Multistrategy Region and Boundary Interaction Network for Salient Object Detection in Optical Remote Sensing Images

Based on current theoretical insights and ideas in salient object detection in optical remote sensing images (RSI-SOD), the utilization of boundaries typically involves enhancing object features through boundary-guided assistance. However, this initial approach overlooks the intrinsic connections between regions and boundaries, which are essential for capturing rich contextual features and refining boundary details from a global perspective, thereby providing robust support for the mutual enhancement of information. To further capture the complex relationships between regions and boundaries, we propose a novel RSI-SOD network, the Multi-Strategy Region and Boundary Interaction Network (MRBINet), which employs multiple strategies to analyze potential key information of the objects. Specifically, we design the Boundary-Region Split Module (BRS) to decompose image information and obtain features of regions and boundaries. Based on the obtained information, the Boundary-Guided Reasoning Refinement Module (BGRR) and the Multi-Source Dynamic Cross-Attention Module (MDC) are employed to explore intrinsic connections between regions and boundaries through graph reasoning and interactive attention, respectively, during the SOD process, effectively constraining diffuse regional information through robust boundary guidance. Finally, we utilize the Collaborative Enhancement Cascade Decoder Module (CECD) to recombine and reinforce the refined regions and boundaries, aiming to restore the intrinsic features of the target object. Extensive experiments on three benchmark datasets demonstrate that our method outperforms classical approaches, showing competitive performance. Furthermore, the intrinsic features enhancement of region and boundary contributes to the performance improvement of the RSI-SOD method. The code and results for this work can be found at https://github.com/JieZzzoo/MRBINet.