A SAR Image Segmentation Method Based on MLRT

SAR image segmentation is a key procedure for target extraction, classification, recognition and surveillance. In this paper, firstly, we propose a multiscale likelihood ratio test (MLRT) for SAR image segmentation. MLRT can enhance the distinction between different signals by fusing their all kinds of features from different scales. And in our application, the multiscale features are from SAR multiscale sequence images. Secondly, in order to satisfy the sample dependence condition of maximum likelihood estimation, and improve the computation efficiency greatly, we employed Bootstrap sampling technique to make all the statistical parameters be estimated in the best independent conditions. Thirdly, a precise segmentation results of SAR image can be obtained based on MLRT, where the parameter can be estimated by maximum likelihood estimation and MLRT histogram of multiscale sequence images base on Bootstrap sample. Finally, correct rates of simulated images segmentation are used to test our method, and true SAR images are segmented to demonstrate the validity.