Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle
Abstract Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instrumen...
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crcambridgeupr:10.1017/aog.2020.45 2024-06-23T07:45:36+00:00 Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle Lohse, Johannes Doulgeris, Anthony P. Dierking, Wolfgang 2020 http://dx.doi.org/10.1017/aog.2020.45 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305520000452 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Annals of Glaciology volume 61, issue 83, page 260-270 ISSN 0260-3055 1727-5644 journal-article 2020 crcambridgeupr https://doi.org/10.1017/aog.2020.45 2024-06-12T04:04:44Z Abstract Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier. Article in Journal/Newspaper Annals of Glaciology Sea ice Cambridge University Press Annals of Glaciology 61 83 260 270 |
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Open Polar |
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Cambridge University Press |
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crcambridgeupr |
language |
English |
description |
Abstract Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier. |
format |
Article in Journal/Newspaper |
author |
Lohse, Johannes Doulgeris, Anthony P. Dierking, Wolfgang |
spellingShingle |
Lohse, Johannes Doulgeris, Anthony P. Dierking, Wolfgang Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
author_facet |
Lohse, Johannes Doulgeris, Anthony P. Dierking, Wolfgang |
author_sort |
Lohse, Johannes |
title |
Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
title_short |
Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
title_full |
Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
title_fullStr |
Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
title_full_unstemmed |
Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
title_sort |
mapping sea-ice types from sentinel-1 considering the surface-type dependent effect of incidence angle |
publisher |
Cambridge University Press (CUP) |
publishDate |
2020 |
url |
http://dx.doi.org/10.1017/aog.2020.45 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305520000452 |
genre |
Annals of Glaciology Sea ice |
genre_facet |
Annals of Glaciology Sea ice |
op_source |
Annals of Glaciology volume 61, issue 83, page 260-270 ISSN 0260-3055 1727-5644 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/aog.2020.45 |
container_title |
Annals of Glaciology |
container_volume |
61 |
container_issue |
83 |
container_start_page |
260 |
op_container_end_page |
270 |
_version_ |
1802641403603320832 |