基于模糊核主成分分析的高光谱遥感影像特征提取研究

HYPERSPECTRAL REMOTE SENSING IMAGE FEATURE EXTRACTION BASED ON FUZZY KERNEL PRINCIPAL COMPONENT ANALYSIS

主成分分析(PCA)是一种基于数理统计的线性特征变换方法,对线性结构数据的分析非常有效,但是对非线性的高光谱遥感影像数据,容易造成信息丢失和失真。本文引入模式识别中的模糊理论和核理论,有效克服了以上缺点,得到了很好的影像特征提取效果。

The principal component analysis （PCA）, a classical linear feature transformation method based on mathematical statistics, is effective in the analysis of linear data. Nevertheless, PCA is likely to result in distortion and loss of data information for non -linear hyperspectral Remote Sensing（RS） image data. In this paper, the fuzzy mathematical theory and the theory of kernel in pattern recognition is proposed for the purpose of effectively overco- ming the shortcomings of traditional PCA. The test results show that the fuzzy kernel principal component analysis （FKPCA） designed in this paper can acquire competitive image feature extraction results.