A hybrid classifier using the parallelepiped and Bayesian techniques

Abstract

A versatile classification scheme is developed which uses the best features of the parallelepiped algorithm and the Bayesian maximum likelihood algorithm. The parallelepiped technique has the advantage of being very fast, especially when implemented into a table look-up scheme; its disadvantage is its inability to distinguish and classify spectral signatures which are similar in nature. This disadvantage is eliminated by the Bayesian technique which is capable of distinguishing subtle differences very well. The hybrid algorithm developed reduces computer time by as much as 90%. A two- and n-dimensional description of the hybrid classifier is given.