Automatic unsupervised classification of snow-covered areas by decision-tree classification and minimum-error thresholding

The problem of the classification of snow-covered areas from multispectral images is addressed in this paper. The key idea of the proposed technique is to integrate a decision tree classifier (DTC) and a Bayesian unsupervised thresholding algorithm, aiming at a complete automation of the classification process. Given a classification problem, the DTC approach decomposes the problem in a suitable tree-structured collection of binary sub-problems, for which simple (e.g., threshold-based) decision rules can be defined. The proposed strategy, by adopting the tree classification, discriminates several snow-covered and non-snow-covered classes, by decomposing the related multi-class problem into a set of binary thresholding sub-problems involving the multispectral channels and the resulting normalized difference vegetation index and normalized difference snow index. Focusing on a critical node in the tree, a Bayesian approach is used to expresses the threshold-selection problem as the minimization of a functional related to the probability of classification error. Experiments are reported on MODIS data.