An Iterative Approach to Multisensor Sea Ice Classification
Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes which govern climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2,...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.30.334 2023-05-15T18:17:15+02:00 An Iterative Approach to Multisensor Sea Ice Classification Quinn Remund David David G. Long Mark R. Drinkwater The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.334 http://polar.jpl.nasa.gov/Publications/remund_etal_TGARS00.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.334 http://polar.jpl.nasa.gov/Publications/remund_etal_TGARS00.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://polar.jpl.nasa.gov/Publications/remund_etal_TGARS00.pdf text ftciteseerx 2016-01-07T22:00:26Z Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes which govern climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2, and SSM/I are reconstructed into enhanced resolution imagery for use in ice type classification. The resulting 12-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum likelihood and maximum a posteriori techniques. For a given ice type, the conditional probability distributions of observed vectors are assumed to be Gaussian. The cluster centroids, covariance matrices, and a priori distributions are estimated from the classification of a previous temporal image set. An initial classification is produced using centroid training data and a . Text Sea ice Unknown |
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description |
Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes which govern climate changes. Sea ice can be grouped into a number of general classes with different characteristics. Multisensor data from NSCAT, ERS-2, and SSM/I are reconstructed into enhanced resolution imagery for use in ice type classification. The resulting 12-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum likelihood and maximum a posteriori techniques. For a given ice type, the conditional probability distributions of observed vectors are assumed to be Gaussian. The cluster centroids, covariance matrices, and a priori distributions are estimated from the classification of a previous temporal image set. An initial classification is produced using centroid training data and a . |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Quinn Remund David David G. Long Mark R. Drinkwater |
spellingShingle |
Quinn Remund David David G. Long Mark R. Drinkwater An Iterative Approach to Multisensor Sea Ice Classification |
author_facet |
Quinn Remund David David G. Long Mark R. Drinkwater |
author_sort |
Quinn Remund David |
title |
An Iterative Approach to Multisensor Sea Ice Classification |
title_short |
An Iterative Approach to Multisensor Sea Ice Classification |
title_full |
An Iterative Approach to Multisensor Sea Ice Classification |
title_fullStr |
An Iterative Approach to Multisensor Sea Ice Classification |
title_full_unstemmed |
An Iterative Approach to Multisensor Sea Ice Classification |
title_sort |
iterative approach to multisensor sea ice classification |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.334 http://polar.jpl.nasa.gov/Publications/remund_etal_TGARS00.pdf |
genre |
Sea ice |
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Sea ice |
op_source |
http://polar.jpl.nasa.gov/Publications/remund_etal_TGARS00.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.334 http://polar.jpl.nasa.gov/Publications/remund_etal_TGARS00.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766191357399924736 |