Target Detection via Bayesian-Morphological Saliency in High-Resolution SAR Images

The classical target detection methods in synthetic aperture radar (SAR) images are mainly dependent on the intensity differences between the targets and clutter. Although they are effective in the simple scenes with high signal-to-clutter ratio (SCR), they may lose effectiveness in the complex scenes with low SCR. Generally, in high-resolution SAR images, the targets present not only high intensities but also specific size characteristics compared with the clutter. Based on this fact, in this paper, we propose a new target detection method for high-resolution SAR images via Bayesian-morphological saliency, which mainly contains two stages: Bayesian saliency map construction and morphological saliency map construction. The Bayesian saliency map can obtain the complete structures of the bright objects including the targets of interest and some bright clutter, via the superpixel segmentation and Bayesian framework. Furthermore, the morphological saliency map can highlight the targets of interest while suppressing both the natural and man-made clutter via the size prior information of the targets. The experimental results on the miniSAR real data set show that the proposed target detection method is effective.