Multiple-Scale Salient-Region Detection of SAR Image Based on Gamma Distribution and Local Intensity Variation

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.