Hierarchical clustering of self-organizing maps for cloud classification

This paper presents a new method for segmenting multispectral satellite images. The proposed method is unsupervised and consists of two steps. During the first step the pixels of a learning set are summarized by a set of codebook vectors using a Probabilistic Self-Organizing Map (PSOM, Statistique et methodes neuronales, Dunod, Paris, 1997). In a second step the codebook vectors of the map are clustered using Agglomerative Hierarchical Clustering (AHC, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 1996). Each pixel takes the label of its nearest codebook vector. A practical application to Meteosat images illustrates the relevance of our approach. (C) 2000 Elsevier Science B.V. All rights reserved.