Title :
Optimal Bayesian texture estimators for speckle filtering of detected and polarimetric data
Author :
Lopés, Armand ; Bruniquel, Jérôme ; Séry, Franck ; Nezry, Edmond
Author_Institution :
CESBIO, Toulouse, France
Abstract :
For surfaces satisfying the “product model”, the sample covariance matrix (CM) is the product of a positive scalar random variable μ representing texture and a mean CM representing the polarimetric properties of the surface. The maximum likelihood (ML) estimator of μ is given by the multilook polarimetric whitening filter (MPWF). The ML estimator satisfies the well known multiplicative speckle model. For the multiplicative model, the authors analyze the optimality of the texture estimators by using decision theory and Bayes approach. They develop a new estimator for gamma distributed texture. The a posteriori mean (APM) estimator is radiometrically unbiased and has the smallest mean square error (MSE) of all estimators. The gamma-MAP estimator, on the contrary, is radiometrically biased, but it preserves the textural contrast better
Keywords :
Bayes methods; gamma distribution; geophysical signal processing; geophysical techniques; image texture; maximum likelihood estimation; radar imaging; radar polarimetry; remote sensing by radar; speckle; synthetic aperture radar; Bayes method; SAR; a posteriori mean estimator; covariance matrix; gamma distribution; geophysical signal processing; image texture; land surface; maximum likelihood estimator; measurement technique; multilook polarimetric whitening filter; multiplicative model; multiplicative speckle model; optimal Bayesian texture estimator; positive scalar random variable; product model; radar imaging; radar polarimetry; radar remote sensing; speckle filtering; synthetic aperture radar; terrain mapping; Bayesian methods; Covariance matrix; Decision theory; Filtering; Filters; Maximum likelihood estimation; Radiometry; Random variables; Speckle; Surface texture;
Conference_Titel :
Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International
Print_ISBN :
0-7803-3836-7
DOI :
10.1109/IGARSS.1997.615337