Abstract
Experiments have been conducted to determine optimum threshold for parametric Maximum Likelihood Classifier (MLC).
Optimum threshold indicates different results for Thematic Mapper (TM) and MSS data. This may due to the fact that the TM spatial resolution is 2.7 times finer than MSS, and consequently, TM imagery has more spectral variability for a class. The increase of the spectral heterogeneity in a class and the higher number of channels being used in the classification process may play significant role.
For example, the optimum threshold for classifying an agricultural scene using MSS data is about 2.5 standard deviations, while that for TM corresponds to more than four standard deviations.
This paper compares the optimum threshold between MSS and TM, and suggests a method of using unassigned boundary pixels to determine the optimum threshold. Further, it describes the relationship of the optimum threshold threshold to the class variance with a full illustration of LANDSAT data.
The experimental conclusions suggest to the user some systematic methods for obtaining an optimal classification with Maximum Likelihood Classifier.
