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The foremost advantage is that it identifies patterns in data. Like all other techniques, it also has its set of advantages and disadvantages. In the field of remote sensing data processing, PCA has been extensively used.
PCA COLUMN SOFTWARE PC
The PC transform technique has found extensive applications in almost all disciplines of physical sciences and engineering.
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The mathematical principles of the technique are well established. This statistical technique aims at reducing variance and dimensionality of the data set by projecting the data along new non-correlated axes. Principal component transform (PCT), also 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. It is an explanatory article so that fuller potential of the PCT applications can be realized. 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. The purpose of the present article is to elucidate the procedure for interpreting PC images.
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Principal component transformation is a standard technique for multi-dimensional data analysis. Keywords: Principal Component Images Remote Sensing Eigenvectors Spectral Behavior This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, IndiaĢPLANEX, Physical Research Laboratory, Ahmedabad, IndiaĮmail: © 2013 Ravi P.