Hyperspectral image classification with embedded linear vision transformer
Document Type
Article
Publication Date
1-1-2025
Abstract
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.
Keywords
Hyperspectral image classification, Convolutional neural network, Linear vision transformer, Gated linear unit activation function
Divisions
universiti
Publication Title
Earth Science Informatics
Volume
18
Issue
1
Publisher
Springer Heidelberg
Publisher Location
TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY