UNSUPERVISED DOMAIN ADAPTATION FOR SEMANTIC SEGMENTATION VIA SELF-SUPERVISION

Recently, deep learning (DL) methods have been widely used for semantic segmentation of remote sensing and achieved significant progress. However, DL-based methods are time-consuming and labor intensive for the networks requiring abundant data with accurate labeling. To solve this issue, unsupervised domain adaption (UDA) has recently been used to transfer the information from labeled source domain to unlabeled target domain. In this paper, we propose a novel UDA approach based on the self-supervised theory for remote sensing image. Specifically, we firstly utilize the inter-domain adaptation to reduce the gap between the source and target domain. Secondly, based on our proposed spatial-frequency (SF) index, we detach the target domain into an easy and hard split. Ultimately, we adopt the intra-domain adaptation by self-supervised adaptation to improve the performance of hard split. Experimental results on ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness and rationality of our methods against the other state-of-the-art approaches.