CoI2A: Collaborative Inter-domain and Intra-domain Alignments for Multisource Domain Adaptation
Chen Lin, Zhenfeng Zhu, Shenghui Wang, Zhenwei Shi, Yao Zhao · IEEE Transactions on Geoscience and Remote Sensing · 2023
In the remote sensing information interpretation tasks, compared with collecting lots of high-quality image labels for the target domain, a large amount of labeled remote sensing data from multiple source domains are generally available without any extra cost. In this paper, our work focuses on how to exploit the rich knowledge obtained from multiple source domains to guide the interpretation of the target scene, and we propose a novel framework called Collaborative Inter-domain and Intra-domain Alignments for multi-source domain adaptation, namely CoI2A, in which inter-domain and intra-domain alignments are well collaborated to reduce the distribution divergence across sources and target. To reduce the discrepancy across sources, the inter-source alignment is proposed to map multiple sources into a unified representation space. In addition, the cross-domain attention is introduced to enforce the intra-class compactness of the target. Inter-domain alignment aligns each source with target domain separately with the help of cross-domain attention. As for the intra-domain alignment, the multi-head attentive representations of the target obtained by cross-domain attention are correlated into a unified one. The experimental results obtained from different scene classification tasks demonstrate the superiority of our model.