A Method of Polarimetric SAR Image Classification Combined Scattering Similarity with Wishart

Aiming at the deficiency of target classification method based on H-a, such as large computation , in this paper, an algorithm combined scattering similarity with wishart for the images classification of the polarimetric SAR, which greatly saves the time of data processing. A method of parameter substitution by normalizing target coherence matrix, a classification method similar to H-a is obtained, which avoids calculating eigenvalues and eigenvectors and improves the classification efficiency. In order to further improve the classification effect, Wishart iterated classifier is combined for classify. Finally, the Yellow River ice cream area obtained by the Radarsat-2 satellite is classified to verify the effectiveness of the method, the proposed method can improve computational efficiency and improve classification effect of ground features.