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Unsupervised Segmentation Of Polarimetric SAR DataMethod of unsupervised segmentation of polarimetric synthetic-aperture-radar (SAR) image data into classes involves selection of classes on basis of multidimensional fuzzy clustering of logarithms of parameters of polarimetric covariance matrix. Data in each class represent parts of image wherein polarimetric SAR backscattering characteristics of terrain regarded as homogeneous. Desirable to have each class represent type of terrain, sea ice, or ocean surface distinguishable from other types via backscattering characteristics. Unsupervised classification does not require training areas, is nearly automated computerized process, and provides nonsubjective selection of image classes naturally well separated by radar.
Document ID
19940000371
Acquisition Source
Legacy CDMS
Document Type
Other - NASA Tech Brief
Authors
Rignot, Eric J.
(Caltech)
Dubois, Pascale
(Caltech)
Van Zyl, Jakob
(Caltech)
Kwok, Ronald
(Caltech)
Chellappa, Rama
(Caltech)
Date Acquired
August 16, 2013
Publication Date
July 1, 1994
Publication Information
Publication: NASA Tech Briefs
Volume: 18
Issue: 7
ISSN: 0145-319X
Subject Category
Physical Sciences
Report/Patent Number
NPO-18591
Accession Number
94B10371
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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