An unsupervised classification of multispectral scanner data using correspondence analysis (CLAMS)

Abstract

An unsupervised classification method was designed as a three step procedure: dimension reduction, classification of channels and clustering of measured reflectance spectra. The basic concept is that similar channels are most likely to characterize typical shapes of reflectance spectrum. Some results are shown which are in good agreement with the known ground truth.