S&GDA: An Unsupervised Domain Adaptive Semantic Segmentation Framework Considering Both Imaging Scene and Geometric Domain Shifts

Abstract

Unsupervised domain adaptation uses labeled data from a source domain to help learn a target domain without any labeled data. Previous studies have not systematically analyzed the causes of remote sensing (RS) domain shifts, making it difficult to effectively model domain shifts caused by differences in geographic scene and platform imaging positions and attitudes. Therefore, this study conducts detailed analysis of the causes of domain shifts in RS images, and an unsupervised domain adaptive semantic segmentation framework, called S&GDA, that considers both imaging scene and geometric domain shifts is proposed. S&GDA comprised two modules: imaging scene simulation and imaging geometric simulation modules. The imaging scene simulation module is instrumental in mitigating domain shifts in geographical scenes due to variations in natural and human factors, thereby achieving cross-domain imaging scene consistency. Meanwhile, the imaging geometric simulation module allows for accurate simulation of domain shifts caused by changes in the position and attitude of a platform, ensuring cross-domain imaging geometry consistency. Note that none of these modules add additional parameters or computational complexity to the model as they only work on the input side of the data. Comprehensive experiments are conducted on the LoveDA and ISPRS datasets to evaluate S&GDA. Results indicate that S&GDA outperforms the state-of-the-art (SOTA) unsupervised domain adaptive semantic segmentation method by 3.12% of mIoU and can achieve 85% of the performance of the fully supervised method.