ICA and GA feature extraction and selection for cloud classification

Authors:
Miguel Macías-Macías;Carlos J. García-Orellana;Horacio González-Velasco;Ramón Gallardo-Caballero
Affiliations:
Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Centro Universitario de Mérida, Mérida, (Badajoz), Spain;Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Centro Universitario de Mérida, Mérida, (Badajoz), Spain;Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Centro Universitario de Mérida, Mérida, (Badajoz), Spain;Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Centro Universitario de Mérida, Mérida, (Badajoz), Spain
Venue:
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Year:
2005

Citing 13
Cited 1

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications

Neural Networks
Soft computing: integrating evolutionary, neural, and fuzzy systems

Soft computing: integrating evolutionary, neural, and fuzzy systems
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
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
Image Feature Extraction by Sparse Coding and Independent Component Analysis

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines

Intelligent Data Analysis
A comparison of PCA, ICA and GA selected features for cloud field classification

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
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
A study of cloud classification with neural networks using spectral and textural features

IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks
Temporal updating scheme for probabilistic neural network with application to satellite cloud classification

IEEE Transactions on Neural Networks

A GA based approach to improving the ICA based classification models for tumor classification

WSEAS Transactions on Information Science and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we tackle a particular case of image segmentation, the automatic detection of the amount and type of clouds over the Iberian Peninsula using satellite images. To segment the images we classify each pixel of the image into one of the classes defined using a neural network and a set of features representative of the pixel. We emphasized in the preprocessing stage, extracting and selecting a suitable set of features from the images to carry out an optimal classification. To carry out the feature extraction we use the independent component analysis (ICA) algorithm. The features extracted with this algorithm are very dependent on the dimension of the patches, so we extract several sets of features, one for each value of the dimension of the patch. All of these sets of features are joined together to form an initial characteristic vector of the pixels of the images. Finally, we reduce the dimensionality of this initial characteristic vector by means of Genetic Algorithms (GA), choosing the best subset of features that offer the best classification results.