Contributions of principal components to discrimination of classes of land cover in multi-spectral imagery

Abstract

Discriminant analysis and canonical variates analysis on principal components of a number of extracts from multi-spectral images showed that low order components with large eigenvalues are not necessarily the most important for distinguishing classes of landcover and discarding components with small eigenvalues may reduce the accuracy of discrimination. It is therefore inadvisable to use principal components analysis for reducing the number of wavebands used for discriminant analysis.