On the relationship between class definition precision and classification accuracy in hyperspectral analysis

Research in recent years into methods for hyperspectral image data analysis has shown that there is a strong relationship between the precision with which classes are defined and the classification accuracy that results. There is also a relationship between these two factors and the complexity of the classifier algorithm used in the analysis. The authors illustrate this relationship empirically using a moderate dimensional, moderately difficult classification task. This example is also used to explore the effect of two recently introduced algorithms that are intended to mitigate the effect of use of a limited number of training samples on classifier performance. The results tend to confirm the theory with regard to training sample size vs. classifier complexity. They also show the two algorithms to be moderately useful in improving classifier performance when training data is limited