Active Contours Driven by Visual Saliency Fitting Energy for Image Segmentation in SAR Images

Abstract

Image segmentation actually could be regarded as a computer vision pre-processing step of cloud computing and big data analysis. Active contour model is a new research direction for images segmentation in synthetic aperture radar(SAR) images. However, due to the SAR images have speckle noise and low signal-to-clutter ratio, active contour models cannot produce satisfied segmentation results. To solve these issues, active contours driven by visual saliency fitting energy is proposed in this paper. First, a saliency map is calculated by improved local contrast measure. Then, a visual saliency fitting energy is proposed based on saliency map and local binary fitting energy. The saliency map has a higher signal-to-clutter ratio and the local binary fitting energy is an active contour model that can effectively segment images with intensity inhomogeneity. In this way, the proposed method has a better segmentation result. The experiments on ship chips in SAR images demonstrate that the proposed method is robust to speckle noise and the segmentation result has good connectivity and integrity for entire ship targets.