A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2604.23622v1
• Date: Sun, 26 Apr 2026 09:30:27 GMT
• Title: A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification
• Abstract summary: In hyperspectral image (HSI) classification, each pixel is categorized into a specific land-cover category or material.<n>Recent works have utilized a multi-scale vision transformer (ViT) to enhance spectral feature capture and yield promising results.<n>We propose a synergistic CNN-Transformer network with pooling attention fusion for HSI classification.
• Score: 10.687430702802608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the hyperspectral image (HSI) classification task, each pixel is categorized into a specific land-cover category or material. Convolutional neural networks (CNNs) and transformers have been widely used to extract local and non-local features in HSI classification. Recent works have utilized a multi-scale vision transformer (ViT) to enhance spectral feature capture and yield promising results. However, most existing methods still face challenges in the effective joint use of spatial-spectral information and in preserving information across layers during the propagation process. To address these issues, we propose a synergistic CNN-Transformer network with pooling attention fusion for HSI classification, which collaboratively utilizes CNNs and ViT to process spatial and spectral features separately. Specifically, we propose a Twin-Branch Feature Extraction (TBFE) module, which employs 3D and 2D convolution in parallel to comprehensively extract spectral and spatial features from HSI. A hybrid pooling attention (HPA) module is designed to aggregate spatial attention. Moreover, a cascade transformer encoder is employed for global spectral feature extraction, and a simple yet efficient cross-layer feature fusion (CFF) module is designed to reduce the loss of crucial information in the previous network layers. Extensive experiments are conducted on several representative datasets to demonstrate the superior performance of our proposed method compared to the state-of-the-art works. Code is available at https://github.com/chenpeng052/SCT-Net.git.

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