2 March 1994 Experiences from operational cloud classifier based on self-organizing map
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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-organizing 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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ari J. E. Visa, Ari J. E. Visa, K. Valkealahti, K. Valkealahti, Jukka Iivarinen, Jukka Iivarinen, O. Simula, O. Simula, } "Experiences from operational cloud classifier based on self-organizing map", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169997; https://doi.org/10.1117/12.169997


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