Heterogeneous Feature Collaboration Network for Salient Object Detection in Optical Remote Sensing Images

In recent years, the task of salient object detection in optical remote sensing images (RSI-SOD) has gained increasing interest. Despite some advancement in current methods, challenges such as the irregular topology of salient objects and cluttered backgrounds in optical RSI remain. To tackle these issues, we propose a novel heterogeneous feature collaboration network (HFCNet). Specifically, we design a new hybrid heterogeneous encoder that combines CNN and transformer to extract a set of heterogeneous features, famous in modeling local and global information, respectively. Subsequently, the adaptive global-local integration (AGLI) module is devised to integrate these complementary heterogeneous features through our feature alignment methods at global and local levels, so the global irregular topology structure and local details can be well-modeled. Furthermore, the proposed saliency-guided attention enhanced decoder (SGAED) leverages deep salient cues to guide the shallow decoders to pay more attention to important areas and suppress irrelevant areas, reducing the interference of cluttered backgrounds. Extensive experiments on three benchmark datasets have confirmed the significant superiority of our method compared with 18 state-of-the-art methods. All codes and results of our method are available at https://github.com/xumingzhu989/HFCNet-TGRS.