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GONG Wenjuan, DONG Anguo, HAN Xue. Band selection algorithm for hyperspectral images based on band index[J]. LASER TECHNOLOGY, 2017, 41(4): 507-510. DOI: 10.7510/jgjs.issn.1001-3806.2017.04.010
Citation: GONG Wenjuan, DONG Anguo, HAN Xue. Band selection algorithm for hyperspectral images based on band index[J]. LASER TECHNOLOGY, 2017, 41(4): 507-510. DOI: 10.7510/jgjs.issn.1001-3806.2017.04.010
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Band selection algorithm for hyperspectral images based on band index

  • School of Science, Chang'an University, Xi'an 710064, China

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  • Received Date: June 23, 2016
  • Revised Date: September 22, 2016
  • Published Date: July 24, 2017
    Graphical Abstract
    • Abstract
  • In order to remove data redundancy of hyperspectral images, and improve the accuracy and efficiency of hyperspectral image processing, a band selection algorithm was proposed based on band index of hyperspectral images. Wavelet transform was used to deal with the noise of hyperspectral image data. Bands are divided into groups by using joint skewness-kurtosis figure, and the band was removed as a redundant band which was determined based on the size of band index. The set of the minimum bands was obtained in this way. The experimental results show that the endmember set selected by using the above bands is consistent with that selected by using all bands. The redundancy band is removed to the greatest extent without affecting the endmember extraction. The classification accuracy of the band set is close to that of all bands. The band selection algorithm is feasible and effective. The study provides help to reduce the dimension of hyperspectral images.
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    • References (18)
  • [1]
    GONG M G, ZHANG M Y, YUAN Y. Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1):544-557. DOI: 10.1109/TGRS.2015.2461653
    [2]
    SUN K, GENG X R, JI L Y. A new sparsity-based band selection method for target detection of hyperspectral image[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2):329-333. DOI: 10.1109/LGRS.2014.2337957
    [3]
    HE Y L, LIU D Z, WANG J L, et al. Independent component analysis-based band selection for hyperspectral imagery[J]. Infrared and Laser Engineering, 2012, 41(3):818-824(in Chinese).
    [4]
    LIU C H, ZHAO C H, ZHANG L Y. A new method of hyperspectral remote sensing image dimensional reduction[J]. Journal of Image and Graphics, 2005, 10(2):218-222. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_39930a0fcbffd07ab748097239d1ef88
    [5]
    MEDJAHED S A, SAADI T A, BENYETTOU A, et al. Gray wolf optimizer for hyperspectral band selection[J]. Applied Soft Computing, 2016, 40:178-186. DOI: 10.1016/j.asoc.2015.09.045
    [6]
    SU H J, YONG B, DU Q. Hyperspectral band selection using improved firefly algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(1):68-72. DOI: 10.1109/LGRS.2015.2497085
    [7]
    FENG J, JIAO L, ZHANG X, et al. Hyperspectral band selection based on trivariate mutual information and clonal selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7):4092-4105. DOI: 10.1109/TGRS.2013.2279591
    [8]
    HOSSAIN M A, JIA X, PICKERING M. Subspace detection using a mutual information measure for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(2):424-428. DOI: 10.1109/LGRS.2013.2264471
    [9]
    SU H J, DU Q, CHEN G S, et al. Optimized hyperspectral band selection using particle swarm optimization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2659-2670. DOI: 10.1109/JSTARS.2014.2312539
    [10]
    SUN W W, ZHANG L P, DU B, et al. Band selection using improved sparse subspace clustering for hyperspectral imagery classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):2784-2797. DOI: 10.1109/JSTARS.2015.2417156
    [11]
    WANG L G, WEI F J. Band selection for hyperspectral imagery based on combination of genetic algorithm and ant colony algorithm [J]. Journal of Image and Graphics, 2013, 18(2):235-242(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGTB201302017.htm
    [12]
    GUO L, CHANG W W, FU C Y. Band selection of optimal for hyperspectral image fusion[J]. Journal of Astronautics, 2011, 32(2):374-379(in Chinese).
    [13]
    ZHAO C H, CHEN W H, YANG L. Research advances and analysis of hyperspectral remote sensing image band selection[J]. Journal of Natural Science of Heilongjiang University, 2007, 24(5):592-602(in Chinese). http://ieeexplore.ieee.org/document/4378560/
    [14]
    SU H J, DU P J, SHENG Y H. Study on band selectional gorithms of hyperspectral image data[J]. Application Research of Computers, 2008, 25(4): 1093-1096(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSYJ200804040.htm
    [15]
    CHEN S Y, LIU J X, DING Y. Study on fusion method of infrared and X-ray image based on wavelet transform[J]. Laser Technology, 2015, 39(5):685-688(in Chinese). http://www.en.cnki.com.cn/Article_en/CJFDTOTAL-JGJS201505021.htm
    [16]
    CHEN F, ZHANG W W, YU W J, et al. Fusion algorithm of EMCCD's low-light-level images based on wavelet transform[J]. Laser Technology, 2014, 38(2):155-160(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTotal-JGJS201402003.htm
    [17]
    ZHOU Y, LI X R, ZHAO L Y. Modified linear-prediction based band selection for hyperspectral image[J]. Acta Optica Sinica, 2013, 33(8): 0828002(in Chinese). DOI: 10.3788/AOS
    [18]
    GAO L R, GAO J W, LI J, et al. Multiple algorithm integration based on ant colony optimization for endmember extraction from hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):2569-2582. DOI: 10.1109/JSTARS.2014.2371615
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