Feature Reduction

Abstract

Classification cost increases with the number of features used to describe pixel vectors in multispectral space — i. e. with the number of spectral bands associated with a pixel. For classifiers such as the parallelepiped and minimum distance procedures this is a linear increase with features; however for maximum likelihood classification, the procedure most often preferred, the cost increase with features is quadratic. Therefore it is sensible economically to ensure that no more features than necessary are utilised when performing a classification. Features which do not aid discrimination, by contributing little to the separability of spectral classes, should be discarded since they will represent a cost burden. Removal of least effective features is referred to as feature selection, this being one form of feature reduction. The other is to transform the pixel vector into a new set of co-ordinates in which the features that can be removed are made more evident. Both procedures are considered in some detail in this Chapter.