Keywords
geophysical signal processing, image classification, oceanographic techniques, radar signal processing, radiometry, remote sensing, remote sensing by radar, sea ice, water, sensor fusion
Abstract
Characterizing the variability in sea ice in the polar regions is fundamental to an understanding of global climate and the geophysical processes governing 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 twelve-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 (ML) and maximum a posteriori (MAP) 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 weighted nearest-neighbor classifier. Though validation is limited, the algorithm results in an ice classification that is judged to be superior to a conventional k-means approach.
Original Publication Citation
Remund, Q. P., D. G. Long, and M. R. Drinkwater. "An Iterative Approach to Multisensor Sea Ice Classification." Geoscience and Remote Sensing, IEEE Transactions on 38.4 (2): 1843-56
BYU ScholarsArchive Citation
Long, David G.; Drinkwater, Mark R.; and Remund, Quinn P., "An iterative approach to multisensor sea ice classification" (2000). Faculty Publications. 591.
https://scholarsarchive.byu.edu/facpub/591
Document Type
Peer-Reviewed Article
Publication Date
2000-07-01
Permanent URL
http://hdl.lib.byu.edu/1877/1018
Publisher
IEEE
Language
English
College
Ira A. Fulton College of Engineering and Technology
Department
Electrical and Computer Engineering
Copyright Status
© 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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