3D convolution attention-based multi-scale fusion network for hyperspectral image classification

doi:10.3389/frsen.2026.1812291

3D convolution attention-based multi-scale fusion network for hyperspectral image classification

2026-05-20

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DOI：10.3389/frsen.2026.1812291

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Deep learning (DL) has significantly advanced pattern recognition and hyperspectral image (HSI) classification owing to its strong capability for hierarchical feature representation. However; existing DL-based HSI classification methods are often limited by scarce labeled samples; high parameter complexity; and the difficulty of learning discriminative features from high-dimensional spectral-spatial data. To address these challenges; this paper proposes a 3D convolution attention-based multi-scale fusion network (3D-CAMFN) for HSI classification. Specifically; a 3D convolutional attention block is first designed to jointly capture spatial-spectral dependencies; enabling the network to adaptively emphasize informative spectral channels while suppressing redundant or noisy responses. Then; shallow; middle; and deep features extracted from different network depths are integrated through a multi-scale fusion mechanism; which effectively balances fine spatial details and high-level spectral abstractions. Finally; a broad learning system (BLS) is introduced to replace the conventional deep classifier head. By using random feature mapping and pseudoinverse-based analytical solutions instead of time-consuming back-propagation; BLS reduces trainable parameters and improves training efficiency. Experimental results on three widely used HSI datasets; namely Salinas (SA); Indian Pines (IP); and WHU-Hi-HanChuan (WHU-HC); show that the proposed 3D-CAMFN achieves competitive or superior classification performance compared with several recent state-of-the-art methods. These results demonstrate that the proposed framework can effectively enhance spatial-spectral feature representation while maintaining computational efficiency; making it suitable for accurate and efficient HSI classification.

Keywords:

attention

multi-scale feature fusion

3D convolution

broad learning system (BLS)

hyperspectral image (HSI) classification

3D convolution attention-based multi-scale fusion network for hyperspectral image classification

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