• Infrared and Laser Engineering
  • Vol. 48, Issue 7, 726003 (2019)
Tang Yi1, Nian Yongjian2、*, He Mi2, Wang Qiannan2, and Xu Ke3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3788/irla201948.0726003 Cite this Article
    Tang Yi, Nian Yongjian, He Mi, Wang Qiannan, Xu Ke. Successive spectral unmixing for hyperspectral images based on L1/2 regularization[J]. Infrared and Laser Engineering, 2019, 48(7): 726003 Copy Citation Text show less
    References

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    [10] Wei Yiwei, Huang Shiqi, Wang Yiting, et al. Volume and sparseness constrained algorithm for hyperspectral unmixing[J]. Infrared and Laser Engineering, 2014, 43(4): 1247-1254. (in Chinese)

    [11] Tong L, Zhou J, Li X, et al. Region-based structure preserving nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4): 1575-1588.

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    [15] Xu Z B, Zhang H, Wang Y, et al. L1/2 regularization[J]. Science China Information Sciences, 2010, 53(6): 1159-1169.

    [16] Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2010, 3(1): 1-122.

    [17] Xu Z B, Chang X Y, Xu F M, et al. L1/2 regularization: a thresholding representation theory and a fast solver[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(7): 1013-1027.

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    [1] Jia Qi, Liao Shouyi, Zhang Zuoyu, Yang Xinjie. Reweighted sparse nonnegative matrix decomposition for hyperspectral unmixing[J]. Infrared and Laser Engineering, 2020, 49(S2): 20200152

    Tang Yi, Nian Yongjian, He Mi, Wang Qiannan, Xu Ke. Successive spectral unmixing for hyperspectral images based on L1/2 regularization[J]. Infrared and Laser Engineering, 2019, 48(7): 726003
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