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The salient region, which is a basic feature in the early stage of the human vision system, has been utilized to solve the problems of image analysis and interpretation nowadays. Although there are several salient-region detection methods for optical images, it is a hard work for synthetic aperture radar (SAR) images, which has large multiplicative speckle noise. Based on the statistical distribution of speckle noise and the local intensity variation, this letter presents a novel multiple-scale salient-region detection method for intensity SAR images. In this method, via constructing a 2-D local-intensity-variation histogram, the self-dissimilarity metric curve over scale is computed first to determine the saliency of the local region and its salient scale. Then, based on the Gamma statistical distribution of speckle noise, a new local complexity metric is proposed to obtain the saliency metric at the salient scale. After collecting all the salient regions in the image, a simple iterative algorithm is presented to refine the stable salient regions. Experimental results show the noise robustness, the accuracy, and the stability of the proposed method for SAR images.
Zhang et al. (Fri,) studied this question.