Hybrid Attention‐Based DeepLabV3+ Network for Semantic Segmentation of Land Cover on Remote‐Sensing Imagery

Land cover segmentation is a prominent issue of remote‐sensing imaging that has attracted significant attention. Aiming at problems such as the unbalanced distribution of land cover categories, missing boundary information, and misclassification during extraction, a novel DeepLabV3+ semantic segmentation network based on hybrid attention mechanisms is proposed. Firstly, the feature extraction part mainly uses ResNet and spatial pyramid pooling modules to enhance the generalization ability. Then, an improved BiFormer module with a gated control mechanism is designed to improve the feature dependency relationship and to strengthen the representation of deep features. For the decoder, an attention merging module adopting coordinate attention and squeeze aggregated excitation layers is proposed to mitigate the issue of information loss and further boost feature reflection capacity. Then, a joint loss function, combining cross‐entropy loss and Dice loss, is implemented to alleviate category imbalance and stabilize parameter training. Experimental results demonstrate that the improved DeepLabV3+ model outperforms others in terms of objective evaluation in the semantic segmentation of land cover in remote‐sensing imagery. This approach not only reinforces the robustness of the model but also facilitates easier adaptation to diverse application tasks.