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Dictionary cache transformer for hyperspectral image classification

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Abstract

The spectral anomalies, limited training samples, and noisy training labels pose significant challenges to accurately classifying hyperspectral images (HSIs). To address these issues, we propose a novel dictionary cache transformer (DiCT) for HSIs classification, leveraging a combination of a group self-attention mechanism and a dictionary cache module. Specifically, the group self-attention mechanism binds the features local to the pixel into a group, thus alleviating the interference caused by pixel spectral anomalies. Furthermore, we capture representative structural information from different samples using their discriminative features to construct the dictionary cache module. The dictionary cache module enhances features by fusing sample features and the most similar element in the dictionary cache, thus improving the model’s resilience to noisy training labels. Experiments on five HSIs datasets demonstrate the proposed DiCT’s superiority in classification performance and robustness to noisy training labels.

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Acknowledgements

The authors would like to express their gratitude to the reviewers for their insightful remarks and ideas on how to improve the paper’s quality.

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 11871104 and 12131006).

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Heng Zhou & Qiaoyu Ma

  2. Department of Statistics and Epidemiology, Graduate School of the PLA General Hospital, Beijing, 100853, China

    Xin Zhang

  3. Beijing Zhongdi Runde Petroleum Technology Co., Ltd., Beijing, 100083, China

    Chunlei Zhang

  4. School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, China

    Yanan Jiang

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  1. Heng Zhou
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  2. Xin Zhang
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  3. Chunlei Zhang
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Correspondence to Xin Zhang.

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Zhou, H., Zhang, X., Zhang, C. et al. Dictionary cache transformer for hyperspectral image classification. Appl Intell 53, 26725–26749 (2023). https://doi.org/10.1007/s10489-023-04934-5

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