Volume 32 Issue 11
Nov.  2006
Turn off MathJax
Article Contents
Zhao Huijie, Li Na, Jia Guorui, et al. Improved independent component analysis applied to classification hyperspectral imagery[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(11): 1333-1336. (in Chinese)
Citation: Zhao Huijie, Li Na, Jia Guorui, et al. Improved independent component analysis applied to classification hyperspectral imagery[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(11): 1333-1336. (in Chinese)

Improved independent component analysis applied to classification hyperspectral imagery

  • Received Date: 30 Apr 2006
  • Publish Date: 30 Nov 2006
  • To avoid the disadvantage of getting into local optimum solution with general numerical computation methods in the general independent component analysis and the restriction of neuron activation functions of neural learning algorithm, an improved model of independent component analysis (ICA) based on genetic algorithm was proposed for the unsupervised classification of hyperspectral data. In the proposed algorithm, the maximizing non-Guassianity was used to measure the statistical independence of the components, and the forth-order cumulant, kurtosis, was adopted as fitness function in genetic algorithm. In the application, the global optimum solution can be obtained and the fine plant classification can be implemented without any prior information when the proposed algorithm is applied to the push-broom hyperspectral technique imager (PHI) data. Moreover, compared with the conventional unsupervised classification algorithm of hyperspectral data, the proposed algorithm is more applicable and can obtain the better precision and accuracy.

     

  • loading
  • [1] 张钧萍, 张晔, 周廷显. 成像光谱技术超谱图像分类研究现状与分析[J]. 中国空间科学技术. 2001, 2(1):37-44 Zhang Junping, Zhang Ye, Zhou Tingxian. State-of-arts and analysis on hyperspectral image classification in imaging spectral technique[J]. Chinese Space Science and Technology, 2001,2(1):37-44(in Chinese) [2] Hyvrinen A, Karhunen J, Oja E. Independent component analysis . [2001] .http://www.cis.hut.fi [3] Stefan A Robila, Pramod K Varshney. Target detection in hyperspectral images based on independent component analysis Proc SPIE of Int Soc Opt Eng. Orlando, USA:SPIE,2002,4726:173-182 [4] Chiang Shao-Shan, Chang Chein-I, Ginsberg I W. Unsupervised target detection in hyperspectral images using projection pursuit [J]. IEEE Trans Geoscience and Remote Sensing, 2001, 39(7):1380~1391 [5] Shah C A, Arora M K, Robila S A, et al. ICA mixture model based unsupervised classification of hyperspectral imagery 31st Applied Imagery Pattern Recognition Workshop. USA:IEEE,2002:29~35 [6] Huang Yaping, Luo Siwei. Genetic algorithm applied to ICA feature selection Neural Networks, 2003 proceeding of the International Joint Conference. USA:IEEE, 2003:704-707
  • 加载中

Catalog

    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 Metrics

    Article views(3617) PDF downloads(900) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return