Domain Adaptive Remote Sensing Image Segmentation Based on Hierarchical Attention

Remote sensing semantic image segmentation technology has significant applications in resource management, natural disaster management, and environmental monitoring and protection. However, different remote sensing image datasets often exhibit issues such as spectral confusion between different objects and spectral variations within the same object. These issues significantly reduce the generalization performance of deep learning models, and cross-domain performance degradation in remote sensing semantic image segmentation algorithms poses a significant challenge. To address these issues, optimizations are performed from two perspectives: neural network architecture and domain adaptation strategies. First, a TransConv network based on a hierarchical multihead self-attention mechanism and multiscale feature fusion is proposed. This network effectively enhances feature extraction and fusion capabilities through sliding window patching, multilayer self-attention modules, and a lightweight feedforward neural network, thereby improving the model's generalization performance. Second, a self-training-based domain adaptation technique is introduced, which optimizes the image input, model parameters, and learning process. As a result, labeled source domain knowledge is successfully transferred to the unlabeled target domain, significantly improving the segmentation performance in the target domain. Experimental results demonstrate that the improved TransConv network significantly outperforms other algorithms in terms of generalization performance. In addition, it excels in domain adaptation tasks with the self-training-based domain adaptation technique. The proposed approach thus enhances the accuracy and generalization capability of remote sensing image semantic segmentation, reduces the impact of erroneous pseudo-labels, and addresses the class imbalance problem, providing more reliable technical support for practical applications.