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[ABT99] Hierarchical Clustering of Self-Organizing Maps for Cloud Classification

Revue Internationale avec comité de lecture : Journal Neurocomputing, vol. 30(1), 1999
motcle:
Résumé: 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) In a second step the codebook vectors of the map are clustered using Agglomerative Hierarchical Clustering (AHC). Each pixel takes the label of its nearest codebook vector. A practical application to Meteosat images illustrates the relevance of our approach.

Commentaires: pp 47-52

Collaboration: UPMC

BibTeX

@article {
ABT99,
title="{Hierarchical Clustering of Self-Organizing Maps for Cloud Classification}",
author="C. Ambroise and F. Badran and S. Thiria and G. Sze",
journal="Neurocomputing",
year=1999,
volume=30,
number=1,
note="{pp 47-52}",
}