TCM-Net: Mixed Global–Local Learning for Salient Object Detection in Optical Remote Sensing Images

Abstract

Deep-learning methods have made significant progress for salient object detection in optical remote sensing images (ORSI-SOD). However, it is difficult for existing methods to effectively exploit both the multi-scale global context and local detail features due to the cluttered background and different scales that characterize ORSIs. To solve the problem, we propose a transformer and convolution mixed network (TCM-Net), with a U-shaped codec architecture for ORSI-SOD. By using a dual-path complementary network, we obtain both the global context and local detail information from the ORSIs of different resolution. A local and global features fusion module was developed to integrate the information at corresponding decoder layers. Furthermore, an attention gate module was designed to refine features while suppressing noise at each decoder layer. Finally, we tailored a hybrid loss function to our network structure, which incorporates three supervision strategies: global, local and output. Extensive experiments were conducted on three common datasets, and TCM-Net outperforms 17 state-of-the-art methods.