Electronics Optics & Control, Volume. 29, Issue 2, 16(2022)

Hyperspectral Image Anomaly Detection Based on Improved RX Incremental Learning

BAI Yu1... LIU Lina1, ZHANG Ning2, LIN Chen2, SONG Wei2 and ZHU Xinzhong2 |Show fewer author(s)
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    References(10)

    [1] [1] GUELMAN M, ORTENBERG F.Small satellites role in future hyperspectral Earth observation missions[J].Acta Astronautica, 2009, 64(11/12): 1252-1263.

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    [8] [8] DONG Y C, CHEN F S, WANG Y, et al.A limb atmospheric radiance inversion method based on a sun-synchronous orbit satellite[C]//Proceedings of SPIE.[S.l.]: SPIE, 2015.doi: 10.1117/12.2180861.

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    [12] [12] XIE W Y, LIU B Z, LI Y S, et al.Autoencoder and adversarial-learning-based semisupervised background estimation for hyperspectral anomaly detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5416-5427.

    [13] [13] STEIN D, BEAVEN S G, HOFF L E, et al.Anomaly detection from hyperspectral imagery[J].IEEE Signal Processing Magazine, 2002, 19(1): 58-69.

    [16] [16] ZHANG N, XIE S B, LUO H L, et al.Airport aircraft target detection based on space spectrum feature fusion[C]//The 5th International Conference on Computer and Communication Systems (ICCCS).Shanghai: IEEE, 2020: 967-971.

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    BAI Yu, LIU Lina, ZHANG Ning, LIN Chen, SONG Wei, ZHU Xinzhong. Hyperspectral Image Anomaly Detection Based on Improved RX Incremental Learning[J]. Electronics Optics & Control, 2022, 29(2): 16

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    Paper Information

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    Received: Jan. 28, 2021

    Accepted: --

    Published Online: Mar. 4, 2022

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2022.02.004

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