Self-Training Based Instance Calibration for Unsupervised Domain Adaptation Semantic Segmentation of Remote Sensing Images

Self-training based unsupervised domain adaptation approaches play a pivotal role in mitigating the domain shift and improving the segmentation performance of the target domain, where a trained model generates pseudo-labels for the target domain. However, how to improve the quality of the pseudo-labels of the target domain containing noise has received considerable critical attention. Moreover, remote sensing images have an imbalanced number of samples across classes. In this paper, we propose a self-training based instance calibration for unsupervised domain adaptation semantic segmentation of remote sensing images. Firstly, we encourage the different augmented results of the same pixel of the target domain images to not only have the same class prediction but also have the similar relationship with the pixel instances of the source domain. Secondly, variations related to the number of samples of different classes are added to the logits of the pixels to narrow the gap between the feature areas of different classes. Experiments on International Society for Photogrammetry and Remote Sensing (ISPRS) and Tibet Plateau remote sensing datasets demonstrate that the proposed model can effectively improve the quality of the pseudo-labels of the target domain and mitigate low segmentation performance of the target domain caused by the imbalanced number of samples across classes.