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YAN Yang, HUA Wenshen, ZHANG Yan, CUI Zihao, LIU Xun. An improved method of hyperspectral endmember extraction based on band selection[J]. LASER TECHNOLOGY, 2019, 43(4): 574-578. DOI: 10.7510/jgjs.issn.1001-3806.2019.04.024
Citation: YAN Yang, HUA Wenshen, ZHANG Yan, CUI Zihao, LIU Xun. An improved method of hyperspectral endmember extraction based on band selection[J]. LASER TECHNOLOGY, 2019, 43(4): 574-578. DOI: 10.7510/jgjs.issn.1001-3806.2019.04.024
shu

An improved method of hyperspectral endmember extraction based on band selection

  • Department of Electronic and Optical Engineering, Army Engineering University, Shijiazhuang 050003, China

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  • Received Date: September 03, 2018
  • Revised Date: October 08, 2018
  • Published Date: July 24, 2019
    Graphical Abstract
    • Abstract
  • In order to solve the problem of destroying the physical meaning of spectral curve of pixels in dimension reduction of traditional N-FINDR algorithm, the best exponential method of band selection was used instead of feature extraction. The dimension reduction method of N-FINDR algorithm was improved. Experiments were carried out using the simulated and real hyperspectral data. The improved N-FINDR algorithm and other two algorithms were used to extract the terminal elements respectively. Full constrained least squares method was used to solve the mixing problem. The results show that the improved N-FINDR algorithm has higher precision and uses less time. It is feasible to use band selection instead of feature extraction to improve the dimension reduction method and retain the physical meaning of spectral curve in N-FINDR algorithm.
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    • References (22)
  • [1]
    LIU X. Research of camouflage target detection and evaluation in hyperspectral image [D].Shijiazhuang: Mechanical Engineering College, 2014: 1-2(in Chinese).
    [2]
    SHAO T. Research on target recognition in hyperspectral imagery based on spectral information [D]. Harbin: Harbin Institute of Technology, 2010: 4-7(in Chinese).
    [3]
    BIONCAS-DIAS J M, PLZAZ A, DOBIGEON N, et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches[J]. IEEE Journal of Selected Topics Applied Earth Observation and Remote Sensing, 2012, 5(2):354-379. DOI: 10.1109/JSTARS.2012.2194696
    [4]
    IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Total variation regularization for sparse hyperspectral unmixing[J]. IEEE Transaction Geoscience Remote Sensing, 2012, 50(11):4484-4502. DOI: 10.1109/TGRS.2012.2191590
    [5]
    FAN L, LIN X, ALEXANDER W, et al. Feature extraction for hyperspectral imagery via ensemble localized manifold learning [J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2486-2490. DOI: 10.1109/LGRS.2015.2487226
    [6]
    ZHU G K, HUANG Y C, LEI J S, et al. Unsupervised hyperspectral band selection by dominant set extraction [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1):227-619. DOI: 10.1109/TGRS.2015.2453362
    [7]
    WANG K, QU H M. Anomaly detection method based on improved minimum noise fraction transformation [J]. Laser Technology, 2015, 39(3):381-385(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jgjs201503022
    [8]
    WANG Q, YANG G, ZHANG J F, et al. Unsupervised band selection algorithm combined with K-L divergence and mutual information [J]. Laser Technology, 2018, 42(3):417-421(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jgjs201803024
    [9]
    LI J, YANG M H, WU K J. Band selection based hyperspectral remote sensing image classification [J]. Journal of Geomatics, 2012, 37(2):41-44(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/gpxygpfx201505047
    [10]
    YAN Y, HUA W Sh, LIU X, et al. Research of hyperspectral unmixing methods[J].Laser Technology, 2018, 42(5):692-698(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/jgjs201805020
    [11]
    WINTER M E. N-FINDR: an algorithm for fast autonomous spectral endmember determination in hyperspectral data[J].Proceedings of the SPIE, 1999, 3753:266-275. DOI: 10.1117/12.366289
    [12]
    CHANG C I, WU C C, LIU W M. A new growing method for simplex-based endmember extraction algorithm [J]. IEEE Transaction on Geoscience and Remote Sensing, 2006, 44(10):2804-2819. DOI: 10.1109/TGRS.2006.881803
    [13]
    WANG L G, DENG L Q, ZHANG J. Endmember selection algorithm based on linear least square support vector machines[J].Spectroscopy and Spectral Analysis, 2010, 30(3): 743-747 (in Ch-inese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gpxygpfx201003038
    [14]
    XIONG W, CHANG C I, WU C C. Fast algorithms to implement N-FINDR for hyperspectral endmember extraction[J].IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2011, 4(3):545-564. DOI: 10.1109/JSTARS.2011.2119466
    [15]
    CHANG C I, DU Q. Estimation of number of spectrally distinct signal in hyperspectral imagery [J].IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3):608-619. DOI: 10.1109/TGRS.2003.819189
    [16]
    MARTIN G, PLAZA A. Region-based spatial preprocessing for endmember extraction and spectral unmixing [J]. IEEE Transaction on Geoscience and Remote Sensing, 2011, 8(4):745-749. DOI: 10.1109/LGRS.2011.2107877
    [17]
    YAN Y, HUA W Sh, CUI Z H, et al. Classification volume for hyperspectral endmember extraction[J]. Laser Optoelectronics Progress, 2018, 55(9):093004(in Chinese). DOI: 10.3788/LOP
    [18]
    GENG X, ZHAO Y, WANG F, et al. A new volume formula for a simplex and its application to endmember extraction for hyperspectral image analysis [J]. International Journal of Remote Sensing, 2010, 31(4):1027-1035. DOI: 10.1080/01431160903154283
    [19]
    GONG W J, DONG A G, HAN X. Band selection algorithm for hyperspectral images based on band index [J]. Laser Technology, 2017, 41(4):507-510(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTotal-JGJS201704010.htm
    [20]
    REN X D, LEI W H, GU Y, et al. Improved band selection method for hyperspectral imagery [J]. Computer Science, 2015, 42(11): 162-168(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/zjdxxb-gx201804011
    [21]
    PLAZA A, PABLO M, ROSA P, et al. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3):650-663. DOI: 10.1109/TGRS.2003.820314
    [22]
    DANIEL C H, CHEIN I C. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery [J]. IEEE Transaction on Geoscience and Remote Sensing, 2001, 39(3):529-545. DOI: 10.1109/36.911111
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