Improved independent component analysis applied to classification hyperspectral imagery
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摘要: 针对独立成分分析在使用常规数值求解时容易陷入局部最优解的问题,以及采用神经学习算法时神经元激活函数的限制问题,将遗传算法与独立成分分析相结合,并对模型进行改进,提出了适合于高光谱数据无监督分类的模型.该算法采用最大化非高斯性进行成分的统计独立性度量,利用四阶累积量-峰度作为遗传算法的适应度函数.在应用分析中,将该算法应用于推扫式高光谱成像仪(PHI,Push-broom Hyperspectral technique Imager)数据地物分类能够获得全局最优解,在没有先验信息情况下实现地物的精细分类;与传统高光谱无监督分类算法比较,表明该算法的适用性,并具有更高的分类精度和准确性.Abstract: 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.
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[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] Hyvrinen 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 -
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