Segmentation of SAR images

Lee, J.-S.; Jurkevich, Igor

doi:10.1109/36.35954

Cited by 108 publications

(35 citation statements)

References 13 publications

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“…Therefore, it is crucial to have efficient tools able to predict the SAR data behaviour as a function of the scene parameters. As a matter of fact, the use of a SAR simulator can provide value-added information for SAR data interpretation and a support for SAR processing techniques (e.g., image despeckling [Di Martino et al, 2012a, 2013a, segmentation [Lee and Jurkevich, 1989;Collins and Allan, 2009], change detection , sea target (and extended target) detection [Watts et al, 1990;Tello et al, 2007]). In this paper a SAR simulator able to provide images presenting the appropriate speckle statistics is introduced.…”

Section: Introductionmentioning

confidence: 99%

A Physical Approach for SAR Speckle Simulation: First Results

Martino

Iodice

Riccio

et al. 2013

European Journal of Remote Sensing

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In this paper a SAR simulator able to provide images presenting appropriate speckle statistics is introduced. The simulator is able to generate both Exponential (fully developed speckle) and K-distributed speckle statistics, according to surface and radar parameters. It is based on sound physical models for the evaluation of the number of equivalent scatterers per resolution cell. The proposed simulator requires as inputs the radar, orbital and surface parameters. The statistics relevant to each single equivalent scatterer are then effectively generated as a 2-D scale mixture of Gaussians and the global statistics of the return from each resolution cell are obtained as the coherent sum of all the scatterers contributions. The rationale of the proposed simulation framework is detailed and meaningful results regarding the analysis of the first simulated images are discussed.

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Section: Introductionmentioning

confidence: 99%

A Physical Approach for SAR Speckle Simulation: First Results

Martino

Iodice

Riccio

et al. 2013

European Journal of Remote Sensing

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“…This complicates the image interpretation, reduces the effectiveness of image segmentation 28 , classification and other 32 , mean and median filter exist and their choice is dependent upon the targeted scientific application. To suppress the speckle in SAR images, enhanced Lee filter 30 was used.…”

Section: Speckle Suppressionmentioning

confidence: 99%

Potential of RISAT-1 SAR Data in Detecting Palaeochannels in Parts of the Thar Desert, India

Kumar¹,

Rajawat²

2017

Current Science

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In the present study, we have demonstrated the potential of RISAT-1 Synthetic Aperture Radar (SAR) data to detect palaeochannels in parts of Thar Desert, India, which may be utilized as one of the guides of geoarchaeological exploration, besides forming groundwater prospective zones. Palaeochannels have been detected using RISAT-1 SAR MRS datasets in the southern parts of Jaisalmer and northeastern parts of Barmer districts, Rajasthan. These palaeochannels of length varying between 14 and 36 km and width varying between 20 and 65 m are present within parabolic sand dune complexes. Palaeochannels have been detected as distinct dark tone on RISAT-1 SAR data compared to feeble expression on corresponding LANDSAT-OLI FCC datasets. This is due to sand-filled valleys, acting as radar smooth surface and absorbing the radar energy with negligible backscatter and enhanced topography due to side-looking capability of RISAT-1 SAR. High-resolution Cartosat DEM has been utilized to prepare topographical profiles, supporting the geomorphological interpretation. Merging of RISAT-1 SAR and LANDSAT ETM datasets using PCA techniques led to enhancements of palaeochannels on merged FCC data products. Like polarization of RISAT-1, SAR data could further enhance and aid in detecting palaeochannels. The entire region was flooded in August 2006 and water had flown through these palaeochannels, which subsequently dried up and facilitated their easy detection; they are otherwise difficult to interpret using pre-flood images. Analysis of sequential post-flood images has been taken up for detailed study of the area, as there is scope to detect additional hitherto unknown palaeochannels.

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“…We view the scene as composed of a set of regions or ensembles of contiguous pixels for which the average backscatter characteristics are similar. In each of these (6) Although in practice the impulse responses h with p E {1, .. , N}, may differ slightly in magnitude, we assume that they are nearly all equal to an impulse response denoted hN. When the one-look complex samples used for multilooking are independent, ap and a, are independent zero-mean random processes, unless p equals q, and Eq.…”

Section: Correlation Properties Of Multilook Intensity Datamentioning

confidence: 99%

“…4 1 5 However, if the number of looks N in Eq. (1) is arbitrarily modified to match the observed larger-thanexpected variance of the intensity (owing to the textural variability of 0.0), gamma distributions still model the statistics of multilook SAR intensity data reasonably well' 6 and have the advantage of a simpler analytical expression. When the one-look samples selected for multilooking are correlated, and they often are because of sampling of the SAR data versus the spatial width of the SAR coherent impulse response, Eqs.…”

Section: Marginal Distributionsmentioning

confidence: 99%

“…These techniques are basically divided into two categories: (1) techniques based on speckle removal as a preprocessing step and thresholding of the intensity level for classification 6 and (2) the gray level co-occurrence matrix (GLCM) method, 7 a statistical approach based on the firstand second-order statistics of the signal. A variety of filters for removing image speckel have been proposed.…”

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confidence: 99%

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Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

Rignot

Chellappa

1993

J. Opt. Soc. Am. A

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

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Segmentation of SAR images

Cited by 108 publications

References 13 publications

A Physical Approach for SAR Speckle Simulation: First Results

A Physical Approach for SAR Speckle Simulation: First Results

Potential of RISAT-1 SAR Data in Detecting Palaeochannels in Parts of the Thar Desert, India

Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

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