cross spectra and a newly developed partitioning technique. For each component wave system (partition) spectral parameters like wavelength and wave propagation direction are calculated and compared to numerical model output provided by ECMWF. The progression of the tropical cyclones is presented and it is described, how the hurricanes are portrayed in the SAR data. The response of waves to fast turning winds is analyzed. Conclusions are drawn about the wave model forecast in hurricane situations using satellite wave mode data. Keywords: Hurricanes, SAR, ocean winds, ocean waves, wind sea and swell

Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula.

PubMed

Mera, David; Cotos, José M; Varela-Pet, José; Garcia-Pineda, Oscar

2012-10-01

Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the ocean's surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time. Copyright © 2012 Elsevier Ltd. All rights reserved.

Sea Surface Wakes Observed by Spaceborne SAR in the Offshore Wind Farms

NASA Astrophysics Data System (ADS)

Li, Xiaoming; Lehner, Susanne; Jacobsen, Sven

2014-11-01

In the paper, we present some X-band spaceborne synthetic aperture radar (SAR) TerraSAR-X (TS-X) images acquired at the offshore wind farms in the North Sea and the East China Sea. The high spatial resolution SAR images show different sea surface wake patterns downstream of the offshore wind turbines. The analysis suggests that there are major two types of wakes among the observed cases. The wind turbine wakes generated by movement of wind around wind turbines are the most often observed cases. In contrast, due to the strong local tidal currents in the near shore wind farm sites, the tidal current wakes induced by tidal current impinging on the wind turbine piles are also observed in the high spatial resolution TS-X images. The discrimination of the two types of wakes observed in the offshore wind farms is also described in the paper.

Wave attenuation in the marginal ice zone during LIMEX

NASA Technical Reports Server (NTRS)

Liu, Antony K.; Vachon, Paris W.; Peng, Chih Y.; Bhogal, A. S.

1992-01-01

The effect of ice cover on ocean-wave attenuation is investigated for waves under flexure in the marginal ice zone (MIZ) with SAR image spectra and the results of models. Directional wavenumber spectra are taken from the SAR image data, and the wave-attenuation rate is evaluated with SAR image spectra and by means of the model by Liu and Mollo-Christensen (1988). Eddy viscosity is described by means of dimensional analysis as a function of ice roughness and wave-induced velocity, and comparisons are made with the remotely sensed data. The model corrects the open-water model by introducing the effects of a continuous ice sheet, and turbulent eddy viscosity is shown to depend on ice thickness, floe sizes, significant wave height, and wave period. SAR and wave-buoy data support the trends described in the model results, and a characteristic rollover is noted in the model and experimental wave-attenuation rates at high wavenumbers.

Robust flood area detection using a L-band synthetic aperture radar: Preliminary application for Florida, the U.S. affected by Hurricane Irma

NASA Astrophysics Data System (ADS)

Nagai, H.; Ohki, M.; Abe, T.

2017-12-01

Urgent crisis response for a hurricane-induced flood needs urgent providing of a flood map covering a broad region. However, there is no standard threshold values for automatic flood identification from pre-and-post images obtained by satellite-based synthetic aperture radars (SARs). This problem could hamper prompt data providing for operational uses. Furthermore, one pre-flood SAR image does not always represent potential water surfaces and river flows especially in tropical flat lands which are greatly influenced by seasonal precipitation cycle. We are, therefore, developing a new method of flood mapping using PALSAR-2, an L-band SAR, which is less affected by temporal surface changes. Specifically, a mean-value image and a standard-deviation image are calculated from a series of pre-flood SAR images. It is combined with a post-flood SAR image to obtain normalized backscatter amplitude difference (NoBADi), with which a difference between a post-flood image and a mean-value image is divided by a standard-deviation image to emphasize anomalous water extents. Flooding areas are then automatically obtained from the NoBADi images as lower-value pixels avoiding potential water surfaces. We applied this method to PALSAR-2 images acquired on Sept. 8, 10, and 12, 2017, covering flooding areas in a central region of Dominican Republic and west Florida, the U.S. affected by Hurricane Irma. The output flooding outlines are validated with flooding areas manually delineated from high-resolution optical satellite images, resulting in higher consistency and less uncertainty than previous methods (i.e., a simple pre-and-post flood difference and pre-and-post coherence changes). The NoBADi method has a great potential to obtain a reliable flood map for future flood hazards, not hampered by cloud cover, seasonal surface changes, and "casual" thresholds in the flood identification process.

A new automatic synthetic aperture radar-based flood mapping application hosted on the European Space Agency's Grid Processing of Demand Fast Access to Imagery environment

NASA Astrophysics Data System (ADS)

Matgen, Patrick; Giustarini, Laura; Hostache, Renaud

2012-10-01

This paper introduces an automatic flood mapping application that is hosted on the Grid Processing on Demand (GPOD) Fast Access to Imagery (Faire) environment of the European Space Agency. The main objective of the online application is to deliver operationally flooded areas using both recent and historical acquisitions of SAR data. Having as a short-term target the flooding-related exploitation of data generated by the upcoming ESA SENTINEL-1 SAR mission, the flood mapping application consists of two building blocks: i) a set of query tools for selecting the "crisis image" and the optimal corresponding "reference image" from the G-POD archive and ii) an algorithm for extracting flooded areas via change detection using the previously selected "crisis image" and "reference image". Stakeholders in flood management and service providers are able to log onto the flood mapping application to get support for the retrieval, from the rolling archive, of the most appropriate reference image. Potential users will also be able to apply the implemented flood delineation algorithm. The latter combines histogram thresholding, region growing and change detection as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. Both algorithms are computationally efficient and operate with minimum data requirements. The case study of the high magnitude flooding event that occurred in July 2007 on the Severn River, UK, and that was observed with a moderateresolution SAR sensor as well as airborne photography highlights the performance of the proposed online application. The flood mapping application on G-POD can be used sporadically, i.e. whenever a major flood event occurs and there is a demand for SAR-based flood extent maps. In the long term, a potential extension of the application could consist in systematically extracting flooded areas from all SAR images acquired on a daily, weekly or monthly basis.

Tracking lava flow emplacement on the east rift zone of Kilauea, Hawai’i with synthetic aperture radar (SAR) coherence

USGS Publications Warehouse

Dietterich, Hannah R.; Poland, Michael P.; Schmidt, David; Cashman, Katharine V.; Sherrod, David R.; Espinosa, Arkin Tapia

2012-01-01

Lava flow mapping is both an essential component of volcano monitoring and a valuable tool for investigating lava flow behavior. Although maps are traditionally created through field surveys, remote sensing allows an extraordinary view of active lava flows while avoiding the difficulties of mapping on location. Synthetic aperture radar (SAR) imagery, in particular, can detect changes in a flow field by comparing two images collected at different times with SAR coherence. New lava flows radically alter the scattering properties of the surface, making the radar signal decorrelated in SAR coherence images. We describe a new technique, SAR Coherence Mapping (SCM), to map lava flows automatically from coherence images independent of look angle or satellite path. We use this approach to map lava flow emplacement during the Pu‘u ‘Ō‘ō-Kupaianaha eruption at Kīlauea, Hawai‘i. The resulting flow maps correspond well with field mapping and better resolve the internal structure of surface flows, as well as the locations of active flow paths. However, the SCM technique is only moderately successful at mapping flows that enter vegetation, which is also often decorrelated between successive SAR images. Along with measurements of planform morphology, we are able to show that the length of time a flow stays decorrelated after initial emplacement is linearly related to the flow thickness. Finally, we use interferograms obtained after flow surfaces become correlated to show that persistent decorrelation is caused by post-emplacement flow subsidence.

The Advanced Rapid Imaging and Analysis (ARIA) Project's Response to the April 25, 2015 M7.8 Nepal Earthquake: Rapid Measurements and Models for Science and Situational Awareness

NASA Astrophysics Data System (ADS)

Owen, S. E.; Fielding, E. J.; Yun, S. H.; Yue, H.; Polet, J.; Riel, B. V.; Liang, C.; Huang, M. H.; Webb, F.; Simons, M.; Moore, A. W.; Agram, P. S.; Barnhart, W. D.; Hua, H.; Liu, Z.; Milillo, P.; Sacco, G. F.; Rosen, P. A.; Manipon, G.

2015-12-01

On April 25, 2015, the M7.8 Gorkha earthquake struck Nepal and the city of Kathmandu. The quake caused a significant humanitarian crisis and killed more than 8,000 due to widespread building damage and triggered landslides throughout the region. This was the strongest earthquake to occur in the region since the 1934 Nepal-Bihar magnitude 8.0 quake caused more than 10,000 fatalities. In the days following the earthquake, the JPL/Caltech ARIA project produced coseismic GPS and SAR displacements, fault slip models, and damage assessments from SAR coherence change that were helpful in both understanding the event and in the response efforts. The ARIA project produced InSAR observations from two new SAR missions - JAXA's ALOS-2 and ESA's Sentinel 1a. The GPS coseismic displacements showed ~1.8 meters of southward motion and ~1.3 meters of uplift in Kathmandu. InSAR images of the displacement field and fault models show that the rupture extended 135 km southeast of the epicenter. The SAR imagery also confirmed that the fault slip did not extend to the surface, though localized offsets formed due to liquefaction. The GPS and SAR analysis has continued to image the large M7.3 aftershock and postseismic deformation. The damage assessments from coherence change were used by several organizations guiding the response effort, including the NGA, the World Bank, and OFDA/USAID. We will present imaging, modeling, and damage assessment results from the recent April 25, 2015 M7.8 earthquake in Nepal, and its largest aftershock, a M7.3 event on May 12, 2015. We also discuss how these data were used for understanding the event, guiding the response, and for educational outreach.

Study on Landslide Disaster Extraction Method Based on Spaceborne SAR Remote Sensing Images - Take Alos Palsar for AN Example

NASA Astrophysics Data System (ADS)

Xue, D.; Yu, X.; Jia, S.; Chen, F.; Li, X.

2018-04-01

In this paper, sequence ALOS PALSAR data and airborne SAR data of L-band from June 5, 2008 to September 8, 2015 are used. Based on the research of SAR data preprocessing and core algorithms, such as geocode, registration, filtering, unwrapping and baseline estimation, the improved Goldstein filtering algorithm and the branch-cut path tracking algorithm are used to unwrap the phase. The DEM and surface deformation information of the experimental area were extracted. Combining SAR-specific geometry and differential interferometry, on the basis of composite analysis of multi-source images, a method of detecting landslide disaster combining coherence of SAR image is developed, which makes up for the deficiency of single SAR and optical remote sensing acquisition ability. Especially in bad weather and abnormal climate areas, the speed of disaster emergency and the accuracy of extraction are improved. It is found that the deformation in this area is greatly affected by faults, and there is a tendency of uplift in the southeast plain and western mountainous area, while in the southwest part of the mountain area there is a tendency to sink. This research result provides a basis for decision-making for local disaster prevention and control.

Extreme Magnetosphere-Ionosphere Coupling at the Plasmapause: a - In-A Bright SAR Arc

NASA Astrophysics Data System (ADS)

Baumgardner, J.; Wroten, J.; Semeter, J.; Mendillo, M.; Kozyra, J.

2007-05-01

Heat conduction from the ring current - plasmapause interaction region generates high electron temperature within the ionosphere that drive stable auroral red (SAR) arc emission at 6300 A. On the night of 29 October 1991, a SAR arc was observed using an all-sky imager and meridional imaging spectrograph at Millstone Hill. At xxxx UT, the SAR arc was south of Millstone at approximate L = 2 and reached emission levels of 13,000 rayleighs (R). Over two solar cycle of imaging observations have been made at Millstone Hill, and SAR arc brightness levels (excluding this event) averaged ~ 500 R. Simultaneous observations using the incoherent scatter radar (ISR), a DMSP satellite pass, the MSIS neutral atmosphere and SAR arc modeling using the Rees and Roble formalism succeeded in simulations of the observed emission. The reason for the unusual brightness was not the extreme temperatures achieved (and therefore heat conduction input), but the fact that the end of the plasmapause field line where the elevated Te values were measured did not occur in the ionospheric trough, but equatorward of it, thereby having far more ambient electrons to heat and subsequently collide with atomic oxygen. This unusual spatial geometry probably resulted from unusual convection patterns early in a superstorm scenario.

SAR System for UAV Operation with Motion Error Compensation beyond the Resolution Cell

PubMed Central

González-Partida, José-Tomás; Almorox-González, Pablo; Burgos-García, Mateo; Dorta-Naranjo, Blas-Pablo

2008-01-01

This paper presents an experimental Synthetic Aperture Radar (SAR) system that is under development in the Universidad Politécnica de Madrid. The system uses Linear Frequency Modulated Continuous Wave (LFM-CW) radar with a two antenna configuration for transmission and reception. The radar operates in the millimeter-wave band with a maximum transmitted bandwidth of 2 GHz. The proposed system is being developed for Unmanned Aerial Vehicle (UAV) operation. Motion errors in UAV operation can be critical. Therefore, this paper proposes a method for focusing SAR images with movement errors larger than the resolution cell. Typically, this problem is solved using two processing steps: first, coarse motion compensation based on the information provided by an Inertial Measuring Unit (IMU); and second, fine motion compensation for the residual errors within the resolution cell based on the received raw data. The proposed technique tries to focus the image without using data of an IMU. The method is based on a combination of the well known Phase Gradient Autofocus (PGA) for SAR imagery and typical algorithms for translational motion compensation on Inverse SAR (ISAR). This paper shows the first real experiments for obtaining high resolution SAR images using a car as a mobile platform for our radar. PMID:27879884

SAR System for UAV Operation with Motion Error Compensation beyond the Resolution Cell.

PubMed

González-Partida, José-Tomás; Almorox-González, Pablo; Burgos-Garcia, Mateo; Dorta-Naranjo, Blas-Pablo

2008-05-23

This paper presents an experimental Synthetic Aperture Radar (SAR) system that is under development in the Universidad Politécnica de Madrid. The system uses Linear Frequency Modulated Continuous Wave (LFM-CW) radar with a two antenna configuration for transmission and reception. The radar operates in the millimeter-wave band with a maximum transmitted bandwidth of 2 GHz. The proposed system is being developed for Unmanned Aerial Vehicle (UAV) operation. Motion errors in UAV operation can be critical. Therefore, this paper proposes a method for focusing SAR images with movement errors larger than the resolution cell. Typically, this problem is solved using two processing steps: first, coarse motion compensation based on the information provided by an Inertial Measuring Unit (IMU); and second, fine motion compensation for the residual errors within the resolution cell based on the received raw data. The proposed technique tries to focus the image without using data of an IMU. The method is based on a combination of the well known Phase Gradient Autofocus (PGA) for SAR imagery and typical algorithms for translational motion compensation on Inverse SAR (ISAR). This paper shows the first real experiments for obtaining high resolution SAR images using a car as a mobile platform for our radar.

SAR Image Change Detection Based on Fuzzy Markov Random Field Model

NASA Astrophysics Data System (ADS)

Zhao, J.; Huang, G.; Zhao, Z.

2018-04-01

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Investigation of Joint Visibility Between SAR and Optical Images of Urban Environments

NASA Astrophysics Data System (ADS)

Hughes, L. H.; Auer, S.; Schmitt, M.

2018-05-01

In this paper, we present a work-flow to investigate the joint visibility between very-high-resolution SAR and optical images of urban scenes. For this task, we extend the simulation framework SimGeoI to enable a simulation of individual pixels rather than complete images. Using the extended SimGeoI simulator, we carry out a case study using a TerraSAR-X staring spotlight image and a Worldview-2 panchromatic image acquired over the city of Munich, Germany. The results of this study indicate that about 55 % of the scene are visible in both images and are thus suitable for matching and data fusion endeavours, while about 25 % of the scene are affected by either radar shadow or optical occlusion. Taking the image acquisition parameters into account, our findings can provide support regarding the definition of upper bounds for image fusion tasks, as well as help to improve acquisition planning with respect to different application goals.

Analysis of the fractal dimension of volcano geomorphology through Synthetic Aperture Radar (SAR) amplitude images acquired in C and X band.

NASA Astrophysics Data System (ADS)

Pepe, S.; Di Martino, G.; Iodice, A.; Manzo, M.; Pepe, A.; Riccio, D.; Ruello, G.; Sansosti, E.; Tizzani, P.; Zinno, I.

2012-04-01

In the last two decades several aspects relevant to volcanic activity have been analyzed in terms of fractal parameters that effectively describe natural objects geometry. More specifically, these researches have been aimed at the identification of (1) the power laws that governed the magma fragmentation processes, (2) the energy of explosive eruptions, and (3) the distribution of the associated earthquakes. In this paper, the study of volcano morphology via satellite images is dealt with; in particular, we use the complete forward model developed by some of the authors (Di Martino et al., 2012) that links the stochastic characterization of amplitude Synthetic Aperture Radar (SAR) images to the fractal dimension of the imaged surfaces, modelled via fractional Brownian motion (fBm) processes. Based on the inversion of such a model, a SAR image post-processing has been implemented (Di Martino et al., 2010), that allows retrieving the fractal dimension of the observed surfaces, dictating the distribution of the roughness over different spatial scales. The fractal dimension of volcanic structures has been related to the specific nature of materials and to the effects of active geodynamic processes. Hence, the possibility to estimate the fractal dimension from a single amplitude-only SAR image is of fundamental importance for the characterization of volcano structures and, moreover, can be very helpful for monitoring and crisis management activities in case of eruptions and other similar natural hazards. The implemented SAR image processing performs the extraction of the point-by-point fractal dimension of the scene observed by the sensor, providing - as an output product - the map of the fractal dimension of the area of interest. In this work, such an analysis is performed on Cosmo-SkyMed, ERS-1/2 and ENVISAT images relevant to active stratovolcanoes in different geodynamic contexts, such as Mt. Somma-Vesuvio, Mt. Etna, Vulcano and Stromboli in Southern Italy, Shinmoe in Japan, Merapi in Indonesia. Preliminary results reveal that the fractal dimension of natural areas, being related only to the roughness of the observed surface, is very stable as the radar illumination geometry, the resolution and the wavelength change, thus holding a very unique property in SAR data inversion. Such a behavior is not verified in case of non-natural objects. As a matter of fact, when the fractal estimation is performed in the presence of either man-made objects or SAR image features depending on geometrical distortions due to the SAR system acquisition (i.e. layover, shadowing), fractal dimension (D) values outside the range of fractality of natural surfaces (2 < D < 3) are retrieved. These non-fractal characteristics show to be heavily dependent on sensor acquisition parameters (e.g. view angle, resolution). In this work, the behaviour of the maps generated starting from the C- and X- band SAR data, relevant to all the considered volcanoes, is analyzed: the distribution of the obtained fractal dimension values is investigated on different zones of the maps. In particular, it is verified that the fore-slope and back-slope areas of the image share a very similar fractal dimension distribution that is placed around the mean value of D=2.3. We conclude that, in this context, the fractal dimension could be considered as a signature of the identification of the volcano growth as a natural process. The COSMO-SkyMed data used in this study have been processed at IREA-CNR within the SAR4Volcanoes project under Italian Space Agency agreement n. I/034/11/0.

A method to calibrate channel friction and bathymetry parameters of a Sub-Grid hydraulic model using SAR flood images

NASA Astrophysics Data System (ADS)

Wood, M.; Neal, J. C.; Hostache, R.; Corato, G.; Chini, M.; Giustarini, L.; Matgen, P.; Wagener, T.; Bates, P. D.

2015-12-01

Synthetic Aperture Radar (SAR) satellites are capable of all-weather day and night observations that can discriminate between land and smooth open water surfaces over large scales. Because of this there has been much interest in the use of SAR satellite data to improve our understanding of water processes, in particular for fluvial flood inundation mechanisms. Past studies prove that integrating SAR derived data with hydraulic models can improve simulations of flooding. However while much of this work focusses on improving model channel roughness values or inflows in ungauged catchments, improvement of model bathymetry is often overlooked. The provision of good bathymetric data is critical to the performance of hydraulic models but there are only a small number of ways to obtain bathymetry information where no direct measurements exist. Spatially distributed river depths are also rarely available. We present a methodology for calibration of model average channel depth and roughness parameters concurrently using SAR images of flood extent and a Sub-Grid model utilising hydraulic geometry concepts. The methodology uses real data from the European Space Agency's archive of ENVISAT[1] Wide Swath Mode images of the River Severn between Worcester and Tewkesbury during flood peaks between 2007 and 2010. Historic ENVISAT WSM images are currently free and easy to access from archive but the methodology can be applied with any available SAR data. The approach makes use of the SAR image processing algorithm of Giustarini[2] et al. (2013) to generate binary flood maps. A unique feature of the calibration methodology is to also use parameter 'identifiability' to locate the parameters with higher accuracy from a pre-assigned range (adopting the DYNIA method proposed by Wagener[3] et al., 2003). [1] https://gpod.eo.esa.int/services/ [2] Giustarini. 2013. 'A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X'. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4. [3] Wagener. 2003. 'Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis'. Hydrol. Process. 17, 455-476.

Evaluation of the Potentials and Challenges of an Airborne InSAR System for Deformation Mapping: A Case Study over the Slumgullion Landslide

NASA Astrophysics Data System (ADS)

Cao, N.; Lee, H.; Zaugg, E.; Shrestha, R. L.; Carter, W. E.; Glennie, C. L.; Wang, G.; Lu, Z.; Diaz, J. C. F.

2016-12-01

Synthetic aperture radar (SAR) interferometry (InSAR) is a technique which uses two or more SAR images of the same area to estimate landscape topography or ground surface displacement. Differential InSAR (DInSAR) is capable of measuring ground displacements at the millimeter level, but a major drawback of traditional DInSAR is that only the deformation along the line-of-sight direction can be detected. Because most of the current spaceborne SAR systems have near-polar, sun-synchronous orbits, deformation measurements in the South-North direction are limited (except for polar regions). Compared with spaceborne SAR, airborne SAR systems have the advantages of flexible scanning geometry and revisit time, high spatial resolution, and no ionospheric distortion. In this study, we present a case study of the Slumgullion landslide conducted in July 2015 to assess an airborne SAR system known as ARTEMIS SlimSAR, which is a compact, modular, and multi-frequency radar system. The Slumgullion landslide, located in the San Juan Mountains near Lake City, Colorado is a long-term slow moving landslide that moves downhill continuously. For this study, the L-band SlimSAR was installed and data were collected on July 3, 7, and 10 and processed using the time-domain backprojection algorithm. GPS surveys and spaceborne DInSAR analysis using COSMO-SkyMed images were also conducted to verify the performance of the airborne SAR system. The airborne DInSAR results showed satisfying agreement with the GPS and spaceborne DInSAR results. The root mean square of the differences between the SlimSAR, and GPS and satellite derived velocities, were 0.6 mm/day, and 0.9 mm/day, respectively. A 3-D deformation map over Slumgullion landslide was generated, which displayed distinct correlation between the landslide motion and topography. This study also indicated that the primary source of the error for the SlimSAR system is the trajectory turbulences of the aircraft. The effect of the trajectory turbulences is analyzed and several possible solutions are proposed to improve the airborne SAR performance. In the long run, an improved airborne SAR system will open avenues for differential interferometry to be used in scientific studies and commercial applications previously prohibited by orbital constraints of spaceborne SAR.

Airborne Multi-Band SAR in the Arctic

NASA Astrophysics Data System (ADS)

Gardner, J. M.; Brozena, J. M.; Liang, R.; Ball, D.; Holt, B.; Thomson, J.

2016-12-01

As one component of the Office of Naval Research supported Sea State Departmental Research Initiative during October of 2015 the Naval Research Laboratory flew an ultrawide-band, low-frequency, polarimetric SAR over the southward advancing sea ice in Beaufort Sea. The flights were coordinated with the research team aboard the R/V Sikuliaq working near and in the advancing pack ice. The majority of the SAR data were collected with the L-Band sensor (1000-1500 MHz) from an altitude of 10,000', providing a useful swath 6 km wide with 75o and 25 o angles of incidence at the inner and outer edge of the swath respectively. Some data were also collected with the P-Band SAR (215-915 MHz). The extremely large bandwidths allowed for formation of image pixels as small as 30 cm, however, we selected 60 cm pixel size to reduce image speckle. The separate polarimetric images are calibrated to one pixel to allow for calculations such as polarimetric decompositions that require the images to be well aligned. Both frequencies are useful particularly for the detection of ridges and areas of deformed ice. There are advantages and disadvantages to airborne SAR imagery compared to satellites. The chief advantages being the enormous allowable bandwidth leading to very fine range resolution, and the ability to fly arbitrary trajectories on demand. The latter permits specific areas to be imaged at a given time with a specified illumination direction. An area can even be illuminated from all directions by flying a circular trajectory around the target area. This captures ice features that are sensitive to illumination direction such as cracks, sastrugi orientation, and ridges. The disadvantages include variation of intensity across the swath with range and incidence angle. In addition to the SAR data, we collected photogrammetric imagery from a DSS-439, scanning lidar from a Riegl Q560 and surface brightness temperatures from a KT-19. However, since all of these sensors are nadir pointing, and some restricted to relatively low-altitude, it was difficult to obtain data co-registered with the SAR. At this meeting we will present some initial results from the SAR imagery, including differentiation of young, thin, and older ice features, and comparisons with satellite SAR with L-band and C-band frequencies.

Shearlet-based edge detection: flame fronts and tidal flats

NASA Astrophysics Data System (ADS)

King, Emily J.; Reisenhofer, Rafael; Kiefer, Johannes; Lim, Wang-Q.; Li, Zhen; Heygster, Georg

2015-09-01

Shearlets are wavelet-like systems which are better suited for handling geometric features in multi-dimensional data than traditional wavelets. A novel method for edge and line detection which is in the spirit of phase congruency but is based on a complex shearlet transform will be presented. This approach to detection yields an approximate tangent direction of detected discontinuities as a byproduct of the computation, which then yields local curvature estimates. Two applications of the edge detection method will be discussed. First, the tracking and classification of flame fronts is a critical component of research in technical thermodynamics. Quite often, the flame fronts are transient or weak and the images are noisy. The standard methods used in the field for the detection of flame fronts do not handle such data well. Fortunately, using the shearlet-based edge measure yields good results as well as an accurate approximation of local curvature. Furthermore, a modification of the method will yield line detection, which is important for certain imaging modalities. Second, the Wadden tidal flats are a biodiverse region along the North Sea coast. One approach to surveying the delicate region and tracking the topographical changes is to use pre-existing Synthetic Aperture Radar (SAR) images. Unfortunately, SAR data suffers from multiplicative noise as well as sensitivity to environmental factors. The first large-scale mapping project of that type showed good results but only with a tremendous amount of manual interaction because there are many edges in the data which are not boundaries of the tidal flats but are edges of features like fields or islands. Preliminary results will be presented.

Ocean Classification of Dynamical Structures Detected by SAR and Spectral Methods

NASA Astrophysics Data System (ADS)

Redondo, J. M.; Martinez-Benjamin, J. J.; Tellez, J. D.; Jorge, J.; Diez, M.; Sekula, E.

2016-08-01

We discuss a taxonomy of different dynamical features in the ocean surface and provide some eddy and front statistics, as well as describing some events detected by several satellites and even with additional cruise observations and measurements, in the North-west Mediterranean Sea area between 1996 and 2012. The structure of the flows are presented using self-similar traces that may be used to parametrize mixing at both limits of the Rossby Deformation Radius scale, RL. Results show the ability to identify different SAR signatures and at the same time provide calibrations for the different local configurations of vortices, spirals, Langmuir cells, oil spills and tensioactive slicks that eventually allow the study of the self-similar structure of the turbulence. Depending on the surface wind and wave level, and also on the fetch. the bathimetry, the spiral parameters and the resolution of vortical features change. Previous descriptions did not include the new wind and buoyancy features. SAR images also show the turbulence structure of the coastal area and the Regions of Fresh Water Influence (ROFI). It is noteworthy tt such complex coastal field-dependent behavior is strongly influenced by stratification and rotation of the turbulence spectrum is observed only in the range smaller than the local Rossby deformation radius, RL. The measures of diffusivity from buoy or tracer experiments are used to calibrate the behavior of different tracers and pollutants, both natural and man-made in the NW Mediterranean Sea. Thanks to different polarization and intensity levels in ASAR satellite imagery, these can be used to distinguish between natural and man-made sea surface features due to their distinct self-similar and fractal as a function of spill and slick parameters, environmental conditions and history of both oil releases and weather conditions. Eddy diffusivity map derived from SAR measurements of the ocean surface, performing a feature spatial correlation of the available images of the region are presented. Both the multi fractal discrimination of the local features and the diffusivity measurements are important to evaluate the state of the environment. The distribution of meso-scale vortices of size, the Rossby deformation scale and other dominant features can be used to distinguish features in the ocean surface. Multi-fractal analysis is then very usefull. The SAR images exhibited a large variation of natural features produced by winds, internal waves, the bathymetric distribution, by convection, rain, etc as all of these produce variations in the sea surface roughness so that the topological changes may be studied and classified. In a similar way bathimetry may be studied with the methodology described here using the coastline and the thalwegs as generators of local vertical vorticity.

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Ozkan, C.; Sunar, F.

2013-10-01

After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

Frequency Diversity for Improving Synthetic Aperture Radar Imaging

DTIC Science & Technology

2009-03-01

for broadside spotlight SAR imaging is shown to be δθ = λ 4Yo . (2.34) When θ is small, as is often the case in spotlight SAR imaging, the required...maximum distance ∆y between samples along the y-axis is shown to be ∆y ≤ λRc 4Yo . (2.35) With platform velocity vy along the y-axis, the minimum PRF is

The use of multifrequency and polarimetric SIR-C/X-SAR data in geologic studies of Bir Safsaf, Egypt

USGS Publications Warehouse

Schaber, G.G.; McCauley, J.F.; Breed, C.S.

1997-01-01

Bir Safsaf, within the hyperarid 'core' of the Sahara in the Western Desert of Egypt, was recognized following the SIR-A and SIR-B missions in the 1980s as one of the key localities in northeast Africa, where penetration of dry sand by radar signals delineates previously unknown, sand-buried paleodrainage valleys ('radar-rivers') of middle Tertiary to Quaternary age. The Bir Safsaf area was targeted as a focal point for further research in sand penetration and geologic mapping using the multifrequency and polarimetric SIR-C/X-SAR sensors. Analysis of the SIR-C/X-SAR data from Bir Safsaf provides important new information on the roles of multiple SAR frequency and polarimetry in portraying specific types of geologic units, materials, and structures mostly hidden from view on the ground and on Landsat TM images by a relatively thin, but extensive blanket of blow sand. Basement rock units (granitoids and gneisses) and the fractures associated with them at Bir Safsaf are shown here for the first time to be clearly delineated using C- and L-band SAR images. The detectability of most geologic features is dependent primarily on radar frequency, as shown for wind erosion patterns in bedrock at X-band (3 cm wavelength), and for geologic units and sand and clay-filled fractures in weathered crystal-line basement rocks at C-band (6 cm) and L-band (24 cm). By contrast, Quaternary paleodrainage channels are detectable at all three radar frequencies owing, among other things, to an usually thin cover of blow sand. The SIR-C/X-SAR data investigated to date enable us to make specific recommendations about the utility of certain radar sensor configurations for geologic and paleoenvironmental reconnaissance in desert regions.Analysis of the shuttle imaging radar-C/X-synthetic aperture radar (SIR-C/X-SAR) data from Bir Safsaf provides important new information on the roles of multiple SAR frequency and polarimetry in portraying specific types of geologic units, materials, and structures mostly hidden from view on the ground and on Landsat images by a relatively thin, but extensive blanket of blow sand. Basement rock units and associated fractures at the Bir Safsaf are clearly delineated using C- and L-band SAR images. The detectability of most geologic features depend primarily on radar frequency. The SIR-C/X-SAR data also provide recommendations about the utility of certain radar configurations for geologic and paleoenvironmental reconnaissance in deserts.

Multitemporal Observations of Sugarcane by TerraSAR-X Images

PubMed Central

Baghdadi, Nicolas; Cresson, Rémi; Todoroff, Pierre; Moinet, Soizic

2010-01-01

The objective of this study is to investigate the potential of TerraSAR-X (X-band) in monitoring sugarcane growth on Reunion Island (located in the Indian Ocean). Multi-temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV) were analyzed in order to study the behaviour of SAR (synthetic aperture radar) signal as a function of sugarcane height and NDVI (Normalized Difference Vegetation Index). The potential of TerraSAR for mapping the sugarcane harvest was also studied. Radar signal increased quickly with crop height until a threshold height, which depended on polarization and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is slightly higher with cross polarization and higher incidence angles (47° in comparison with 17° and 31°). Results also showed that the co-polarizations channels (HH and VV) were well correlated. High correlation between SAR signal and NDVI calculated from SPOT-4/5 images was observed. TerraSAR data showed that after strong rains the soil contribution to the backscattering of sugarcane fields can be important for canes with heights of terminal visible dewlap (htvd) less than 50 cm (total cane heights around 155 cm). This increase in radar signal after strong rains could involve an ambiguity between young and mature canes. Indeed, the radar signal on TerraSAR images acquired in wet soil conditions could be of the same order for fields recently harvested and mature sugarcane fields, making difficult the detection of cuts. Finally, TerraSAR data at high spatial resolution were shown to be useful for monitoring sugarcane harvest when the fields are of small size or when the cut is spread out in time. The comparison between incidence angles of 17°, 37° and 58° shows that 37° is more suitable to monitor the sugarcane harvest. The cut is easily detectable on TerraSAR images for data acquired less than two or three months after the cut. The radar signal decreases about 5dB for images acquired some days after the cut and 3 dB for data acquired two month after the cut (VV-37°). The difference in radar signal becomes negligible (<1 dB) between harvested fields and mature canes for sugarcane harvested since three months or more. PMID:22163387

Space Radar Image Isla Isabela in 3-D

NASA Technical Reports Server (NTRS)

1999-01-01

This is a three-dimensional view of Isabela, one of the Galapagos Islands located off the western coast of Ecuador, South America. This view was constructed by overlaying a Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) image on a digital elevation map produced by TOPSAR, a prototype airborne interferometric radar which produces simultaneous image and elevation data. The vertical scale in this image is exaggerated by a factor of 1.87. The SIR-C/X-SAR image was taken on the 40th orbit of space shuttle Endeavour. The image is centered at about 0.5 degree south latitude and 91 degrees west longitude and covers an area of 75 by 60 kilometers (47 by 37 miles). The radar incidence angle at the center of the image is about 20 degrees. The western Galapagos Islands, which lie about 1,200 kilometers (750 miles)west of Ecuador in the eastern Pacific, have six active volcanoes similar to the volcanoes found in Hawaii and reflect the volcanic processes that occur where the ocean floor is created. Since the time of Charles Darwin's visit to the area in 1835, there have been more than 60 recorded eruptions on these volcanoes. This SIR-C/X-SAR image of Alcedo and Sierra Negra volcanoes shows the rougher lava flows as bright features, while ash deposits and smooth pahoehoe lava flows appear dark. Vertical exaggeration of relief is a common tool scientists use to detect relationships between structure (for example, faults, and fractures) and topography. Spaceborne Imaging Radar-C and X-Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI).

Complex phase error and motion estimation in synthetic aperture radar imaging

NASA Astrophysics Data System (ADS)

Soumekh, M.; Yang, H.

1991-06-01

Attention is given to a SAR wave equation-based system model that accurately represents the interaction of the impinging radar signal with the target to be imaged. The model is used to estimate the complex phase error across the synthesized aperture from the measured corrupted SAR data by combining the two wave equation models governing the collected SAR data at two temporal frequencies of the radar signal. The SAR system model shows that the motion of an object in a static scene results in coupled Doppler shifts in both the temporal frequency domain and the spatial frequency domain of the synthetic aperture. The velocity of the moving object is estimated through these two Doppler shifts. It is shown that once the dynamic target's velocity is known, its reconstruction can be formulated via a squint-mode SAR geometry with parameters that depend upon the dynamic target's velocity.

Development of Oil Spill Monitoring System for the Black Sea, Caspian Sea and the Barents/Kara Seas (DEMOSS)

NASA Astrophysics Data System (ADS)

Sandven, Stein; Kudriavtsev, Vladimir; Malinovsky, Vladimir; Stanovoy, Vladimir

2008-01-01

DEMOSS will develop and demonstrate elements of a marine oil spill detection and prediction system based on satellite Synthetic Aperture Radar (SAR) and other space data. In addition, models for prediction of sea surface pollution drift will be developed and tested. The project implements field experiments to study the effect of artificial crude oil and oil derivatives films on short wind waves and multi-frequency (Ka-, Ku-, X-, and C-band) dual polarization radar backscatter power and Doppler shift at different wind and wave conditions. On the basis of these and other available experimental data, the present model of short wind waves and radar scattering will be improved and tested.A new approach for detection and quantification of the oil slicks/spills in satellite SAR images is developed that can discriminate human oil spills from biogenic slicks and look-alikes in the SAR images. New SAR images are obtained in coordination with the field experiments to test the detection algorithm. Satellite SAR images from archives as well as from new acquisitions will be analyzed for the Black/Caspian/Kara/Barents seas to investigate oil slicks/spills occurrence statistics.A model for oil spills/slicks transport and evolution is developed and tested in ice-infested arctic seas, including the Caspian Sea. Case studies using the model will be conducted to simulate drift and evolution of oil spill events observed in SAR images. The results of the project will be disseminated via scientific publications and by demonstration to users and agencies working with marine monitoring. The project lasts for two years (2007 - 2009) and is funded under INTAS Thematic Call with ESA 2006.

NASA Administrator Sean O'Keefe speaking at the AirSAR 2004 Mesoamerica hangar naming ceremony

NASA Image and Video Library

2004-03-03

NASA Administrator Sean O'Keefe speaking at the AirSAR 2004 Mesoamerica hangar naming ceremony. AirSAR 2004 Mesoamerica is a three-week expedition by an international team of scientists that will use an all-weather imaging tool, called the Airborne Synthetic Aperture Radar (AirSAR), in a mission ranging from the tropical rain forests of Central America to frigid Antarctica.

David Bushman at the Mission Manager's console onboard NASA's DC-8 during the AirSAR 2004 campaign

NASA Image and Video Library

2004-03-03

David Bushman at the Mission Manager's console onboard NASA's DC-8 during the AirSAR 2004 campaign. AirSAR 2004 is a three-week expedition by an international team of scientists that will use an all-weather imaging tool, called the Airborne Synthetic Aperture Radar (AirSAR), in a mission ranging from the tropical rain forests of Central America to frigid Antarctica.