Classification Decision Rule Modification On The Basis Of Information Extracted From Training Data

A classification of training data can be a useful source of information to increase thematic classification accuracy. Not only does it indicate the distribution of misclassification but it can provide data which can enable the modification of the classifiers decision rules. This can lead to significant increases in classification accuracy. Rule modification also permits the definition of uncertainty classes which may be used to delimit transitional zones between cover-types. Both rule modification and uncertainty class definition are discussed in relation to the classification of semi-natural vegetation in Surrey from Landsat-R1 data.