Experiences from operational cloud classifier based on self-organizing map
Ari J. E. Visa, Kimmo Valkealahti, Jukka Iivarinen, Olli Simula · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.