SAR image segmentation with level set and clonal selection algorithm

Abstract

Synthetic aperture radar (SAR) image segmentation is a fundamental problem in SAR image interpretation. SAR images often contain non-texture object and texture object. Level set method, known as deformable model, is a powerful image segmentation technique. It can get accurate contours of non-texture object, but has poor performance in getting contours of texture object. In this paper, a new modified model of level set based on clonal selection algorithm is proposed. We use clonal selection algorithm to choose some pixels near the contour, and then perform a neighborhood modification on the level set function during its evolution. The region texture information, supervising the modification process, is incorporated into the level set framework. This new method is particularly well adapted to detection of texture object of interesting. We illustrated the performance of the new method on SAR images. Furthermore, we compared our method with level set method and the modified model of level set based on standard genetic algorithm (SGA) in texture object detection results and image segmentation results. The experimental results show that incorporating region texture information into the level set framework, consistent texture objects are obtained, and accurate and robust segmentations can be achieved.