This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
||||||||
|
Paper Details
Paper Title
Principal Component Image Interpretation - A Logical and Statistical Approach
Authors
Md Shahid Latif
Abstract
Principal component analysis a multivariate statistical data analysis algorithm widely used as a dimensionality reduction algorithm in image processing task. In remote sensing data analysis, PCA used as a spectral enhancement pre-processing algorithm to reduce higher dimension space to lower dimension space with preservation of all the information in original variables. This paper provides a lucid approach to analyse and interpret PC images using statistical and logical approach. It also describe the dependency of the tonal variation of pixel vector of PCs image with magnitude and sign (negative or positive) of the coefficient of eigenvector and pixel value in original multi-spectral bands.
Keywords- Principal Component Analysis, Eigenvalue, Eigenvector, Component Loading Factor.
Publication Details
Unique Identification Number - IJEDR1404069Page Number(s) - 3803-3810Pubished in - Volume 2 | Issue 4 | Dec 2014DOI (Digital Object Identifier) - Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Md Shahid Latif, "Principal Component Image Interpretation - A Logical and Statistical Approach", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 4, pp.3803-3810, Dec 2014, Available at :http://www.ijedr.org/papers/IJEDR1404069.pdf
Article Preview
|
|
||||||
|
|
||||||||
