Principal component analysis with optimum order sample correlation coefficient for image enhancement

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.