Multispectral data classification using similarity factors

► A matrix is associated to a multi-resolution representation of the signal. ► A PCA model is then used for each matrix created from the available dataset. ► Similarity is evaluated between the sample and the class reference PCA models. ► The trial-and-error selection of spectra preprocessing is avoided. ► Good classification results and an intuitive graphical interpretation are obtained.