Some further results of three stage ML classification applied to remotely sensed images

Recently, a three stage Maximum Likelihood (TSML) classier (1) has been proposed to reduce the computational requirements of the ML classication rule. Some modications are proposed here further to improve this fast algorithm. Winograd's method is proposed for use with range calculations, and is also used with Lower Triangular and Unitary canonical form approaches (2) in calculating quadratic forms. New types of range are derived by expanding the discriminant function which are then used with a TSML algorithm to identify their usefulness in eliminating groups at stages I & II. The use of pre-calculated values is proposed to obviate some multiplications while calculating the ranges. Further, threshold logic (3) is used with an old and a mod-ied TSML classier and its eectiveness observed in further reducing computation time. Performance of the old and the modied TSML algorithms is studied in detail by varying the dimensionality and number of samples. For the purpose of experiment, 6 channel thematic mapper (TM) and randomly generated 12 dimensional data sets are used. A maximum speed-up factor of 4-8 is observed with these data sets. These experiments are also repeated with modied maximum likelihood and Mahalanobis distance classiers to inspect CPU time requirements.