A HIERARCHICAL SALIENCY BASED TARGET DETECTION METHOD FOR HIGH-RESOLUTION SAR IMAGES

The traditional target detection methods for high-resolution synthetic aperture radar (SAR) images rely on the high intensity contrast between the targets and clutter, which are usually effective on the homogeneous background; however, they may lose effectiveness in the heterogeneous background. Compared with the clutter, the targets of interest usually have specific size and shape characteristics in high-resolution SAR images. Based on what mentioned above, we propose a target detection method based on the hierarchical saliency (HS) for high-resolution SAR images. The proposed method constructs the Bayesian saliency map to obtain the complete structures of both the targets of interest and strong clutter with superpixels as units firstly. Then, the morphological saliency map is constructed to retain the targets of interest while suppressing the strong clutter via the introduction of prior size information. The experimental results on the MiniSAR images show the effectiveness of the proposed target detection method.