DCG-Net: Enhanced Hyperspectral Image Classification with Dual-Branch Convolutional Neural Network and Graph Convolutional Neural Network Integration

Abstract

In recent years, graph convolutional neural networks (GCNs) and convolutional neural networks (CNNs) have made significant strides in hyperspectral image (HSI) classification. However, existing models often encounter information redundancy and feature mismatch during feature fusion, and they struggle with small-scale refined features. To address these issues, we propose DCG-Net, an innovative classification network integrating CNN and GCN architectures. Our approach includes the development of a double-branch expanding network (E-Net) to enhance spectral features and efficiently extract high-level features. Additionally, we incorporate a GCN with an attention mechanism to facilitate the integration of multi-space scale superpixel-level and pixel-level features. To further improve feature fusion, we introduce a feature aggregation module (FAM) that adaptively learns channel features, enhancing classification robustness and accuracy. Comprehensive experiments on three widely used datasets show that DCG-Net achieves superior classification results compared to other state-of-the-art methods.