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.
Zhao Huijie, Li Na, Jia Guorui, Dong Chao.Improved independent component analysis applied to classification hyperspectral imagery[J] JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2006,V32(11): 1333-1336
�ž�Ƽ, ����, ��͢��. �������������ͼ������о���״�����[J]. �й��ռ��ѧ����. 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)
Hyv�|rinen A, Karhunen J, Oja E. Independent component analysis .
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
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
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
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