Clustering algorithms for large sets of heterogeneous remote sensing data

The authors introduce a concept for a global classification of remote sensing images in large archives, e.g. covering the whole globe. Such an archive for example will be created after the Shuttle Radar Topography Mission in 1999. The classification is realized as a two step procedure: unsupervised clustering and supervised hierarchical classification. Features, derived from different and non-commensurable models, are combined using an extended k-means clustering algorithm and supervised hierarchical Bayesian networks incorporating any available prior information about the domain.