Integrating incidence angle dependencies into the clustering-based segmentation of SAR images
Abstract: Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nat...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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Language: | English |
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2020
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Online Access: | https://hdl.handle.net/10067/1708110151162165141 |
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ftunivantwerpen:c:irua:170811 2024-09-30T14:43:11+00:00 Integrating incidence angle dependencies into the clustering-based segmentation of SAR images Cristea, Anca van Houtte, Jeroen Doulgeris, Anthony P. 2020 https://hdl.handle.net/10067/1708110151162165141 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2020.2993067 info:eu-repo/semantics/altIdentifier/isi/000544052600007 info:eu-repo/semantics/closedAccess 1939-1404 IEEE journal of selected topics in applied earth observation and remote sensing Economics Physics Engineering sciences. Technology info:eu-repo/semantics/article 2020 ftunivantwerpen https://doi.org/10.1109/JSTARS.2020.2993067 2024-09-10T04:06:35Z Abstract: Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments. Article in Journal/Newspaper Sea ice IRUA - Institutional Repository van de Universiteit Antwerpen IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 2925 2939 |
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Open Polar |
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IRUA - Institutional Repository van de Universiteit Antwerpen |
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ftunivantwerpen |
language |
English |
topic |
Economics Physics Engineering sciences. Technology |
spellingShingle |
Economics Physics Engineering sciences. Technology Cristea, Anca van Houtte, Jeroen Doulgeris, Anthony P. Integrating incidence angle dependencies into the clustering-based segmentation of SAR images |
topic_facet |
Economics Physics Engineering sciences. Technology |
description |
Abstract: Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments. |
format |
Article in Journal/Newspaper |
author |
Cristea, Anca van Houtte, Jeroen Doulgeris, Anthony P. |
author_facet |
Cristea, Anca van Houtte, Jeroen Doulgeris, Anthony P. |
author_sort |
Cristea, Anca |
title |
Integrating incidence angle dependencies into the clustering-based segmentation of SAR images |
title_short |
Integrating incidence angle dependencies into the clustering-based segmentation of SAR images |
title_full |
Integrating incidence angle dependencies into the clustering-based segmentation of SAR images |
title_fullStr |
Integrating incidence angle dependencies into the clustering-based segmentation of SAR images |
title_full_unstemmed |
Integrating incidence angle dependencies into the clustering-based segmentation of SAR images |
title_sort |
integrating incidence angle dependencies into the clustering-based segmentation of sar images |
publishDate |
2020 |
url |
https://hdl.handle.net/10067/1708110151162165141 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
1939-1404 IEEE journal of selected topics in applied earth observation and remote sensing |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2020.2993067 info:eu-repo/semantics/altIdentifier/isi/000544052600007 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1109/JSTARS.2020.2993067 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
13 |
container_start_page |
2925 |
op_container_end_page |
2939 |
_version_ |
1811645095996293120 |