USE OF ERROR MATRICES TO IMPROVE AREA ESTIMATES WITH MAXIMUM-LIKELIHOOD CLASSIFICATION PROCEDURES

The maximum likelihood classifier is by far the most widespread among supervised classification methods. This procedure offers numerous advantages, but it has considerable shortcomings in the presence of strongly irregular spectral distributions, mainly related to bias in area estimates. Since these cases are quite common, some methods have already been proposed to correct biased area estimates from maximum likelihood classifications, but they are often not generally applicable or statistically stable. In this article a method is put forward to correct maximum likelihood assignment probabilities by means of a transition matrix; this matrix is derived through a simple mathematical algorithm from a contingency table of a previous classification compared to reference pixels. The purpose is clearly to attain a diagonalization of the final error sources to better estimate area extents and, above all, to achieve higher global discrimination accuracy. As different environmental situations may cause wide variability in the results of such a procedure, this was tested in three case studies using TM data acquired over areas with different landscapes. The results, evaluated by means of suitable statistics, significantly support that the method has general validity and applicability.