Principal Component Analysis of Selective Bands for Digital Satellite Imagery Classification

Abstract

Principal Component Analysis technique was applied to correlated multispectral data to convert it into uncorrelated data for the purpose of benefiting from its use in the automatic classification process.This project aims to classify multispectral satellite images using selective thematic Mapper (TM) spectral bands, and principal component (PC) images. The supervised classification method with maximum likelihood is adopted to perform the classification process.This technique was implemented using banding images and PC’s images. The results showed that the classification accuracy of the three spectral bands with the highest contrast was 86.9%, compared to the classification accuracy of the first three PC’s (89.08%). This value represents the highest classification accuracy obtained among the classification accuracy values for the spectral bands or PC images alone or in combination.The results show the advantages of feature selection in the PC’s, for every value of n-components, the 1stprincipal is the best choice. Moreover at low values of n-PC’s contain more information about the discriminability of classes than for any combination of n-original spectral channels.