Experiences from Operational Cloud Classifier Based on Self-Organising Map
Ari Visa, Kimmo Valkealahti, Jukka Iivarinen, Olli Simula
Helsinki University of Technology
Laboratory of Information and Computer Science
Rakentajanaukio 2 C, FIN-02150 Espoo
Finland
Abstract
A new operational system to interpret satellite images is represented.
The described method is adaptive. It is trained by examples. In the reported
application a combination of textural and spectral measures is used as a
feature vector. The adaptation or learning of the extracted feature
vectors occurs by a self-organising process. As a result a topological
feature map is generated. The map is identified by known samples,
examples of clouds. The map is used later on as a code book for cloud
classification. The obtained verification results are good.
The represented method is general in the sense that by reselecting
features it can be applied to new problems.
In SPIE Vol. 2243 Applications of Artificial Neural Networks V ,
volume 2243, pages 484-495, Orlando, Florida, April 5-8, 1994.
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