A Semantic Segmentation Method of Remote Sensing Image Based on Feature Fusion and Attention Mechanism

Current methods for semantic segmentation of remote-sensing images, especially for irregular and small targets, often result in low precision and incomplete feature extraction. To address this issue, an improved semantic segmentation method was developed utilizing DeepLabv3+. First, DeepLabv3+ is combined with the proposed feature fusion module to make full use of the complementary information of low- and high-level features. Second, the channel attention module helps extract effective features while suppressing irrelevant features, thereby enabling the extraction of more meaningful global information from high-level features. Finally, rich spatial information is selected using guided spatial attention, which improves the accuracy of edge segmentation of target objects. The results of the comparison show that the mean F1 score (MF1) and overall accuracy (OA) of the proposed method on the ISPRS Potsdam dataset are 89.81% and 88.45%, respectively. The MF1 of the proposed method is 89.90% and the OA is 89.14% for the UAVid dataset, which are higher than those of the other comparison algorithms. The proposed method exhibits superior semantic segmentation capabilities for remote-sensing images.