Fast maximum likelihood classification of remotely-sensed imagery

Abstract We have devised, written and tested an implementation of the Gaussian Maximum Likelihood classification method for a commercial image processor. This has resulted in significant savings in execution time for the classification of multispectural remotely-sensed imagery, at very little cost to the accuracy, when compared to a software version of the same algorithm.