Combine Meta-Learning with Feature Alignment for Cross-Domain Heterogeneous Hyperspectral Image Classification

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.