Exploiting spatial correlation features for SAR image analysis

Spatial information is of great importance in Synthetic Aperture Radar (SAR) image analysis and recently, many methods have been developed that take this feature into account. This paper deals with a supervised approach to SAR image classification that exploits spatial features within a hierarchical classification framework. In contrast to the classical approach, which makes the hypothesis about sample data independence, in the proposed method, the spatial dependence of neighboring pixels is taken into account to estimate relatively simple statistical features such as sample spatial mean and sample spatial variance, thus allowing contextual information to be easily handled. The Bhattacharyya distribution distance is used during the training phase, and the generated tree is applied during the test phase. After this, both phases are based on the proposed features. As a result, second-order statistics play a major role in the present classification problem. Experimental results on different SAR data sets are reported. It is shown that the accuracy of the proposed method is better than that of the hit classifier and that the new method is also computationally more convenient