  A new classifier for Polarimetric SAR (PolSAR) images is proposed and
assessed in this paper. Its input consists of segments, and each one is
assigned the class which minimizes a stochastic distance. Assuming the complex
Wishart model, several stochastic distances are obtained from the h-phi family
of divergences, and they are employed to derive hypothesis test statistics that
are also used in the classification process. This article also presents, as a
novelty, analytic expressions for the test statistics based on the following
stochastic distances between complex Wishart models: Kullback-Leibler,
Bhattacharyya, Hellinger, R\'enyi, and Chi-Square; also, the test statistic
based on the Bhattacharyya distance between multivariate Gaussian distributions
is presented. The classifier performance is evaluated using simulated and real
PolSAR data. The simulated data are based on the complex Wishart model, aiming
at the analysis of the proposal well controlled data. The real data refer to
the complex L-band image, acquired during the 1994 SIR-C mission. The results
of the proposed classifier are compared with those obtained by a Wishart
per-pixel/contextual classifier, and we show the better performance of the
region-based classification. The influence of the statistical modeling is
assessed by comparing the results using the Bhattacharyya distance between
multivariate Gaussian distributions for amplitude data. The results with
simulated data indicate that the proposed classification method has a very good
performance when the data follow the Wishart model. The proposed classifier
also performs better than the per-pixel/contextual classifier and the
Bhattacharyya Gaussian distance using SIR-C PolSAR data.

Computer Vision and Pattern Recognition;Applications