Logistic discrimination between classes with nearly equal spectral response in high dimensionality

Logistic discrimination can be regarded as a partially parametric approach to pattern recognition. The method is quite general and robust: it assumes nothing about the probability distribution of variables and requires the estimation of fewer parameters than some better-known procedures such as the Gaussian maximum likelihood discriminator. This paper describes the logistic discrimination model and gives results obtained when using it to classify an AVIRIS image with classes that are spectrally very similar.