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Ensemble graph Laplacian-based anomaly detector for hyperspectral imagery

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Abstract

Hyperspectral anomaly detection is an alluring topic in hyperspectral image processing. As one of the most famous hyperspectral anomaly detection algorithms, Reed-Xiaoli detector is widely studied since it is understandable and easy to implement. However, the estimation of inverse covariance matrix may be time-consuming and easily corrupted by the anomalies. To solve these problems, we propose a novel ensemble graph Laplacian-based anomaly detector which comprises two main steps. Firstly, a multiple random sampling strategy is applied to improve the detection accuracies and robustness. Secondly, we can obtain multiple detection results through a graph Laplacian-based solution, and these results are further fused through ensemble learning. Experimental results on one simulated and two real hyperspectral datasets demonstrate the superiority of the proposed method.

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Data available on request from the authors.

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Author notes

  1. Haojie Hu and Danyao Shen contributed equally to this work.

Authors and Affiliations

  1. Xi’an Research Institute of High Technology, Xi’an, 710025, China

    Haojie Hu, Danyao Shen, Shuai Yan, Fang He & Jiaxin Dong

  2. School of Information and Communication, National University of Defense Technology, Wuhan, 430019, China

    Shuai Yan

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  1. Haojie Hu
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  2. Danyao Shen
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  3. Shuai Yan
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  4. Fang He
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Correspondence to Fang He.

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Hu, H., Shen, D., Yan, S. et al. Ensemble graph Laplacian-based anomaly detector for hyperspectral imagery. Vis Comput 40, 201–209 (2024). https://doi.org/10.1007/s00371-023-02775-4

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