Hierarchical Feature Progressive Alignment Network for Remote Sensing Image Scene Classification in Multitarget Domain Adaptation

Multi-target domain adaptation (MTDA) presents a formidable challenge in remote sensing image scene classification (RSICS), where the objective is to transfer knowledge from a labeled source domain to several unlabeled target domains. Compared to single-source-single-target domain adaptation (S3TDA), MTDA is inherently more complex due to domain shifts among multiple target domains. Directly merging the unique features of multi-target domains can result in corrupted information and poor classification performance. To address these challenges, we propose a hierarchical feature progressive alignment network (HFPAN) for RSICS in MTDA. Firstly, our method introduces a fine-grained and contextual information extraction network to extract the global-local correlation in remote sensing images. Secondly, we construct a hierarchical feature embedding framework that maintains hierarchical inter-intra constraints for the extracted features. Finally, we perform an alignment process for the constructed hierarchical features to minimize the differences in MTDA, progressing from coarse to fine granularity. To evaluate the efficacy of our proposed method, we conducted several cross-domain scene classification experiments on five public datasets. These experiments demonstrate the novelty of our approach and its ability to achieve improved classification performance.