A comparative evaluation of spectral reflectance representations for spectrum reconstruction, interpolation and classification

Due to the high dimensionality of spectral data, spectrum representation techniques have often concentrated on modelling the spectra as a linear combination of a small basis set. Here, we focus on the evaluation of a B-Spline representation, a Gaussian mixture model, PCA and wavelets when applied to represent real-world spectrometer and spectral image data. These representations are important since they open up the possibility of reducing densely sampled spectra to a compact form for spectrum reconstruction, interpolation and classification. In particular, we shall perform an evaluation of these representations for the above tasks on two datasets consisting of reflectance spectra and hyperspectral images.