Game Theoretic Classification of Polarimetric SAR images

This paper reports on a region based classification of polarimetric synthetic aperture radar (PolSAR) images using the concept of game theory. The proposed method mainly contains the following steps. Firstly, the PolSAR image is partitioned into over-segments using an adaptation of k-means approach. Then, in order to compute the similarity between two distinct over-segments, a measure from polarimetric features and region size is defined. Finally, the regions or over-segments are merged into the meaningful clusters using a game theory based approach. In the game theory way, region merging problem is transformed into an iterative figure/ground separation state. In other words, considering the similarity measure, over-segments that belong to the figure compete with others through the game and obtain a considerable advantage in comparison with others. Accordingly, these privileged over-segments can be merged as an individual cluster. For clustering of the remaining over-segments, the procedure should be repeated. The performance of the proposed classification framework on simulated and real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for classification of PolSAR images.