A Saliency-Based Method for SAR Target Detection
Haixiang Li, Xuelian Yu, Xuegang Wang · 2018
Attention model and saliency detection has been widely researched in computer vision. This paper contributes to present a novel saliency-based detection algorithm for synthetic aperture radar (SAR) interpretation. Firstly, the input image is segmented into superpixels by simple linear iterative cluster (SLIC) process. Secondly, three features maps are created based on standard deviation, peak value and comparison with background, respectively, and the final saliency map is the fusion of the three feature maps. Lastly, a global threshold is utilized to segment the saliency map and extract target regions. Experimental results with real SAR images demonstrate that the produced saliency map is effective on both highlighting targets and inhibiting background. The proposed method also shows better detection performance and less time-consuming than the conventional CFAR.