GINet:Graph interactive network with semantic-guided spatial refinement for salient object detection in optical remote sensing images

Abstract

There are many challenging scenarios in the task of salient object detection in optical remote sensing images (RSIs), such as various scales and irregular shapes of salient objects, cluttered backgrounds, etc. Therefore, it is difficult to directly apply saliency models targeting natural scene images to optical RSIs. Besides, existing models often do not give sufficient exploration for the potential relationship of different salient objects or different parts of the salient object. In this paper, we propose a graph interaction network (i.e. GINet) with semantic-guided spatial refinement to conduct salient object detection in optical RSIs. The key advantages of GINet lie in two points. Firstly, the graph interactive reasoning (GIR) module conducts information exchange of different-level features via the graph interaction operation, and enhances features along spatial and channel dimensions via the graph reasoning operation. Secondly, we designed the global content-aware refinement (GCR) module, which incorporates the foreground and background feature-based local information and the semantic feature-based global information simultaneously. Experiments results on two public optical RSIs datasets clearly show the effectiveness and superiority of the proposed GINet when compared with the state-of-the-art models.