An Operational Cloud Classifier Based On A Self-Organized Feature Map

Kimmo Valkealahti, Jukka Iivarinen, Ari Visa, Olli Simula

Helsinki University of Technology
Laboratory of Information and Computer Science
Rakentajanaukio 2 C, SF-02150 Espoo
Finland

Abstract

Experiences from a new method to interpret satellite images is represented. The described method is adaptive. It is trained by examples. In the reported application a combination of texturel and spectral measures is used as a feature vector. The adaptation of the extracted feature vectors occurs by a self-organizing process. As a result a topological feature map is generated. The map is identified and fine tuned by known samples, examples of clouds. The map is used later on as a codebook for cloud classification. The obtained verification results are good. The represented method is general in the sense that with new features the method can be applied to new problems.
Report A19, December 1993
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