Electronics Optics & Control, Volume. 27, Issue 5, 42(2020)

A Hyperspectral Anomaly Detection Algorithm Based on Non-local Self-Similarity

WANG Yang1, LIU Zhigang1, JU Huihui2, and WANG Yiting1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(4)

    [3] [3] LI W, DU Q.Collaborative representation for hyperspectral anomaly detection[J].IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(3):1463-1474.

    [4] [4] SUN W W, LIU C, LI J L, et al.Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery[J].Journal of Applied Remote Sensing, 2014, 8(1):083641.

    [8] [8] SALMON J.On two parameters for denoising with non-local means[J].IEEE Signal Processing Letters, 2010, 17(3):269-272.

    [9] [9] BUADES A, COLL B, MOREL J M.Nonlocal image and movie denoising[J].International Journal of Computer Vision, 2008, 76(2):123-139.

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    WANG Yang, LIU Zhigang, JU Huihui, WANG Yiting. A Hyperspectral Anomaly Detection Algorithm Based on Non-local Self-Similarity[J]. Electronics Optics & Control, 2020, 27(5): 42

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    Paper Information

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    Received: Jun. 4, 2019

    Accepted: --

    Published Online: Dec. 25, 2020

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2020.05.009

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