REGION OF INTEREST EXTRACTION BASED ON CO-SALIENCY ANALYSIS AND FEEDBACK STRATEGY FOR REMOTE SENSING IMAGES

Saliency analysis has been revealed an effective method to extract the region of interest (ROI) in remote sensing images. However, most existing saliency detection methods mainly focus on extracting the ROIs from a single image, which usually are not able to generate satisfactory results because of the background interference of remote sensing images. The employment of co-saliency detection which focuses on detecting common salient objects in a set of images can provide an effective solution to this issue. In this paper, we propose a novel ROI extraction model based on co-saliency analysis and feedback strategy for remote sensing images. We combine the bottom-up measures including consistency, central prior and contrast on spectral and texture feature of multiple images together, with subsequent adjusting operation by feedback strategy to enhance the common salient objects. Experiment results reveal that our model outperforms seven state-of-the-art models.