Global-local graph convolutional broad network for hyperspectral image classification

The conventional broad learning system (BLS) struggles to represent the complex nonlinear features of hyperspectral images (HSI) due to its reliance on linear sparse feature extraction methods. Additionally, traditional BLS models focus primarily on class separability, ignoring the manifold structure that characterizes relationships between samples. To address these issues, previous research has incorporated graph convolutional networks (GCNs) and manifold learning into the BLS framework, but these methods often emphasize only local manifold structures, overlooking global structural information. In this paper, we propose a Global-Local Graph Convolutional Broad Network (GLGBN) for HSI classification. GLGBN addresses both global and local manifold structures, optimizing the classification boundary by minimizing local scatter and maximizing global scatter. It uses linear discriminant analysis (LDA) to preserve global manifold structure and locality preserving projections (LPP) to model local relationships via a Laplacian graph. This dual approach ensures that similar samples remain close while dissimilar samples are separated, enhancing classification accuracy. The proposed GLGBN model demonstrated outstanding overall accuracy across multiple public datasets: 95.31% on Indian Pines, 97.67% on Pavia University and 98.37% on Salinas, surpassing several classical and state-of-the-art approaches.