Hyperspectral Image Transformer Classification Networks

Hyperspectral image (HSI) classification is an important task in earth observation missions. Convolution neural networks (CNNs) with the powerful ability of feature extraction have shown prominence in HSI classification tasks. However, existing CNN-based approaches cannot sufficiently mine the sequence attributes of spectral features, hindering the further performance promotion of HSI classification. This article presents a hyperspectral image transformer (HiT) classification network by embedding convolution operations into the transformer structure to capture the subtle spectral discrepancies and convey the local spatial context information. HiT consists of two key modules, i.e., spectral-adaptive 3-D convolution projection module and convolution permutator (ConV-Permutator) to retrieve the subtle spatial-spectral discrepancies. The spectral-adaptive 3-D convolution projection module produces the local spatial-spectral information from HSIs using two spectral-adaptive 3-D convolution layers instead of the linear projection layer. In addition, the Conv-Permutator module utilizes the depthwise convolution operations to separately encode the spatial-spectral representations along the height, width, and spectral dimensions, respectively. Extensive experiments on four benchmark HSI datasets, including Indian Pines, Pavia University, Houston2013, and Xiongan (XA) datasets, show the superiority of the proposed HiT over existing transformers and the state-of-the-art CNN-based methods. Our codes of this work are available at https://github.com/xiachangxue/DeepHyperX for the sake of reproducibility.

Automated summary

This study proposes a hyperspectral image classification network that combines convolution neural networks with transformer structures, enabling the capture of subtle spectral differences and the conveyance of local spatial context information. Experimental results demonstrate that the proposed network outperforms existing transformers and state-of-the-art CNN-based methods on four benchmark datasets.