Combine Meta-Learning with Feature Alignment for Cross-Domain Heterogeneous Hyperspectral Image Classification
Date
Authors
Ye, Minchao
Jin, Yuheng
Zhao, Jianwei
Yan, Weiqi
Qian, Yuntao
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The scarcity of labeled samples results in the challenge of small-sample-size in hyperspectral image (HSI) classification. Transfer learning offers hope for solving this problem. In cross-domain transfer learning, the source domain boasts abundant labeled training samples, whereas the target domain comprises only limited labeled training samples. Leveraging the information from the source domain can benefit the classification of the target domain. However, inconsistencies in land-cover classes between source and target domains may hinder knowledge transfer between domains. Fortunately, few-shot learning (FSL) provides an effective solution to this challenge. In recent years, meta-learning has gained widespread attention as a mainstream approach within FSL. This paper proposes a novel method for cross-domain heterogeneous HSI classification, called cross-domain meta-learning with feature alignment (CD-MFA). CD-MFA enhances the generalization performance of the inner-loop optimization by incorporating task-adaptive loss function. The adaptive weighting strategy is used in the outer-loop optimization to balance the classification losses of the source and target domains to learn more discriminative features. Additionally, by aligning the features of the source and target domains under the guidance of the Gaussian prior, the impact of domain shift can be mitigated. It is worth noting that CD-MFA is trained concurrently on both the source and target domains so that the two domains are will bound, thereby enhancing the effectiveness of knowledge transfer. Experimental results on four publicly available HSI datasets validate the effectiveness of CD-MFA.Description
Keywords
0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering, Geological & Geomatics Engineering, 37 Earth sciences, 40 Engineering, Cross-domain heterogeneous hyperspectral image classification, meta-learning, task-adaptive loss function, adaptive weighting strategy
Source
IEEE Transactions on Geoscience and Remote Sensing, ISSN: 0196-2892 (Print); 1558-0644 (Online), Institute of Electrical and Electronics Engineers (IEEE), 1-1. doi: 10.1109/tgrs.2026.3652354
Publisher's version
Rights statement
This is the Author's Accepted Manuscript of an article published in IEEE Transactions on Geoscience and Remote Sensing. The Version of Record will be available at DOI: 10.1109/tgrs.2026.3652354