Bilinear decomposition (soft modelling using principal component analysis) of multivariate imagery results in: score and loading plots, score images, classification projections and residual images in the scene space. Feature space score plots are used as a starting point for pixel class delineations, followed by iterative scene space evaluation. This is a reversal of traditional image processing practice, which selects training samples in the scene space. The present feature space class definitions can be shown to have certain optimality characteristics with respect to traditional scene space delineations.
After problem-dependent relevant pixel class delineations have been obtained, one can compute corresponding local class PC-models that serve as an alternative basis for problem-dependent classification and sequential segmentation. Multivariate image analysis (MIA) allows interactive exploration and classification of most types of technical multivariate imagery. We present a general strategy for multivariate image analysis, illustrated by a remote sensing showcase.