Papers › 3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification

3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification

20 Apr 2024arXiv:2404.13252archive 2025-07-28

Shyam Varahagiri, Aryaman Sinha, Shiv Ram Dubey, Satish Kumar Singh

In recent years, Vision Transformers (ViTs) have shown promising classification performance over Convolutional Neural Networks (CNNs) due to their self-attention mechanism. Many researchers have incorporated ViTs for Hyperspectral Image (HSI) classification. HSIs are characterised by narrow contiguous spectral bands, providing rich spectral data. Although ViTs excel with sequential data, they cannot extract spectral-spatial information like CNNs. Furthermore, to have high classification performance, there should be a strong interaction between the HSI token and the class (CLS) token. To solve these issues, we propose a 3D-Convolution guided Spectral-Spatial Transformer (3D-ConvSST) for HSI classification that utilizes a 3D-Convolution Guided Residual Module (CGRM) in-between encoders to "fuse" the local spatial and spectral information and to enhance the feature propagation. Furthermore, we forego the class token and instead apply Global Average Pooling, which effectively encodes more discriminative and pertinent high-level features for classification. Extensive experiments have been conducted on three public HSI datasets to show the superiority of the proposed model over state-of-the-art traditional, convolutional, and Transformer models. The code is available at https://github.com/ShyamVarahagiri/3D-ConvSST.

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Classification Hyperspectral Image Classification Image Classification image-classification

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Methods

Absolute Position Encodings Adam Attention Average Pooling BPE Dense Connections Dropout Global Average Pooling Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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