Sea ice mapping method for SeaWinds
A sea ice mapping algorithm for SeaWinds is developed that incorporates statistical and spatial a priori information in a modified maximum a posteriori (MAP) framework. Spatial a priori data are incorporated in the loss terms of a Bayes risk formulation. Conditional distributions and priors for sea...
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ftbrighamyoung:oai:scholarsarchive.byu.edu:facpub-1390 2023-07-23T04:19:50+02:00 Sea ice mapping method for SeaWinds Long, David G. Anderson, Hyrum S. 2005-03-01T08:00:00Z application/pdf https://scholarsarchive.byu.edu/facpub/391 https://scholarsarchive.byu.edu/context/facpub/article/1390/viewcontent/IR_CISOPTR_713.pdf English eng BYU ScholarsArchive https://scholarsarchive.byu.edu/facpub/391 https://scholarsarchive.byu.edu/context/facpub/article/1390/viewcontent/IR_CISOPTR_713.pdf Faculty Publications Bayes methods geophysical signal processing maximum likelihood estimation oceanographic techniques principal component analysis remote sensing by radar sea ice water synthetic aperture radar terrain mapping Electrical and Computer Engineering text 2005 ftbrighamyoung 2023-07-03T22:20:23Z A sea ice mapping algorithm for SeaWinds is developed that incorporates statistical and spatial a priori information in a modified maximum a posteriori (MAP) framework. Spatial a priori data are incorporated in the loss terms of a Bayes risk formulation. Conditional distributions and priors for sea ice and ocean statistics are represented as empirical histograms that are forced to conform to a set of expected histograms via principal component filtering. Tuning parameters for the algorithm allow adjustments in the algorithm's performance. Results of the algorithm exhibit high correlation with the Remund-Long sea ice mapping algorithm for SeaWinds and the Special Sensor Microwave/Imager National Aeronautics and Space Administration Team 30% ice edge, and are verified with RADARSAT-1 ScanSAR imagery. The resulting sea ice maps exhibit high edge detail, preserve polynyas and ice bodies disjoint from the primary ice sheet, and thus are suitable for use with wind retrieval and sea ice studies. Principles employed in the algorithm may be of interest in other classification studies. Text Ice Sheet Sea ice Brigham Young University (BYU): ScholarsArchive |
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Open Polar |
collection |
Brigham Young University (BYU): ScholarsArchive |
op_collection_id |
ftbrighamyoung |
language |
English |
topic |
Bayes methods geophysical signal processing maximum likelihood estimation oceanographic techniques principal component analysis remote sensing by radar sea ice water synthetic aperture radar terrain mapping Electrical and Computer Engineering |
spellingShingle |
Bayes methods geophysical signal processing maximum likelihood estimation oceanographic techniques principal component analysis remote sensing by radar sea ice water synthetic aperture radar terrain mapping Electrical and Computer Engineering Long, David G. Anderson, Hyrum S. Sea ice mapping method for SeaWinds |
topic_facet |
Bayes methods geophysical signal processing maximum likelihood estimation oceanographic techniques principal component analysis remote sensing by radar sea ice water synthetic aperture radar terrain mapping Electrical and Computer Engineering |
description |
A sea ice mapping algorithm for SeaWinds is developed that incorporates statistical and spatial a priori information in a modified maximum a posteriori (MAP) framework. Spatial a priori data are incorporated in the loss terms of a Bayes risk formulation. Conditional distributions and priors for sea ice and ocean statistics are represented as empirical histograms that are forced to conform to a set of expected histograms via principal component filtering. Tuning parameters for the algorithm allow adjustments in the algorithm's performance. Results of the algorithm exhibit high correlation with the Remund-Long sea ice mapping algorithm for SeaWinds and the Special Sensor Microwave/Imager National Aeronautics and Space Administration Team 30% ice edge, and are verified with RADARSAT-1 ScanSAR imagery. The resulting sea ice maps exhibit high edge detail, preserve polynyas and ice bodies disjoint from the primary ice sheet, and thus are suitable for use with wind retrieval and sea ice studies. Principles employed in the algorithm may be of interest in other classification studies. |
format |
Text |
author |
Long, David G. Anderson, Hyrum S. |
author_facet |
Long, David G. Anderson, Hyrum S. |
author_sort |
Long, David G. |
title |
Sea ice mapping method for SeaWinds |
title_short |
Sea ice mapping method for SeaWinds |
title_full |
Sea ice mapping method for SeaWinds |
title_fullStr |
Sea ice mapping method for SeaWinds |
title_full_unstemmed |
Sea ice mapping method for SeaWinds |
title_sort |
sea ice mapping method for seawinds |
publisher |
BYU ScholarsArchive |
publishDate |
2005 |
url |
https://scholarsarchive.byu.edu/facpub/391 https://scholarsarchive.byu.edu/context/facpub/article/1390/viewcontent/IR_CISOPTR_713.pdf |
genre |
Ice Sheet Sea ice |
genre_facet |
Ice Sheet Sea ice |
op_source |
Faculty Publications |
op_relation |
https://scholarsarchive.byu.edu/facpub/391 https://scholarsarchive.byu.edu/context/facpub/article/1390/viewcontent/IR_CISOPTR_713.pdf |
_version_ |
1772183285806399488 |