中文摘要：

为了实现高光谱降维并保留重要的光谱特征,通过独立分量分析（independent component analysis,ICA）混合模型和高光谱线性模型的对比分析,提出了结合纯像元提取和ICA的高光谱数据降维方法。该方法通过估计虚拟维数（virtual dimensionality,VD）确定特征个数,采用自动目标生成过程（automatic targetgeneration process,ATGP）从原始数据中提取纯像元向量,作为ICA算法的初始化向量,以负熵为目标函数产生独立分量,并通过高阶统计量筛选实现高光谱数据的降维。分类实验结果表明,该方法不仅解决了传统ICA的随机排序问题,而且与经典降维算法主分量分析（principal components analysis,PCA）相比,分类精度提高了6.83%,在大大降低高光谱数据量的情况下很好地保留了高光谱数据的特征,有利于数据的后续分析和应用。

英文摘要：

By comparing ICA with hyperspectral linear model,this paper proposed a new approach to hyperspectral data reduction based on combination of pure pixel extraction and ICA,in order to retain significant spectral characteristics in reduction process.In this method,virtual dimensionality estimation determined the number of spectral characteristics,and automatic target generation process extracted pure pixel vectors which could be applied as initialization vectors for ICA,and then used negentropy iteration and higher order statistics for independent components generation and final components selection respectively.Classification results show that the approach not only solves the stochastic scheduling problem of traditional ICA,but also achieves a classification accuracy increment by 6.83 percent comparing with the classical dimensionality reduction method PCA.The proposed approach can protect characteristics of hyperspectral data very well in the case of significantly data reduction and makes it favorable for the following analysis and application.