SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches
Precision tracking of sea ice is crucial for understanding ice dynamics and practical use in navigation through the deformation characteristics. Imaging remote sensing systems, particularly Synthetic Aperture Radar (SAR), make possible monitoring of sea ice under almost any weather conditions and in...
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ftchalmersuniv:oai:research.chalmers.se:524253 2024-10-20T14:07:02+00:00 SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches Demchev, Denis M. Eriksson, Leif Smolanitsky, Vasily 2021 text https://research.chalmers.se/en/publication/524253 unknown https://research.chalmers.se/en/publication/524253 Remote Sensing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing 2021 ftchalmersuniv 2024-10-08T15:50:57Z Precision tracking of sea ice is crucial for understanding ice dynamics and practical use in navigation through the deformation characteristics. Imaging remote sensing systems, particularly Synthetic Aperture Radar (SAR), make possible monitoring of sea ice under almost any weather conditions and independent of the illumination conditions. Two main family of approaches have been widely used so far – area-based and feature-based. We evaluate their performance using texture properties of sea ice images which are physically related to the radar signal backscattering within a SAR resolution cell. Here we use the local entropy as indicator determined by gray-level-co-occurrence matrix (GLCM) method describing how often different compositions of pixel brightness values occur in an image. A total of 120 satellite SAR sea ice images acquired over different Arctic seas from Sentinel-1 were processed and analysed. Based on the analysis, assessment criteria for choosing sea ice tracking algorithm is proposed. Other/Unknown Material Arctic Sea ice Chalmers University of Technology: Chalmers research Arctic |
institution |
Open Polar |
collection |
Chalmers University of Technology: Chalmers research |
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ftchalmersuniv |
language |
unknown |
topic |
Remote Sensing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing |
spellingShingle |
Remote Sensing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing Demchev, Denis M. Eriksson, Leif Smolanitsky, Vasily SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches |
topic_facet |
Remote Sensing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing |
description |
Precision tracking of sea ice is crucial for understanding ice dynamics and practical use in navigation through the deformation characteristics. Imaging remote sensing systems, particularly Synthetic Aperture Radar (SAR), make possible monitoring of sea ice under almost any weather conditions and independent of the illumination conditions. Two main family of approaches have been widely used so far – area-based and feature-based. We evaluate their performance using texture properties of sea ice images which are physically related to the radar signal backscattering within a SAR resolution cell. Here we use the local entropy as indicator determined by gray-level-co-occurrence matrix (GLCM) method describing how often different compositions of pixel brightness values occur in an image. A total of 120 satellite SAR sea ice images acquired over different Arctic seas from Sentinel-1 were processed and analysed. Based on the analysis, assessment criteria for choosing sea ice tracking algorithm is proposed. |
author |
Demchev, Denis M. Eriksson, Leif Smolanitsky, Vasily |
author_facet |
Demchev, Denis M. Eriksson, Leif Smolanitsky, Vasily |
author_sort |
Demchev, Denis M. |
title |
SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches |
title_short |
SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches |
title_full |
SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches |
title_fullStr |
SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches |
title_full_unstemmed |
SAR image texture entropy analysis for applicability assessment of area-based and feature-based AEA ice tracking approaches |
title_sort |
sar image texture entropy analysis for applicability assessment of area-based and feature-based aea ice tracking approaches |
publishDate |
2021 |
url |
https://research.chalmers.se/en/publication/524253 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
https://research.chalmers.se/en/publication/524253 |
_version_ |
1813446020603838464 |