Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle
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 glo...
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Cambridge University Press
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ftdoajarticles:oai:doaj.org/article:157b0e2314ad4649aa5eeeaec4a1fa29 2023-05-15T13:29:35+02:00 Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking 2020-12-01T00:00:00Z https://doi.org/10.1017/aog.2020.45 https://doaj.org/article/157b0e2314ad4649aa5eeeaec4a1fa29 EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0260305520000452/type/journal_article https://doaj.org/toc/0260-3055 https://doaj.org/toc/1727-5644 doi:10.1017/aog.2020.45 0260-3055 1727-5644 https://doaj.org/article/157b0e2314ad4649aa5eeeaec4a1fa29 Annals of Glaciology, Vol 61, Pp 260-270 (2020) classification remote sensing sea ice Meteorology. Climatology QC851-999 article 2020 ftdoajarticles https://doi.org/10.1017/aog.2020.45 2023-03-12T01:31:55Z 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 Directory of Open Access Journals: DOAJ Articles Annals of Glaciology 61 83 260 270 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
classification remote sensing sea ice Meteorology. Climatology QC851-999 |
spellingShingle |
classification remote sensing sea ice Meteorology. Climatology QC851-999 Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle |
topic_facet |
classification remote sensing sea ice Meteorology. Climatology QC851-999 |
description |
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 |
Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking |
author_facet |
Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking |
author_sort |
Johannes Lohse |
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 |
publishDate |
2020 |
url |
https://doi.org/10.1017/aog.2020.45 https://doaj.org/article/157b0e2314ad4649aa5eeeaec4a1fa29 |
genre |
Annals of Glaciology Sea ice |
genre_facet |
Annals of Glaciology Sea ice |
op_source |
Annals of Glaciology, Vol 61, Pp 260-270 (2020) |
op_relation |
https://www.cambridge.org/core/product/identifier/S0260305520000452/type/journal_article https://doaj.org/toc/0260-3055 https://doaj.org/toc/1727-5644 doi:10.1017/aog.2020.45 0260-3055 1727-5644 https://doaj.org/article/157b0e2314ad4649aa5eeeaec4a1fa29 |
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_ |
1766001328150020096 |