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

Bei Cheng, Zao Liu, Chengbiao Fu, Tao Shen, Zhengzhou Li

Abstract: 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.