ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS
The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processes of sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. One is a multi-Gamma model, which ch...
Published in: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Language: | English |
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00013232 2023-05-15T18:16:16+02:00 ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS Wang, Xiaojian Li, Yu Zhao, Quanhua 2016-06 electronic https://doi.org/10.5194/isprs-annals-III-2-99-2016 https://noa.gwlb.de/receive/cop_mods_00013232 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00013188/isprs-annals-III-2-99-2016.pdf https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-2/99/2016/isprs-annals-III-2-99-2016.pdf eng eng Copernicus Publications ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences -- ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences -- http://www.isprs.org/publications/annals.aspx -- 2194-9050 https://doi.org/10.5194/isprs-annals-III-2-99-2016 https://noa.gwlb.de/receive/cop_mods_00013232 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00013188/isprs-annals-III-2-99-2016.pdf https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-2/99/2016/isprs-annals-III-2-99-2016.pdf uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2016 ftnonlinearchiv https://doi.org/10.5194/isprs-annals-III-2-99-2016 2022-02-08T22:55:43Z The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processes of sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. One is a multi-Gamma model, which characterizes continuous variations corresponding to ice-free area or the background. The other is a Poisson line mosaic model, which characterizes the regional variations of sea ice with different types. The linear combination of the two models builds the mixture model to represent spatial structures of sea ice within SAR intensity imagery. To estimate different sea ice parameters, such as its concentration, scale etc. We define two kinds of geostatistic metrics, theoretical first- and second-order variograms. Their experimental alternatives can be calculated from the SAR intensity imagery directly, then the parameters of the mixture model are estimated through fitting the theoretical variograms to the experimental ones, and by comparing the estimated parameters to the egg code, it is verified that the estimated parameters can indicate sea ice structure information showing in the egg code. The proposed method is applied to simulated images and Radarsat-1 images. The results of the experiments show that the proposed method can estimate the sea ice concentration and floe size accurately and stably within SAR testing images. Article in Journal/Newspaper Sea ice Niedersächsisches Online-Archiv NOA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 99 108 |
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Niedersächsisches Online-Archiv NOA |
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language |
English |
topic |
article Verlagsveröffentlichung |
spellingShingle |
article Verlagsveröffentlichung Wang, Xiaojian Li, Yu Zhao, Quanhua ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS |
topic_facet |
article Verlagsveröffentlichung |
description |
The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processes of sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. One is a multi-Gamma model, which characterizes continuous variations corresponding to ice-free area or the background. The other is a Poisson line mosaic model, which characterizes the regional variations of sea ice with different types. The linear combination of the two models builds the mixture model to represent spatial structures of sea ice within SAR intensity imagery. To estimate different sea ice parameters, such as its concentration, scale etc. We define two kinds of geostatistic metrics, theoretical first- and second-order variograms. Their experimental alternatives can be calculated from the SAR intensity imagery directly, then the parameters of the mixture model are estimated through fitting the theoretical variograms to the experimental ones, and by comparing the estimated parameters to the egg code, it is verified that the estimated parameters can indicate sea ice structure information showing in the egg code. The proposed method is applied to simulated images and Radarsat-1 images. The results of the experiments show that the proposed method can estimate the sea ice concentration and floe size accurately and stably within SAR testing images. |
format |
Article in Journal/Newspaper |
author |
Wang, Xiaojian Li, Yu Zhao, Quanhua |
author_facet |
Wang, Xiaojian Li, Yu Zhao, Quanhua |
author_sort |
Wang, Xiaojian |
title |
ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS |
title_short |
ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS |
title_full |
ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS |
title_fullStr |
ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS |
title_full_unstemmed |
ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS |
title_sort |
estimating sea ice parameters from multi-look sar images using first- and second-order variograms |
publisher |
Copernicus Publications |
publishDate |
2016 |
url |
https://doi.org/10.5194/isprs-annals-III-2-99-2016 https://noa.gwlb.de/receive/cop_mods_00013232 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00013188/isprs-annals-III-2-99-2016.pdf https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-2/99/2016/isprs-annals-III-2-99-2016.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences -- ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences -- http://www.isprs.org/publications/annals.aspx -- 2194-9050 https://doi.org/10.5194/isprs-annals-III-2-99-2016 https://noa.gwlb.de/receive/cop_mods_00013232 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00013188/isprs-annals-III-2-99-2016.pdf https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-2/99/2016/isprs-annals-III-2-99-2016.pdf |
op_rights |
uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/isprs-annals-III-2-99-2016 |
container_title |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
III-2 |
container_start_page |
99 |
op_container_end_page |
108 |
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1766189783482105856 |