A Comparison of PCA and GA Selected Features for Cloud Field Classification
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Independent Component Analysis for Cloud Screening of Meteosat Images
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
ICA and GA feature extraction and selection for cloud classification
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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In this work we make a comparative study of the results obtained in the automatic interpretation of the Iberian Peninsula Meteosat images by means of neural networks techniques, in particular, multi-layer perceptrons and self organizing maps. The interpretation of these images implies their segmentation in the classes SEA (S), LAND (L), LOW CLOUDS (CL), MIDDLE CLOUDS (CM), HIGH CLOUDS (CH) and CLOUDS WITH VERTICAL GROWTH (CV).