  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.
