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Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity dataWe present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization SAR intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computer-simulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.
Document ID
19930071228
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Rignot, E.
(JPL Pasadena, CA, United States)
Chellappa, R.
(Maryland Univ. College Park, United States)
Date Acquired
August 16, 2013
Publication Date
April 1, 1993
Publication Information
Publication: Optical Society of America, Journal, A: Optics and Image Science
Volume: 10
Issue: 4
ISSN: 0740-3232
Subject Category
Communications And Radar
Accession Number
93A55225
Funding Number(s)
CONTRACT_GRANT: F49620-92-J-0130
Distribution Limits
Public
Copyright
Other

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