Semantic Segmentation Method of Residential Areas in Remote Sensing Images Based on Cross-Attention Mechanism

Aiming at common problems such as high classification error rate, environmental noise interference, regional discontinuity, and structural absence in the semantic segmentation of residential areas, this paper proposes a CrossAtt-UNet architecture based on the Cross Attention mechanism. This network is based on the Att-UNet framework and innovatively proposes a Cross Attention module. Cross-level information features are extracted by establishing cross-associations on the feature map’s horizontal and vertical coordinate axes. It ensures the efficient utilization of computing resources and significantly improves the accuracy of semantic segmentation and the adjacency relationship of the target region. After many experimental verifications, this network architecture performs outstandingly on the semantic segmentation dataset of living areas, with an accuracy of 95.47%, an mAP (mean average precision) of 94.57%, an mIoU (mean intersection over union) of 89.80%, an F1-score of 94.63%, a train_loss (training loss) of 0.0878, and a val_loss (validation loss) of 0.1459. Its segmentation performance, area integrity, and edge recognition accuracy are higher than those of mainstream networks. The concrete damage detection experiment further indicates that this network has good generalization ability, demonstrating stable performance and robustness.