• Infrared and Laser Engineering
  • Vol. 47, Issue 2, 226002 (2018)
Xu Xia1, Zhang Ning2, Shi Zhenwei1, Xie Shaobiao3, and Qi Naiming3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3788/irla201847.0226002 Cite this Article
    Xu Xia, Zhang Ning, Shi Zhenwei, Xie Shaobiao, Qi Naiming. Sparse unmixing of hyperspectral images based on Pareto optimization[J]. Infrared and Laser Engineering, 2018, 47(2): 226002 Copy Citation Text show less
    References

    [1] Pan B, Shi Z W, Zhang N, et al. Hyperspectral image classification based on nonlinear spectral-spatial network[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1782-1786.

    [2] Yang Xinfeng, Hu Xunuo, Nian Yongjian. Class-based compression algorithm for hyperspectral images[J]. Infrared and Laser Engineering, 2016, 45(2): 0228003. (in Chinese)

    [3] Xu X, Shi Z W. Multi-objective based spectral unmixing for hyperspectral images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 124: 54-69.

    [4] Deng Chengzhi, Zhang Shaoquan, Wang Shengqian, et al. Hyperspectral unmixing algorithm based on L1 regularization[J]. Infrared and Laser Engineering, 2015, 44(3): 1092-1097. (in Chinese)

    [5] Tang Zhongqi, Fu Guangyuan, Chen Jin, et al. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Optics and Precision Engineering, 2015, 23(9): 2708-2714. (in Chinese)

    [6] Feng Shuyi, Zhang Ning, Shen Ji, et al. Method of cloud detection with hyperspectral remote sensing image based on the reflective characteristics[J]. Chinese Optics, 2015, 8(2): 198-204. (in Chinese)

    [7] Huang Hong, Zheng Xinlei. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Optics and Precision Engineering, 2016, 24(4): 873-881. (in Chinese)

    [8] Fang Hong, Yang Hairong. Greedy algorithms and compressed sensing[J]. Acta Automatica Sinica, 2011, 37(12): 1413-1421. (in Chinese)

    [9] Bi Yanmeng, Wang Qian, Yang Zhongdong, et al. Advances on space-based hyper spectral remote sensing for atmospheric CO2 in near infrared band[J]. Chinese Optics, 2015, 8(5): 725-735. (in Chinese)

    [10] Qian C, Yu Y, Zhou Z. Subset selection by Pareto optimization[C]//Neural Inf Process Syst, 2015: 1765-1773.

    [11] Bioucas-Dias J, Figueiredo M. Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing[C]//2nd Workshop Hyperspectr. Image Signal Process Evol Remote Sens, 2010, 1: 1-4.

    [12] Iordache M D, Bioucas-Dias J M, Plaza A. Total variation spatial regularization for sparse hyperspectral unmixing[J]. IEEE Trans Geosci Remote Sens, 2012, 50(11): 4484-4502.

    [13] Shi Z, Tang W, Duren Z, et al. Subspace matching pursuit for sparse unmixing of hyperspectral data[J]. IEEE Trans Geosci Remote Sens, 2014, 52(6): 3256-3274.

    [14] Pan B, Shi Z W, An Z Y, et al. A novel spectral-unmixing-based green algae area estimation method for GOCI data[J]. IEEE J Sel Topics Appl Earth Observ Remote Sens, 2017, 10(2): 437-449.

    Xu Xia, Zhang Ning, Shi Zhenwei, Xie Shaobiao, Qi Naiming. Sparse unmixing of hyperspectral images based on Pareto optimization[J]. Infrared and Laser Engineering, 2018, 47(2): 226002
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