SAR Image Ship Detection Based on a Superpixel Non-window Fast CFAR Strategy

Abstract

Synthetic aperture radar (SAR) image ship detection has been attracted attention widely. As a state-of-the-art method, constant false alarm rate (CFAR) detection algorithm is often used in SAR image ship detection applications. However, the detection results based on traditional sliding window in classical CFAR are very sensitive to the sliding window size, and it is difficult to avoid that there are no target pixels in clutter background. In addition, CFAR pixel-by-pixel detection algorithm is inefficient, especially for the detection of target in large sea scene high-resolution SAR images, because the computational complexity of CFAR algorithm based on sliding window is very high. To solve the above problems, this paper proposes a fast ship detection algorithm based on superpixel CFAR in SAR image. This method applies superpixels to CFAR target detection. Based on the superpixel segmentation results, the pure background clutter pixels of SAR image are obtained for clutter model parameter estimation, which avoids the shortcomings of sliding window design in traditional CFAR method. At the same time, compared with the traditional CFAR with the pixel-by-pixel clutter estimation, all the pixel clutter estimation parameters in each superpixel are unified, which greatly speeds up the detection. Experimental results show that the proposed method is very robust for ship detection in SAR images of different resolutions and scenes.