SRANet: semantic relation aware network for semantic segmentation of remote sensing images

Remote sensing images contain complex feature information, and traditional convolutional networks cannot effectively model the contextual relationships. To address this problem, we propose a semantic segmentation network for remote sensing images based on semantic relationship aware. We construct the semantic relationship aware module to obtain the global semantic information of remote sensing images by self-attention. In addition, the separable space convergence pyramid module was constructed to effectively utilize the feature information in the high-level feature maps. By separable convolution with different dilation rates, the network can acquire multiscale semantic information. Our semantic relation aware network (SRANet) improves the overall accuracy by 0.33% over the benchmark network in the Vaihingen dataset and by 0.42% in the Potsdam dataset. The class activation maps show that the SRANet has ideal activation responses for targets at different scales in images. Furthermore, our SRANet can produce competitive segmentation performance compared with other state-of-the-art segmentation networks. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)