A Fast Algorithm for the Sample of PolSAR Data Generation Based on the Wishart Distribution and Chaotic Map.

The performance of most supervised classification methods for synthetic aperture radar (SAR) images is largely tired to the number of samples, while labeled samples are usually very difficult and costly to obtain in the remotely sensing field. Semi-supervised methods, which are achieved by using pseudo samples, have thus been proposed to deal with this problem. Most of these methods, however, cannot be directly applied to fully polarization SAR (PolSAR) images due to the complexity of PolSAR data and it is also inefficient. To this end, this paper proposes a fast algorithm for the generation of pseudo samples of fully PolSAR data used in the classification, which is developed based on the complex Wishart distribution and chaotic maps. First, a weighting parameter that estimates the importance degree of labeled samples is defined in terms of the complexWishart distribution. Second, chaotic maps are introduced to randomly and non-repeatedly select proper samples from the labeled sample set. Third, based on the selected samples with the weighting parameter, pseudo samples are generated. Finally, combined with appropriate classifiers, classification is attained. The experiment carried out on a fully PolSAR image verifies the effectiveness of the proposed algorithm.