A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks

Abstract

A radial basis function neural network (RBFNN) is developed to examine two mixing models, linear and nonlinear spectral mixtures, which describe the spectra collected by both airborne and laboratory-based spectrometers. The authors examine the possibility that there may be naturally occurring situations where the typically used linear model may not provide the most accurate resultant spectral description. Under such a circumstance, a nonlinear model may better describe the mixing mechanism.