首页|Global–Local Multigranularity Transformer for Hyperspectral Image Classification
Global–Local Multigranularity Transformer for Hyperspectral Image Classification
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NETL
NSTL
IEEE
Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local features, but they are deficient in capturing global contextual information. Recently, transformer has become proficient in attending to global information due to their self-attention mechanisms, yet they may fall short in capturing multiscale features of HSI. To address these limitations, a global–local multigranularity transformer (GLMGT) network is proposed for HSI classification. The GLMGT combines CNN with the transformer to comprehensively capture multigranularity spectral and spatial features across global and local scales. Specifically, we introduce a multigranularity spatial feature extraction block to extensively extract spatial information at different granularities, including multiscale local spatial features and global spatial features. In addition, we introduce a multigranularity spectral feature extraction block to fully leverage spectral information across different granularities. The validity of the proposed method is demonstrated through experimental validation using seven publicly available datasets, which include two Chinese satellite hyperspectral datasets (ZY1-02D Huanghekou and GF-5 Yancheng) and one UAV-based hyperspectral dataset.
School of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an, China
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China
Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
NETL
NSTL
IEEE
