Prototype and Context-Enhanced Learning for Unsupervised Domain Adaptation Semantic Segmentation of Remote Sensing Images

In unsupervised domain adaptation (UDA) of remote sensing images (RSIs), the huge inter-domain discrepancies and intra-domain variances lead to complicated class-level relations. Specifically, the instances of the same class differ greatly while instances of different classes are similar, whether across different RSIs domains or within the same RSIs domain. However, existing methods cannot fully consider these problems, limiting the performance of UDA semantic segmentation of RSIs. To this end, this paper proposes a novel cross-domain multi-prototypes learning method, the core idea of which is to abstract the cross-and intra-domain class-level relations into multiple prototypes. Specifically, the multiple prototypes belonging to different classes can detailedly describe complex inter-class relations, and the multiple prototypes within the same class can better model rich intra-class relations. Further, the source and target samples are jointly used for prototypes calculation, to fully fuse the feature information of different RSIs. In a nutshell, utilizing the samples from different RSIs domains to learn multiple prototypes for each class can achieve better domain alignment at the class level. In addition, considering that RSIs simultaneously contain large targets with wide coverage and important small targets, two masked consistency learning strategies are designed to better explore the contextual structure of target RSIs and improve the quality of pseudo labels for prototype updating. The global consistency strategy can strengthen the utilization of global context relations, while the local consistency strategy can further improve the learning of local context details. Therefore, the proposed method is actually a prototype and context enhanced learning method for UDA semantic segmentation of RSIs. Extensive experiments demonstrate that the proposed method can achieve better performance than existing state-of-the-art UDA methods.