An Improved Full Polarimetric SAR Image Classification Method Combining with Granularity Computing of Quotient Space Theory

A new Full Polarimetric Synthetic Aperture Radar (SAR) image classification method is proposed that combines quotient space granularity computing and the texture information to carry out comprehensive classification. Firstly, we classify the Cloude and Yamaguchi4 decomposition characteristics with texture features using Smooth Support Vector Machine (SSVM) algorithm, to get two classification results, which are the quotient spaces. According to quotient space theory, the two particle size layer are synthesized and in accordance with SAR data distribution polarization characteristics, we use the Wishart measure instead of the traditional Euclidean distance to infer the granularity difference and calculate its category, and combine the results of this reasoning with the synthetic domain in order to get exact classification results. To validate the proposed method, polarimetric SAR data acquired by AIRSAR for San Francisco and Flevoland were employed in classification experiments. The results indicate that the classification results obtained by the proposed method arenot only better than the combination of texture information of the Cloude and Yamaguchi4 supervised classification results, but superior to all the features as a feature vector based on a simple feature fusion for supervised classification results.