Supervised classification of remote sensing images with unknown classes

This paper addresses the problem of classifying multispectral images when the a priori knowledge about classes is not complete: the true number of classes is not known, or it is not possible to obtain ground truth data for some of the classes in the image. We propose a method to perform image classification taking into account all the classes, "known" and "unknown", based on accurate estimates of the prior probabilities and of the joint probability density functions (pdfs). To this end, we propose the application of the dependence tree approximation to mitigate the problem of few available samples. Finally, we investigate the suitability of the application of a biased cross-validation criterion for the optimization of 2-dimensional pdf estimations.