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Maximum likelihood classification of synthetic aperture radar imageryClassification of synthetic aperture radar (SAR) images has important applications in geology, agriculture, and the military. A statistical model for SAR images is reviewed and a maximum likelihood classification algorithm developed for the classification of agricultural fields based on the model. It is first assumed that the target feature information is known a priori. The performance of the algorithm is then evaluated in terms of the probability of incorrect classification. A technique is also presented to extract the needed feature information from a SAR image; then both the feature extraction and the maximum likelihood classification algorithms are tested on a SEASAT-A SAR image.
Document ID
19860057278
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Frost, V. S.
(Kansas Univ. Center for Research, Inc. Lawrence, KS, United States)
Yurovsky, L. S.
(University of Kansas Center for Research Inc., Lawrence, United States)
Date Acquired
August 12, 2013
Publication Date
December 1, 1985
Publication Information
Publication: Computer Vision, Graphics, and Image Processing
Volume: 32
ISSN: 0734-189X
Subject Category
Communications And Radar
Accession Number
86A42016
Funding Number(s)
CONTRACT_GRANT: NAGW-381
Distribution Limits
Public
Copyright
Other

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