A Novel Saliency Detection Method for Lunar Remote Sensing Images

Abstract

The saliency detection provides an alternative methodology to semantic image understanding in many applications, for example, content-based image retrieval. To detect saliency for lunar remote sensing images, this letter proposes a crater feature model by analyzing the relationship between local interest points and saliency of lunar images. Based on the model, we propose a novel saliency detection method for lunar images. Our method merges and combines the speed-up robust feature features of the highlight region and shadow region of an impact crater to get the candidate regions of interest (ROI). Then, a descriptive feature vector is generated for each ROI, and the resulting saliency regions are distinguished from false detected and inconspicuous ones through a support vector machine. The method has been put into test on Chang'e-1 and Chang'e-2 lunar image data, and confirmed to be able to detect the salient region of impact craters correctly, with results much better than those obtained by the classical saliency detection method.