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.
Date of this Version