Recurrent Adaptive Graph Reasoning Network With Region and Boundary Interaction for Salient Object Detection in Optical Remote Sensing Images

In the realm of optical remote sensing imagery, tackling the intricate task of detecting salient objects poses challenges that expose limitations in prevailing CNN- and transformer-based methodologies, particularly in dealing with intricate object topologies. To address this, we present a novel solution, the recurrent adaptive graph reasoning network (RAGRNet), tailored to capturing patterns of salient objects within complex and irregular remote sensing images. For the first time, our network orchestrates simultaneous modeling of both object regions and boundaries, ushering in a comprehensive comprehension of their intrinsic correlations. This pioneering approach involves the deployment of a multigranularity boundary-focused module (MGBF), which effectively crafts enriched boundary features. Going further, the recurrent adaptive graph reasoning (RAGR) module scrutinizes the semantic interplay between region and boundary representations, thereby fortifying the learning process of intricately structured object features. Concluding the process, a specialized decoder, termed the heterogeneous semantic cooperative interconnection (HSCI), systematically fuses region and boundary information in a progressively unified manner. This intricate interplay harnesses their synergistic complementarity to bolster the Precision and sophistication of modeling salient objects. In rigorous evaluations across diverse datasets, our proposed network outperforms 38 state-of-the-art (SOTA) methodologies, a testament to its remarkable efficacy. Code and results will be available at https://github.com/JieZzzoo/RAGRNet.