6533b829fe1ef96bd1289704

RESEARCH PRODUCT

Regularized RBF Networks for Hyperspectral Data Classification

Gustavo Camps-vallsJose MorenoAntonio J. Serrano-lópezJavier Calpe-maravillaJosé D. Martín-guerreroLuis Gómez-chova

subject

Artificial neural networkbusiness.industryComputer scienceMathematicsofComputing_NUMERICALANALYSISComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHyperspectral imagingPattern recognitionSupport vector machineComputingMethodologies_PATTERNRECOGNITIONComputer Science::Computational Engineering Finance and ScienceRobustness (computer science)Computer Science::Computer Vision and Pattern RecognitionRadial basis function kernelRadial basis functionArtificial intelligenceAdaBoostbusinessCurse of dimensionality

description

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.

yearjournalcountryeditionlanguage
2004-01-01
https://doi.org/10.1007/978-3-540-30126-4_53