Advanced Search

ISSN1001-3806 CN51-1125/TN Map

Volume 40 Issue 6
Sep.  2016
Article Contents
Turn off MathJax

Citation:

Hyperspectral matching method based on absorption features

  • Corresponding author: GUO Baofeng, gbf@hdu.edu.cn
  • Received Date: 2015-10-29
    Accepted Date: 2015-12-16
  • When adopting traditional hyperspectral ground objects identification method, the error of spectral matching became big because of the difference of absorption peak number. In order to solve the problem, an optional algorithm based on hyperspectral absorption peak characteristics was brought out, and then spectral matching according to the selected absorption was carried out. At beginning, the continuum removal in spectral curve and the extraction of spectral characteristic parameters matrix were made. And then the matching vertor of absorption peak was searched gradually according to the cosine distance-Euclidian distance of every vertor from the standard characteristic parameter matrix and the to-be-detected characteristic parameter matrix. After theoretical analysis and experimental verification of the selected absorption peak characteristic parameters matrix, the results show that this algorithm can get the best characteristic parameters vector, to realize the selection of absorption peak and make the hyperspectra matching with the selected absorption peak characteristic parameters matrix. The study is helpful for the decrease of the error of spectral matching.
  • 加载中
  • [1]

    TONG Q X, ZHANG B, ZHENG L F. Hyperspectral remote sensing[M].Beijing:Higher Education Press,2006:59-67(in Chinese).
    [2]

    LIAO W, PIZURICA A, PHILIPS W, et al. Semisupervised local discriminant analysis for feature extraction in hyperspectral images[J].IEEE Transactions on Geo science and Remote Sensing, 2013, 51(1):184-198.
    [3]

    ZHANG Ch, CAI H J, LI Zh J. Hyperspectral characteristic parameter of winter wheat photosynthetic active radiation component estimation model[J]. Spectroscopy and Spectral Analysis, 2013, 51(1):184-198(in Chinese).
    [4]

    ZHANG Y, ZHANG J L, ZHAO X Sh, et al. Extraction of mineral alteration information from core hyperspectral images based on weight of absorption peak[J], Remote Sensing for Land and Resources, 2015,27(2):154-159(in Chinese).
    [5]

    SUN Y L,ZHANG X,SHUAI T, et al. 2015 Radiometric normalization of hyperspectral satellite images with spectral angle distance and Euclidean distance[J].Journal of Remote Sensing, 2015,19(4):618-626(in Chinese).
    [6]

    FAN L, ZHAO W J, GONG Zh N, et al. Correspondence analysis of rock spectra based on continuum removing[J] Journal of Jilin University(Earth Science Edition), 2012,42(2):575-582(in Chinese).
    [7]

    ZHANG J, SHEN Y T, WANG X J. Hyperspectral quantitative models for chlorophyll-a of algae based on spectral absorption feature parameters and spectral absorption index[J]. Journal of Agro Environment Science,2011,30(8):1622-1629(in Chinese).
    [8]

    LI H J, XU Sh Y, YAN D J. Research of remote sensing image matching with sub-pixel accuracy[J]. Laser Technology,2008,31(5):493-495(in Chinese).
    [9]

    SHU X W, ZHANG Y J, GENG H, et al. Study on phase shift anto-balanced laser absorption spectroscopy[J]. Laser Technology, 2011,35(5):618-621(in Chinese).
    [10]

    PADMA S, SANJEEVI S.Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis[J]. International Journal of Applied Earth Observations and Geoinformation,2014,32(4):138-151.
    [11]

    GAN R T, GUO Zh N, LIN J B. Spectral matching technology for light-emitting diode-based jaundice photodynamic therapy device[J]. Journal of Modern Optics,2015, 62(3):212-217.
    [12]

    WU X W, GUO B F, CHEN Ch Zh, et al. RX anomaly detection based a weighted combination kernel and its parameters selection[J]. Laser Technology, 2015, 39(6):745-751(in Chinese).
    [13]

    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).
    [14]

    HUANG T T, WEI Zh H, XIU L C, et al. The research of mineral spectral matching based on weighted absorption peaks[J].Rock and Mineral Analysis,2011,30(5):584-589(in Chinese).
    [15]

    LIN H J, ZHANG H F, GAO Y Q, et al. Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species[J].Spectroscopy and Spectral Analysis, 2014,34(12):3358-3362(in Chinese).
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article views(6347) PDF downloads(438) Cited by()

Proportional views

Hyperspectral matching method based on absorption features

    Corresponding author: GUO Baofeng, gbf@hdu.edu.cn
  • 1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
  • 2. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

Abstract: When adopting traditional hyperspectral ground objects identification method, the error of spectral matching became big because of the difference of absorption peak number. In order to solve the problem, an optional algorithm based on hyperspectral absorption peak characteristics was brought out, and then spectral matching according to the selected absorption was carried out. At beginning, the continuum removal in spectral curve and the extraction of spectral characteristic parameters matrix were made. And then the matching vertor of absorption peak was searched gradually according to the cosine distance-Euclidian distance of every vertor from the standard characteristic parameter matrix and the to-be-detected characteristic parameter matrix. After theoretical analysis and experimental verification of the selected absorption peak characteristic parameters matrix, the results show that this algorithm can get the best characteristic parameters vector, to realize the selection of absorption peak and make the hyperspectra matching with the selected absorption peak characteristic parameters matrix. The study is helpful for the decrease of the error of spectral matching.

Reference (15)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return