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Multiscale Sample Transformer for Hyperspectral Image Classification
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AI
DOI：10.1109/TGRS.2025.3642126.png)
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Abstract
En 中文
Hyperspectral image (HSI) captures hundreds of narrow bands, offering abundant spatial and spectral information across various application domains. In recent years, deep learning techniques have shown strong potential in HIS processing, with a range of models being explored. However, the fixed geometric structure of convolutional kernels in the CNN model impedes interactions between long-term dependencies, and existing transformer methods have not fully harnessed the spectral structural characteristics of HSI data. To address these issues, we proposed a multiscale sample transformer (MSST) that effectively extracts spatial and spectral features via two separate branches. In the spectral branch, the spectral sequence of the individual target pixel is used as an input sample, and a spectral transformer (SpecFormer) is proposed to comprehensively extract the spectral structural features. In the spatial branch, a 3-D tensor consisting of target pixel and their neighboring pixels is used as an input sample, dimension reduction is performed, and a spatial transformer (SpatFormer) is designed to efficiently extract the deep spatial features of HSI data. Consequently, MSST enlarges the receptive field and captures the comprehensive joint spatial–spectral features. Extensive experiments conducted on five widely used HSI datasets demonstrate the superior classification performance of the proposed framework. Our source codes will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhwt-xidian/Multi-Scale-Samples-Transformer_MSST</uri>
Keywords:
Convolution neural network (CNN)
hyperspectral image (HSI) classification
spatial–spectral transformer (SpecFormer)
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2.1W
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10.7W