Signature extension through the application of cluster matching algorithms to determine appropriate signature transformations

Signature extension is intended to increase the space-time range over which a set of training statistics can be used to classify data without significant loss of recognition accuracy. A first cluster matching algorithm MASC (Multiplicative and Additive Signature Correction) was developed at the Environmental Research Institute of Michigan to test the concept of using associations between training and recognition area cluster statistics to define an average signature transformation. A more recent signature extension module CROP-A (Cluster Regression Ordered on Principal Axis) has shown evidence of making significant associations between training and recognition area cluster statistics, with the clusters to be matched being selected automatically by the algorithm.