Global–Local Residual Fusion Network for Hyperspectral Image Classification

As hyperspectral images (HSIs) continue to increase in data resolution and information richness, current deep learning models need to enhance their feature extraction and understanding capabilities for classification tasks. The complementarity between convolution and attention mechanisms in deep learning enables the capture of both local and global information. However, it faces the problems of the intrinsic coupling of different operators and the precise fusion of different features. In this study, a novel global–local residual fusion network (GLRFNet) is proposed to improve the HSI classification. First, a feature projection with multiple kernels is designed to generate the feature pool before deep extraction and enhance the information connection between operators. Then, a global–local residual (GLR) feature extraction network is built to capture both fine-grained details and large-scale dependencies, improving the feature perception in various classification scenes. It consists of local convolution, global attention, and residual construction branches in a separate and coupled manner. Finally, an optimized inverted bottleneck fusion (IBF) module is built to perform nonlinear and comprehensive feature fusion for mining the high-level semantics and understanding the category relationships. The experiments on four HSI datasets demonstrate the superiority of GLRFNet compared to other state-of-the-art methods, especially its classification performance under small sample conditions, with higher recognition accuracy and better class boundaries. In addition, parameter analysis and ablation experiments are also conducted to determine the optimal parameters and verify the module effectiveness.