Classification of high dimensional multispectral data

Abstract

New sensor technology has made it possible to gather multi!;pectral images in hondreds and potentially thousands of spectral bands, whereas current sensors typically gather images in 12 or fewer bands. This tremendous increase in spectral resolution should provide a wealth of detailed information, but the techniques used to analyze lower di:mensional data often perform poorly on high dimensional data. In this thesis, algorithms are developed to analyze high dimensional multispectral data. In particular a method for gathering training samples is demonstrated, the effect of atmospheric adjustments on classification accuracy is explored, a new method for estimating the covariance matrix of a class is presented, and a new method for estimating the number of clusters in a data cloud is developed. These techniques enable the data analyst to classify high dimensional data more accurately and efficiently than is possible with standard pattern recognition techniques.