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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. Sèze", | |
| journal | = | "Neurocomputing", | |
| year | = | 1999, | |
| volume | = | 30, | |
| number | = | 1, | |
| note | = | "{pp 47-52}", | |
| } | |||
