Target Detection Algorithm for SAR Image Based on Visual Saliency
Based on visual saliency theory and local probability density function statistical feature, a target detection algorithm for SAR image is proposed. Local probability density function statistical feature reflects the difference between target and clutter on human vision. According to local probability density function statistical feature, saliency map of SAR image could be calculated by using hypothesis testing theory and Bayes theorem. Then target detection result could be acquired from saliency map by binary segmentation. For different kinds of real SAR images, target detections are implemented by the proposed algorithm and CFAR algorithm. The comparison of the detection results shows that the proposed algorithm detects all size-fixed targets with lower false alarm rate than CFAR algorithm.
Huijie Xie Tao Tang Deliang Xiang Yi Su
College of Electronic Science and Engineering, National University of Defense Technology, China
国际会议
Progress in Electromagnetics Research Symposium 2014(2014年电磁学研究新进展学术研讨会)
广州
英文
1817-1822
2014-08-01（万方平台首次上网日期，不代表论文的发表时间）