GLGAT-CFSL: Global-Local Graph Attention Network-Based Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Few-shot learning (FSL) is an effective approach to address the issue of limited labeled data in hyperspectral image classification (HSIC). However, it overlooks the domain shift between the source domain (SD) and the target domain (TD) in cross-domain tasks. Most existing domain adaptation (DA) methods alleviate the domain shift problem to some extent, but DA methods based on traditional convolutional operators overlook the nonlocal spatial relationships in HSI, while methods based on graph neural networks (GNNs), although effective in leveraging nonlocal spatial information for domain alignment, overly emphasize global relationships, which is disadvantageous for pixel-level classification in HSI. To solve these issues, this article proposes a novel globalp-local graph attention network-based cross-domain FSL (GLGAT-CFSL), which comprehensively reduces domain shift through global-to-local domain alignment. It has the following advantages: 1) an innovative dynamic triplet graph attention network is devised to identify nonlocal spatial relationships in HSI for global graph alignment (GGA) while also addressing common overfitting and oversmoothing issues in GNNs; 2) an ingenious local similarity learning (LSL) strategy is designed after global domain alignment, utilizing intradomain connectivity structures and interdomain node similarities for local DA, promoting cross-domain information propagation and more comprehensive reduction of domain shift; and 3) we propose a novel triaxial dynamic convolutional neural network (TDCNN) as the feature extractor, promoting cross-dimensional interaction between spectral and spatial dimensions, establishing a more generalizable and rich feature representation between the SD and the TD. The experimental results on three HSI datasets demonstrate the superiority and effectiveness of the proposed GLGAT-CFSL.