COMPARISON OF 2 DISCRIMINANT FUNCTIONS FOR CLASSIFYING BINARY INFRARED DATA

A comparison is presented of two simple discriminant functions for classifying binary infrared data. For the multicategory problem of 13 classes used in this invstigation, random guessing would achieve about 8% correct classification. A dot product calculation produces 49. 1% correct classification, while a distance measurement produces 58. 7%. The results from this investigation are also qualitatively compared to previous work using infrared data which retained some intensity information. It is found that the binary packing of spectral data show great promise in the area of infrared analysis.