Laser & Optoelectronics Progress, Volume. 60, Issue 20, 2028001(2023)

Semi-Supervised Hyperspectral Anomaly Detection Based on Spatial-Spectral Background Reconstruction

Luyao Li1, Zhongwei Li1、*, Leiquan Wang2, Juan Li2, and Shunxiao Shi2
Author Affiliations
  • 1College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, Shandong , China
  • 2College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, Shandong , China
  • show less
    References(26)

    [1] Li Z Y, Yu W X, Kuang G Y et al. The research of anomaly detection based on high-dimensional geometrical feature in hyperspectral imagery[J]. Remote Sensing Technology and Application, 18, 379-383(2003).

    [2] Plaza A, Benediktsson J A, Boardman J W et al. Recent advances in techniques for hyperspectral image processing[J]. Remote Sensing of Environment, 113, S110-S122(2009).

    [3] Wang Q, Lin J Z, Yuan Y. Salient band selection for hyperspectral image classification via manifold ranking[J]. IEEE Transactions on Neural Networks and Learning Systems, 27, 1279-1289(2016).

    [4] Nasrabadi N M. Hyperspectral target detection: an overview of current and future challenges[J]. IEEE Signal Processing Magazine, 31, 34-44(2014).

    [5] Li L, Li W, Du Q et al. Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection[J]. IEEE Transactions on Cybernetics, 51, 4363-4372(2021).

    [6] Jiang T, Xie W, Li Y et al. Weakly supervised discriminative learning with spectral constrained generative adversarial network for hyperspectral anomaly detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 33, 6504-6517(2022).

    [7] Li S T, Zhang K Z, Duan P H et al. Hyperspectral anomaly detection with kernel isolation forest[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 319-329(2020).

    [8] Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38, 1760-1770(1990).

    [9] Molero J M, Garzón E M, García I et al. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 801-814(2013).

    [10] Li W, Du Q, Zhang B. Combined sparse and collaborative representation for hyperspectral target detection[J]. Pattern Recognition, 48, 3904-3916(2015).

    [11] Li W, Du Q. Collaborative representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1463-1474(2015).

    [12] Zhang Y X, Du B, Zhang L P et al. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1376-1389(2016).

    [13] Wang R, Hu H, He F et al. Self-weighted collaborative representation for hyperspectral anomaly detection[J]. Signal Processing, 177, 107718(2020).

    [14] Cheng T K, Wang B. Graph and total variation regularized low-rank representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 391-406(2020).

    [15] Kang X D, Zhang X P, Li S T et al. Hyperspectral anomaly detection with attribute and edge-preserving filters[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 5600-5611(2017).

    [16] Xie W Y, Jiang T, Li Y S et al. Structure tensor and guided filtering-based algorithm for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 4218-4230(2019).

    [17] Ergen T, Kozat S S. Unsupervised anomaly detection with LSTM neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 31, 3127-3141(2020).

    [18] Li Z W, Shi S X, Wang L Q et al. Unsupervised generative adversarial network with background enhancement and irredundant pooling for hyperspectral anomaly detection[J]. Remote Sensing, 14, 1265(2022).

    [19] Jiang K, Xie W Y, Li Y S et al. Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 5224-5236(2020).

    [20] Hinton G E, Zemel R S. Autoencoders, minimum description length and Helmholtz free energy[C], 3-10(1993).

    [21] 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, 58, 5416-5427(2020).

    [22] Arisoy S, Nasrabadi N M, Kayabol K. Unsupervised pixel-wise hyperspectral anomaly detection via autoencoding adversarial networks[J]. IEEE Geoscience and Remote Sensing Letters, 19, 5502905(2021).

    [23] Su H J, Wu Z Y, Zhang H H et al. Hyperspectral anomaly detection: a survey[J]. IEEE Geoscience and Remote Sensing Magazine, 10, 64-90(2022).

    [24] Xu Y, Wu Z B, Li J et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1990-2000(2016).

    [25] 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, 8, 083641(2014).

    [26] Li L, Li W, Qu Y et al. Prior-based tensor approximation for anomaly detection in hyperspectral imagery[J]. IEEE Transactions on Neural Networks and Learning Systems, 33, 1037-1050(2022).

    Tools

    Get Citation

    Copy Citation Text

    Luyao Li, Zhongwei Li, Leiquan Wang, Juan Li, Shunxiao Shi. Semi-Supervised Hyperspectral Anomaly Detection Based on Spatial-Spectral Background Reconstruction[J]. Laser & Optoelectronics Progress, 2023, 60(20): 2028001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Remote Sensing and Sensors

    Received: Oct. 8, 2022

    Accepted: Nov. 29, 2022

    Published Online: Oct. 13, 2023

    The Author Email: Zhongwei Li (li.zhongwei@vip.163.com)

    DOI:10.3788/LOP222705

    Topics