A Level Set SAR Image Segmentation Method with Combined Region and Edge Information

In this paper, a new level set synthetic aperture radar (SAR) image segmentation approach based on region and edge information is proposed. An energy functional which is adapted for SAR image segmentation is defined. The energy functional consists of a region-based term derived from maximum-likelihood estimation of a mixed Gamma model and a boundary-based term derived from geodesic active contour model. Partial differential equations (PDEs) of curve evolution are obtained by minimization of the energy functional. To implement image segmentation, the solution of the PDEs by a level set approach is proposed. The efficiency of the method is verified by both synthetic and real SAR images. Experimental results implement the more accurate and rapid SAR images segmentation without preprocessing steps to filter speckle noise.