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

Wang Miao, Wenqi Han, Jie Geng, Wen Jiang

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

Multitarget 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 multitarget 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. First, our method introduces a fine-grained and contextual information extraction (FCIE) network to extract the global-local correlation in remote sensing (RS) images. Second, we construct a hierarchical feature embedding (HFE) 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.

源语言英语
文章编号5603013
页(从-至)1-13
页数13
期刊IEEE Transactions on Geoscience and Remote Sensing
62
DOI
出版状态已出版 - 2024

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