Cross-Layer Feature Fusion and Attention-Based Class Feature Alignment Network for Unsupervised Cross-Domain Remote Sensing Scene Classification.
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| Title: | Cross-Layer Feature Fusion and Attention-Based Class Feature Alignment Network for Unsupervised Cross-Domain Remote Sensing Scene Classification. |
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| Authors: | Wei, Jiahao1 (AUTHOR), Li, Erzhu1,2 (AUTHOR), Zhang, Ce1,2 (AUTHOR) ce.zhang@bristol.ac.uk |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p859. 22p. |
| Subjects: | Remote-sensing images, Encoding |
| Abstract: | Highlights: What are the main findings? Global distribution alignment alone is insufficient for cross-domain remote sensing scene classification; class-level feature misalignment is a critical yet overlooked factor limiting unsupervised domain adaptation performance. The proposed cross-layer feature fusion and attention-based architecture significantly enhances scene representation learning and enables effective class-aware feature alignment across domains. What are the implications of the main findings? Cross-domain adaptation in remote sensing should move beyond global feature distribution alignment and explicitly model class-level structures, as neglecting class-aware alignment can fundamentally limit generalization performance. Effective cross-domain scene classification requires joint optimization of multi-layer semantic representation and class-aware alignment, suggesting that future unsupervised domain adaptation architectures should integrate cross-layer feature fusion and adaptive attention mechanisms rather than relying on shallow feature matching. Remote sensing scene classification is one of the crucial techniques for high-resolution remote sensing image interpretation and has received widespread attention in recent years. However, acquiring high-quality labeled data is both costly and time-consuming, making unsupervised domain adaptation (UDA) an important research focus in scene classification. Existing UDA methods focus primarily on aligning the overall feature distributions across domains but neglect class feature alignment, resulting in the loss of critical class information. To address this issue, a cross-layer feature fusion and attention-based class feature alignment network (CFACA-NET) is proposed for unsupervised cross-domain remote sensing scene classification. Specifically, a multi-layer feature extraction module (MFEM) consisting of a cross-layer feature fusion module (CFFM), a multi-scale dynamic attention module (MSDAM), and a fused feature optimization module (FFOM) is designed to enhance the representation ability of scene features. A high-confidence sample selection module is further introduced, which utilizes evidence theory and information entropy to obtain reliable pseudo-labels. Finally, a class feature alignment module is proposed, incorporating a two-stage training strategy to achieve effective class feature alignment. Experimental results on three remote sensing scene classification datasets demonstrate that CFACA-NET outperforms existing state-of-the-art methods in cross-domain classification performance, effectively enhancing cross-domain adaptation capability. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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