Characterization of spatial statistics of distributed targets in SAR data
A method for the analysis of spatial statistics in multifrequency polarimetric Synthetic Aperture Radar (SAR) data is presented. The objective is to extract the intrinsic variability of the target by removing the variability from other sources. Three sources which contribute to the spatial variabili...
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1993
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ftcdlib:oai:escholarship.org/ark:/13030/qt3dm1d1h8 2023-05-15T18:17:44+02:00 Characterization of spatial statistics of distributed targets in SAR data Rignot, E Kwok, R 345 - 363 1993-01-01 application/pdf https://escholarship.org/uc/item/3dm1d1h8 unknown eScholarship, University of California qt3dm1d1h8 https://escholarship.org/uc/item/3dm1d1h8 public International Journal of Remote Sensing, vol 14, iss 2 Geological & Geomatics Engineering Physical Geography and Environmental Geoscience Geomatic Engineering article 1993 ftcdlib 2020-03-20T23:55:51Z A method for the analysis of spatial statistics in multifrequency polarimetric Synthetic Aperture Radar (SAR) data is presented. The objective is to extract the intrinsic variability of the target by removing the variability from other sources. Three sources which contribute to the spatial variability in the returned power from a distributed target are modelled, they are (1) image speckle, (2) system noise, and (3) the intrinsic spatial variability of the target or texture. Speckle and system noise are modelled based on an understanding of the physics of the SAR imaging and processing systems. Texture is modelled as a random variable which modulates the mean returned power from a distributed target. An image model which accounts for all three sources of variability is presented. The presence of texture is shown to increase the image variance-to-mean square ratio and to introduce deviations of the image a u toco variance function from the expected SAR system response. Two textural parameters, the standard deviation of texture and its autocovariance coefficient, are examined. This statistical approach is illustrated using sea-ice SAR imagery acquired by the Jet Propulsion Laboratory three-frequency polarimetric airborne SAR. Textural modulation of the signal has been detected in the imagery. Results show that for different sea-ice types the spatial statistics seem to vary more across frequency than across polarization and the observed differences increase in magnitude with decreasing frequency. The results also suggest the potential of this approach for discrimination of various sea-ice types and open water in single frequency, single polarization SAR data. Correlation of the spatial statistics to field measurements will be important for the verification of these observations. © 1993 Taylor & Francis Group, LLC. Article in Journal/Newspaper Sea ice University of California: eScholarship |
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Open Polar |
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University of California: eScholarship |
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ftcdlib |
language |
unknown |
topic |
Geological & Geomatics Engineering Physical Geography and Environmental Geoscience Geomatic Engineering |
spellingShingle |
Geological & Geomatics Engineering Physical Geography and Environmental Geoscience Geomatic Engineering Rignot, E Kwok, R Characterization of spatial statistics of distributed targets in SAR data |
topic_facet |
Geological & Geomatics Engineering Physical Geography and Environmental Geoscience Geomatic Engineering |
description |
A method for the analysis of spatial statistics in multifrequency polarimetric Synthetic Aperture Radar (SAR) data is presented. The objective is to extract the intrinsic variability of the target by removing the variability from other sources. Three sources which contribute to the spatial variability in the returned power from a distributed target are modelled, they are (1) image speckle, (2) system noise, and (3) the intrinsic spatial variability of the target or texture. Speckle and system noise are modelled based on an understanding of the physics of the SAR imaging and processing systems. Texture is modelled as a random variable which modulates the mean returned power from a distributed target. An image model which accounts for all three sources of variability is presented. The presence of texture is shown to increase the image variance-to-mean square ratio and to introduce deviations of the image a u toco variance function from the expected SAR system response. Two textural parameters, the standard deviation of texture and its autocovariance coefficient, are examined. This statistical approach is illustrated using sea-ice SAR imagery acquired by the Jet Propulsion Laboratory three-frequency polarimetric airborne SAR. Textural modulation of the signal has been detected in the imagery. Results show that for different sea-ice types the spatial statistics seem to vary more across frequency than across polarization and the observed differences increase in magnitude with decreasing frequency. The results also suggest the potential of this approach for discrimination of various sea-ice types and open water in single frequency, single polarization SAR data. Correlation of the spatial statistics to field measurements will be important for the verification of these observations. © 1993 Taylor & Francis Group, LLC. |
format |
Article in Journal/Newspaper |
author |
Rignot, E Kwok, R |
author_facet |
Rignot, E Kwok, R |
author_sort |
Rignot, E |
title |
Characterization of spatial statistics of distributed targets in SAR data |
title_short |
Characterization of spatial statistics of distributed targets in SAR data |
title_full |
Characterization of spatial statistics of distributed targets in SAR data |
title_fullStr |
Characterization of spatial statistics of distributed targets in SAR data |
title_full_unstemmed |
Characterization of spatial statistics of distributed targets in SAR data |
title_sort |
characterization of spatial statistics of distributed targets in sar data |
publisher |
eScholarship, University of California |
publishDate |
1993 |
url |
https://escholarship.org/uc/item/3dm1d1h8 |
op_coverage |
345 - 363 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
International Journal of Remote Sensing, vol 14, iss 2 |
op_relation |
qt3dm1d1h8 https://escholarship.org/uc/item/3dm1d1h8 |
op_rights |
public |
_version_ |
1766192747478253568 |