Hyperspectral image classification with embedded linear vision transformer

Hyperspectral image consists of multiple contiguous spectral bands, which are crucial for precise land classification. In earlier studies, convolutional neural network has emerged as effective methods for hyperspectral image classification due to their powerful feature extraction capabilities. Recently, vision transformers have been applied in the field of hyperspectral image classification. However, most existing transformer methods mainly focus on global relationships, lacking the ability to capture multiscale features, leading to subpar classification performance. To address this issue, this paper proposes a hyperspectral image classification with embedded linear vision transformer (ELViT). Firstly, the ELViT employs a token generator to embed multiscale semantic tokens, providing the model with richer image features by leveraging the local representation capability of convolutional neural network. Then, We propose a transformer with linear complexity designed to capture global correlations between different tokens, allowing the model to prioritize distinctive feature information. Additionally, a gated linear unit activation function is utilized to supplement the establishment of long-range relationships in the transformer. Experimental results demonstrate that ELViT outperforms both convolutional neural network based and transformer based methods, achieving excellent classification performance with overall accuracies of 98.43%, 99.67%, and 99.27% on the Pavia University, Salinas Valley, and WHU-Hi-LongKou datasets, respectively.