Edge Complementary Multi-Scale Aggregation Network for Salient Object Detection in Optical Remote Sensing Images

In recent years, salient object detection (SOD) has attracted more and more attention. However, the SOD in remote sensing images (RSI-SOD) faces various issues, including large scene span, cluttered background and changeable object scale. To address these challenges, an edge complementary multi-scale aggregation network (ECMANet) is proposed in this paper. Specifically, a multi-scale feature aggregation module (MFAM) is designed to extract hierarchical multi-scale information and reduce the noise interference of different scale information. In addition, foreground edge guidance module (FEGM) is designed to cross-refine foreground information and edge information. Finally, the foreground, edge, and background are generated by background-foreground fusion module (BFFM) to complement the overall network information. Extensive experiments are conducted on two popular datasets demonstrate that the proposed method outperforms other state-of-the-art methods.