HyUniDA: Breaking Label Set Constraints for Universal Domain Adaptation in Cross-Scene Hyperspectral Image Classification
Qingmei Li, Yibin Wen, Juepeng Zheng, Yuxiang Zhang, Haohuan Fu
Abstract
Although enormous Domain Adaptation (DA) approaches have been proposed for cross-scene hyperspectral image (HSI) classification, majority DA methods strongly depend on much prior knowledge of the association among the label sets of source and target domains (encompassing closed set, partial and open set DA), thereby significantly hindering their applications. Realistic application scenarios often require knowledge transfer between domains without restrictions on the label space, which is called Universal Domain Adaptation (UniDA). In this paper, we propose HyUniDA, which is the first attempt to address UniDA scenario from HSIs. HyUniDA contains two major parts: the Shared Semantic Pairing (SSP) and Domain Similarity Score (DSS). We group both source and target domains to form discriminative clusters. The SSP identifies pairs of clusters that have coincident semantic features as the common classes. By examining the consistency level of samples across source and target domains, DSS can estimate the quantity of target clusters and generate distinct clusters without prior knowledge. Meanwhile, we apply the contrastive domain discrepancy to alleviate the offset of samples distribution, with a representative regularizer to assist distinguish target domain clusters. We evaluate our proposed method on three transfer learning tasks for six typical HSI datasets, it turns out that our proposed method yields 3.83%~37.57% improvements compared to other state-of-the-art DA methods.