Hierarchical clustering of self-organizing maps for cloud classification

@article{Ambroise2000HierarchicalCO,
  title={Hierarchical clustering of self-organizing maps for cloud classification},
  author={Christophe Ambroise and Geni{\`e}ve S{\`e}ze and Fouad Badran and Sylvie Thiria},
  journal={Neurocomputing},
  year={2000},
  volume={30},
  pages={47-52}
}
This paper presents a new method for segmenting multispectral satellite images. The proposed method is unsupervised and consists of two steps. During the rst step the pixels of a learning set are summarized by a set of codebook vectors using a Probabilistic Self-Organizing Map (PSOM, [9]) In a second step the codebook vectors of the map are clustered using Agglomerative Hierarchical Clustering (AHC, [7]). Each pixel takes the label of its nearest codebook vector. A practical application to… CONTINUE READING