Sonar image segmentation based on statistical modeling of wavelet subbands

This paper deals with the classification and segmentation of seafloor images recorded by sidescan sonar. To address this problem, related to texture analysis, a supervised approach is considered. The features of the textured images are extract by characterizing the wavelet coefficients through parametric probabilistic models. In this contribution, the generalized Gaussian distribution and the α-stable distribution are used. For the classification step, two classifiers are considered: the k-nearest neighbor algorithm, that exploit the Kullback-Leibler divergence as similarity measurement, and the support vector machines. Experimental results on sonar images demonstrate the effectiveness of the proposed approach for sonar image classification and segmentation.