TransHSI

Hyperspectral images (HSIs) classification research has seen some progress with the use of convolutional neural networks (CNNs). However, the limited ability of CNNs to capture deep global features and spectral sequence features presents a challenge. This paper addresses this is-sue by proposing a Transformer model based on the attention mechanism, which fully inte-grates CNNs to extract spectral-spatial features, resulting in the TransHSI classification model. TransHSI proposes a new spectral-spatial feature module, in which the spectral feature extraction module combines 3D CNN with different convolution kernel sizes and Transformer to extract global and local spectral features of HSI. And a fusion mechanism is proposed to cascade the ex-tracted spectral-spatial features and the original HSI after dimensionality reduction, making full use of the shallow and deep features of HSI. The network is optimized using the residual module.Experimental results on three public datasets Indian Pines, Pavia University and Data Fu-sion Contest 2018, show that compared with 11 other traditional and advanced HSI classification algorithms, TransHSI achieves the best overall accuracy and kappa coefficient in all three datasets.