SAR Image Segmentation Based On Instantaneous Coefficient Of Variation

Synthetic aperture radar (SAR) can image in almost any condition and produce high-resolution images with good flexibility. SAR has been applied widely for military and civil use. People hope to extract the interesting regions by SAR image segmentation. Image segmentation plays a key role in digital image processing and works for characters extracting, paramerters measurement and pattern recognition. To study SAR image segmentation is meaningful to theories and applications.Compared with traditional segmentation based histogram and edge detection, the methods region-based is a popular tool due to its ability of suppressing noise and more accurate segmentation. But the methods region-based has to resolve over-segmentation and regions edge location. According to these, instantaneous coefficient of variation (ICOV) gradient operator is used to construct a region model characterized by edge-preserving, and the main work is as follows:1. To develop a SAR image region model characterized by edge-preserving. SAR image segmentation suffers from over segmentation and losing edge because of speckle. Speckle reducing anisotropic diffusion (SRAD) is an edge-preserving filter and used to reduce speckle and protect object edge in SAR images.2. To propose a method to get initial regions using instantaneous coefficient of variation (ICOV) gradient operator and watershed transform. ICOV has single peak response and narrow respose width at edge location. Watershed transform is able to detect continous and close edge with one-pixel width. The properties of ICOV and watershed transform are advantageous to edge detection.3. To construct region adjacency graph(RAG)based on initial regions of watershed transform. SAR image region model is accomplished by RAG. Traditional image is presentated based on pixels. RAG simplifies SAR image structures in the form of small regions (sets of pixels).At the end, the above region model of SAR image is combined with region-level MRF to be used in SAR image segmentation. The experiments results prove that the proposed segmentation leads to more accurate edge detection compared to the segmentation based on other classical gradient operators.