Semi-Supervised Hyperspectral Image Classification Based on Multiscale Spectral–Spatial Graph Attention Network

Hyperspectral image (HSI) classification with limited training samples remains a challenging task due to the high cost and time consumption of collecting labeled samples. In recent years, semi-supervised image classification methods have garnered widespread attention, as they address the scarcity of labeled samples and enhance classification accuracy by leveraging the inherent relationships between the labeled and unlabeled samples. In this letter, we introduce a semi-supervised classification method based on multiscale spectral-spatial graph attention network (MSSGAT). We construct multiple neighborhood graphs with different scales, where all samples (including training and testing samples) are represented as nodes and exploit the spectral-spatial features with different receptive fields. These features are then fed into the graph attention network (GAT) to learn attention coefficients between neighboring nodes and self-attention coefficients for target nodes, which are further aggregated to extract more discriminative features for enhancing HSI classification accuracy. The experimental results on Indian Pines, Salinas, and Pavia University datasets demonstrate its competitive performance. The overall accuracy (OA) scores are 99.36%, 99.59%, and 96.85%, respectively, which exceed the state-of-the-art (SOTA) models.