RBF two-stage learning networks exploiting supervised data in the selection of hidden unit parameters: an application to SAR data classification

Radial basis function (RBF) classifiers, which consist of an hidden and an output layer, are traditionally trained with a two-stage hybrid learning approach. This approach combines an unsupervised (data-driven) first stage to adapt RBF hidden layer parameters with a supervised (error-driven) second stage to learn RBF output weights. Several simple strategies that exploit labeled data in the adaptation of centers and spread parameters of RBF hidden units may be pursued. Some of these strategies have been shown to reduce traditional weaknesses of RBF classification, while typical advantages are maintained. In the field of remotely sensed image classification, the authors compare a traditional RBF two-stage hybrid learning procedure with an RBF two-stage learning technique exploiting labeled data to adapt hidden unit parameters.