Principal component analysis with optimum order sample correlation coefficient for image enhancement
Principal component analysis with optimum order sample correlation coefficient for image enhancement
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DOI：
10.1080/01431160600606882
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发表时间：
2006-08
影响因子：
3.4
通讯作者：
Qiuming Cheng Linhai Jing;A. Panahi
中科院分区：
文献类型：
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作者：
Qiuming Cheng Linhai Jing;A. Panahi
主成分分析（PCA）在遥感信息提取中应用广泛，发挥着重要作用。然而，基于二阶协方差或相关性的普通PCA能够基于大多数像素值的统计特性形成分量-平均值附近的像素值。在许多应用中，主成分应该在最佳相关系数的基础上构建，以便这些成分可以表示感兴趣的少数像素的低值或高值。基于最优阶样本相关系数，提出了一种新的主成分分析方法，以增强包括低或高少数像素值在内的图像波段的贡献，从而有助于提取图像分类和模式识别的弱信息。普通主成分分析成为本文介绍的新版主成分分析的特例。通过对加拿大Mitchell‐sulurets地区的Landsat Thematic Mapper （TM）图像识别Au/Cu相关蚀变带的案例研究，验证了新方法的有效性。
Principal component analysis (PCA) has been commonly used and has played an important role in remote sensing for information extraction. However, the ordinary PCA based on second‐order covariance or correlation is capable of forming components on the basis of the statistical properties of a majority of pixel values – pixel values around mean values. For many applications, principal components should be constructed on the basis of optimum correlation coefficients so that the components can represent low or high values of minority pixels of interest. A new version of the PCA has been proposed on the basis of an optimum order sample correlation coefficient for enhancing the contribution of the image bands including the low or high minority pixel values that can assist in extracting weak information for image classification and pattern recognition. The ordinary PCA becomes the special case of the new version of the PCA introduced in this paper. The new method was validated with a case study of identification of Au/Cu‐associated alteration zones from a Landsat Thematic Mapper (TM) image in the Mitchell‐Sulphurets district, Canada.