Soft computing: integrating evolutionary, neural, and fuzzy systems
Soft computing: integrating evolutionary, neural, and fuzzy systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Comparative Study of Two Neural Models for Cloud Screening of Iberian Peninsula Meteosat Images
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
A study of cloud classification with neural networks using spectral and textural features
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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 a back propagation neural network (BPNN) is used for the segmentation of Meteosat images covering the Iberian Peninsula. The images are segmented in the classes land (L), sea (S), fog (F), low clouds (CL), middle clouds (CM), high clouds (CH) and clouds with vertical growth (CV). The classification is performed from an initial set of several statistical textural features based on the gray level co-occurrence matrix (GLCM) proposed by Welch [1]. This initial set of features is made up of 144 parameters and to reduce its dimensionality two methods for feature selection have been studied and compared. The first one includes genetic algorithms (GA) and the second is based on principal component analysis (PCA). These methods are conceptually very different. While GA interacts with the neural network in the selection process, PCA only depends on the values of the initial set of features.