Abstract
Principal component transformation is a standard technique for multi-dimensional data analysis. The purpose of the present article is to elucidate the procedure for interpreting PC images. The discussion focuses on logically explaining how the negative/positive PC eigenvectors (loadings) in combination with strong reflection/absorption spectral behavior at different pixels affect the DN values in the output PC images. It is an explanatory article so that fuller potential of the PCT applications can be realized.
Highlights
Principal component transform (PCT), known as eigenvector transformation, Hotelling transformation, Karhunen-Loève (K-L) transformation, eigenvalue-eigenvector decomposition, is a standard and highly powerful technique for processing multidimensional data
Most commonly PCT is performed on remote sensing data sets using various software packages, and the resulting PC images invariably appear quite different to the input spectral band images
Though principal component analysis (PCA) is a standard technique for multidimensional data analysis, there has been no well outlined procedure for interpreting PC images
Summary
Principal component transform (PCT), known as eigenvector transformation, Hotelling transformation, Karhunen-Loève (K-L) transformation, eigenvalue-eigenvector decomposition, is a standard and highly powerful technique for processing multidimensional data. In the field of remote sensing data processing, numerous examples of principal component analysis (PCA) have been described; examples of its logical interpretations and applications have been rather limited. Most commonly PCT is performed on remote sensing data sets using various software packages, and the resulting PC images invariably appear quite different to the input spectral band images. After this a brief review of earlier studies on applications of PCA in remote sensing is given This is followed by scope of the present work and illustration of the dependence of PC image displays on software. An assessment and evaluation of effect of combinations of eigenvectors with DN values of spectral bands (with the help of examples), followed by brief conclusions are presented
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