Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery

摘要

For high-dimensional data, the appropriate selection of features has a significant effect on the cost and accuracy of an automated classifier. In this paper, a feature selection technique using genetic algorithms is applied. For classifi- cation, crisp and fuzzy k-nearest neighbor(kNN) classifiers are compared. Composite fuzzy classifier architectures are investigated. Experiments are conducted on airborne visible/infrared imaging spectrometer(AVIRIS) data, and the results are evaluated in the paper.