Classification strategies for polarimetric SAR sea ice data
ABSTRACT / RESUME Bayesian classification of polarimetric data is based on the complex Wishart distribution of the coherency matrix. The classifier has shown to be a flexible tool available in a variety of options. In this paper we investigate several classification strategies for sea ice classifica...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.407.1931 2023-05-15T18:17:28+02:00 Classification strategies for polarimetric SAR sea ice data Bernd Scheuchl Irena Hajnsek Ian Cumming The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.1931 http://sar.ece.ubc.ca/papers/PolInSAR_strategies.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.1931 http://sar.ece.ubc.ca/papers/PolInSAR_strategies.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://sar.ece.ubc.ca/papers/PolInSAR_strategies.pdf text ftciteseerx 2016-01-08T03:06:13Z ABSTRACT / RESUME Bayesian classification of polarimetric data is based on the complex Wishart distribution of the coherency matrix. The classifier has shown to be a flexible tool available in a variety of options. In this paper we investigate several classification strategies for sea ice classification. All methods show reasonable results, although some user input prior to classification seems beneficial. Noise and incidence angle were found to affect the classification result, the latter due to a change in the relative mix of scattering mechanisms. Surface scattering is found to be strongly incidence angle dependent. Multi-frequency data have a superior information content and can separate ice types, whereas the scattering information content can not be exactly discriminated using only C-band data. 1 Text Sea ice Unknown |
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Open Polar |
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ftciteseerx |
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English |
description |
ABSTRACT / RESUME Bayesian classification of polarimetric data is based on the complex Wishart distribution of the coherency matrix. The classifier has shown to be a flexible tool available in a variety of options. In this paper we investigate several classification strategies for sea ice classification. All methods show reasonable results, although some user input prior to classification seems beneficial. Noise and incidence angle were found to affect the classification result, the latter due to a change in the relative mix of scattering mechanisms. Surface scattering is found to be strongly incidence angle dependent. Multi-frequency data have a superior information content and can separate ice types, whereas the scattering information content can not be exactly discriminated using only C-band data. 1 |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Bernd Scheuchl Irena Hajnsek Ian Cumming |
spellingShingle |
Bernd Scheuchl Irena Hajnsek Ian Cumming Classification strategies for polarimetric SAR sea ice data |
author_facet |
Bernd Scheuchl Irena Hajnsek Ian Cumming |
author_sort |
Bernd Scheuchl |
title |
Classification strategies for polarimetric SAR sea ice data |
title_short |
Classification strategies for polarimetric SAR sea ice data |
title_full |
Classification strategies for polarimetric SAR sea ice data |
title_fullStr |
Classification strategies for polarimetric SAR sea ice data |
title_full_unstemmed |
Classification strategies for polarimetric SAR sea ice data |
title_sort |
classification strategies for polarimetric sar sea ice data |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.1931 http://sar.ece.ubc.ca/papers/PolInSAR_strategies.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
http://sar.ece.ubc.ca/papers/PolInSAR_strategies.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.1931 http://sar.ece.ubc.ca/papers/PolInSAR_strategies.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766191702570172416 |