Cross-Domain Meta-Learning Under Dual-Adjustment Mode for Few-Shot Hyperspectral Image Classification

Hyperspectral image (HSI) classification with limited training samples has been well studied in recent years. Among them, the few-shot learning (FSL) technique demonstrates excellent processing capability under limited labeled samples. Nevertheless, the current FSL-based works provide scarce attention to effective class prototypes and metric types, resulting in high generalization error and poor interpretation during the cross-domain testing phase. A dual-adjustment mode-based cross-domain meta-learning (DMCM) method for few-shot HSI classification is proposed to tackle this issue. Specifically, a three-dimensional ghost attention network (TGAN) with strong learning capability without massive parameters is first constructed. Meanwhile, a dual-adjustment mode comprising intracorrection (IC) and interalignment (IA) learning strategies is then adopted to solve domain shift issue via episode-level meta-tasks, where IC and IA focus on effective class prototypes and data distribution differences between domains, respectively. Afterward, considering that the traditional Euclidean distance metric is insensitive to the distribution of within-class samples, the class-covariance metric (CCM) is employed to account for the distribution in feature space of each class to optimize decision boundary and alleviate the misclassification problem. Extensive experiments on three publicly available target hyperspectral datasets demonstrate the effectiveness of the proposed method in comparison with other state-of-the-art (SOTA) methods. The codes will be available on the website at https://github.com/HlEvag/DMCM.git.