SAR IMAGE SEGMENTATION BASED ON SUPER-PIXEL AND KERNEL-IMPROVED CV MODEL

The paper focuses on the effect of pixel intensity random variation in the homogeneous region of Synthetic Aperture Radar (SAR) images. Considering the shortage of CV (Chan-Vese) model to describe the energy variation inside and outside the curve, a SAR image segmentation algorithm based on super-pixel and kernel-improved CV model is proposed. The Simple Linear Iterative Clustering 0 (SLIC0) algorithm is employed for pixel reconstruction. Simultaneously, our improved model combines a Laplacian kernel function with l(2) fitting energy to construct the novel fitting energy, which can describe the energy inside and outside the curve clearly. Specifically, a distance regularized term is embedded into our proposed model to avoid the re-initialization of level set and accelerate the convergence of the improved model. Finally, experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrates our method achieves competitive performances compared with other related algorithms.