Region of Interest Extraction Based on Bayesian Joint Saliency Detection for Remote Sensing Images
Wanning Zhu, Libao Zhang, Yinggang Zhani · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Saliency detection is an essential tool to extract regions of interest (ROIs) in remote sensing (RS) images. However, many methods are applied to single image and cannot detect ROIs accurately due to the ignorance of high correlation among different RS images. Thus, we propose the Bayesian joint saliency detection method to extract ROIs. Firstly, we generate the prior saliency based on global color contrast according to co-clustering, which ensures that regions with similar features have the same saliency. Secondly, we produce the likelihood saliency by constructing intensity co-occurrence histogram, which can explore the intensity distribution of multiple images. Finally, due to the complex scenes in RS images, Bayesian enhancement strategy is applied to combine the prior saliency with the likelihood saliency, and obtain ROI with less background inference. Quantitative and qualitative experiments results indicate that our method outperforms competing methods and shows good performance in ROI extraction.