DA&MTSS: An End-to-End Remote Sensing Image Domain Adaptive Semantic Segmentation Framework Combining Data Augmentation and Mobile Threshold Self-supervision

The application of deep learning-based semantic segmentation in remote sensing (RS) images has achieved considerable success. However, many supervised methods still heavily rely on a large amount of labeled data, requiring time-consuming and labor-intensive manual annotations. Besides, networks trained on labeled source domain data often perform poorly in inference tasks with target domain data due to the domain shift phenomenon. To address these challenges, we construct a novel end-to-end Unsupervised Domain Adaptation (UDA) framework, named DA&MTSS, which integrates data augmentation with self-supervision. Specifically, we analyze the common factors that cause domain shifts in RS images and adopt different data augmentation techniques to attenuate the domain shift and enhance the generalization ability and robustness of the network in cross-domain inference. In the self-supervision phase, we design a new sample-based mobile threshold method to dynamically control the thresholds of both dominant and long-tail classes during training and generate stable pseudo-labels. Therefore, our method eliminates the need for additional training or expert knowledge and achieves the co-evolution of network parameters and pseudo-label quality in the training process. The results of comprehensive experiments on five tasks across the ISPRS Vaihingen, Potsdam, and LoveDA datasets demonstrate that this method consistently achieves higher mIoU scores, showcasing the performance advantage of DA&MTSS in UDA for the semantic segmentation of RS image.