Cosaliency Detection and Region-of-Interest Extraction via Manifold Ranking and MRF in Remote Sensing Images

Saliency-based region-of-interest (ROI) extraction is significant for the interpretation of remote sensing images (RSIs). Recently, cosaliency detection has shown its superiority of better extraction of common ROIs by using both intraimage and interimage cues. However, most existing methods still suffer from the complex backgrounds of RSIs, resulting in incomplete ROI extraction, many false positives, and blurred boundaries. In this article, we propose a cosaliency detection framework via manifold ranking and the Markov random field (MRF) for RSIs to address these problems. First, we design a two-stage manifold ranking schema for converting single-image saliency maps (SISMs) to multi-image saliency maps (MISMs). This step takes full advantage of the correlation between images to improve the integrity of ROIs and reduce false positives. Second, we locally fuse saliency proposals by minimizing the energy function in an MRF. The design of the energy function comprehensively considers the global and local performance of saliency proposals to assign appropriate fusion weights. Finally, we generate the ROI masks by thresholding the cosaliency maps. Our approach is evaluated on four RSI datasets and compared to the state-of-the-art methods. Experimental results demonstrate the effectiveness of our model in both cosaliency detection and ROI extraction.