Adjacent Complementary Network for Salient Object Detection in Optical Remote Sensing Images

Salient object detection in optical remote sensing images (RSI-SOD) is a newly developing task and has attracted extensive attention. Although some satisfactory performances have been achieved, there are still some poor performance situations, such as complex backgrounds and objects with various scales. Because the existing methods have the issue of insufficient adjacent feature representation and inappropriate constraint supervision. To alleviate the issues, we attempt to utilize the adjacent complementary and introduce the global constraints. Accordingly, we propose the adjacent complementary network (ACNet) with global constraints for RSI-SOD. Specifically, in the encoder, we mainly design the adjacent complementary module (ACM) to complement the features among the current layer and adjacent layers. Therefore, it can effectively aggregate saliency features. Besides, we have equipped a global average pooling (GAP) to enhance global information. In the decoder, we design a fusion enhancement module (FEM) to enhance the feature conversion between the coding layer and decoding layer. Finally, we employ a novel loss, which introduces global constraints and can supervise the objects with various scales. Extensive studies on two benchmark datasets demonstrate that ACNet is competitive and outperforms the 15 state-of-the-art (SOTA) approaches.