Aiming at the problem of ship detection in synthetic aperture radar(SAR)images,a multi-layer saliency target detection method combining selection mechanism and contour information is proposed.Firstly,non-subsampled shearlet transform(NSST)and spectral residual method are used to extract the globally significant region.Secondly,an active contour saliency model based on dynamic constant false alarm rate(CFAR)is proposed to filter out the false alarms of candidate regions step by step and extract the target contour,so as to realize the accurate detection of targets.The proposed method can quickly capture the target area from coarse to fine,so as to achieve high-efficiency and high-resolution SAR image ship detection.Finally,the algorithm is tested on real SAR datasets.Compared with other classical ship detection methods,the proposed algorithm not only effectively suppresses the influence of sea clutter,but also greatly improves the detection accuracy.
关键词
SAR图像目标检测/非下采样剪切波变换/显著性检测/活动轮廓模型
Key words
synthetic aperture radar(SAR)image target detection/non-subsampled shearlet transform(NSST)/saliency detection/active contour model
维普
万方数据