Common Regions of Interest Extraction Based on Saliency Statistic Analysis for Multiple Remote Sensing Images

Various landscape characteristics and irregular object boundaries often make object extraction more difficult. Automated analysis of remote sensing (RS) images is challenging and saliency detection is an effective solution. Yet, many traditional algorithms emphasize simply on a single image and would, therefore, neglect the similarity of an image set. In this paper, concerning the relationships among images, a region of interest extraction model based on common features analysis for remote sensing images is proposed. Firstly, multi-image saliency maps, showing the common salient objects, are generated by clustering in RGB and CIELab color spaces. Next, a method, highlighting the salient region, is based on global and local saliency statistics analysis. Finally, regions of interest are segmented from original images according to saliency maps which have been made boundaries holding by superpixels. Experimental evaluation shows that compared with six existing models, we get more accurate saliency maps.