Superpixel Segmentation of Marine SAR Images Based on Local Fuzzy Iteration and Edge Information for Target Detection

Most of the existing superpixel segmentation based SAR target detection algorithms cannot keep the independence of small targets under complex background, especially when the size of the targets varies greatly. Focusing on the problem, we propose a novel local fuzzy iterative superpixel segmentation method using edge information (LFI-EI) for SAR image target detection. Firstly, we use an edge detector to extract the edge information in the SAR image. Secondly, a new superpixel initialization method combining the edge information is proposed and a dissimilarity measurement is defined. Thirdly, instead of hard iterative clustering, we introduce the fuzzy theory to construct local fuzzy iterative clustering to obtain the superpixel segmentation result. Finally, a small regions integration step using edge information is implemented to enhance the superpixel segmentation result. By comparing the superpixel segmentation results of three competitors on simulated SAR images and real SAR images, it can be found that the proposed LFI-EI method can achieve satisfying superpixel segmentation performance, and the problem that small targets cannot maintain their independence when the size of targets varies greatly in the SAR image can be solved to some extent. Meanwhile, based on the LFI-EI superpixel segmentation method, the LFI-EI detector is proposed for target detection. The detector uses the contrast features between superpixels to transform the detection problem into a classification problem, and ultimately obtains the target detection result. The target detection experiment on the superpixel segmentation results proves that LFI-EI detector has good detection performance, and further verifies the superiority of the proposed LFI-EI superpixel segmentation method.