ABSTRACT
A neural network-based methodology for time and memory efficient supervised or unsupervised classification in heavily demanding applications is presented in this chapter. Significantly increased speed in the design (training) of neural, fuzzy and statistical classifiers as well as in the classification phase is achieved by: (a) using a self-organizing feature map (SOFM) for vector quantization and indexed representation of the input data space; (b) appropriate training set reduction using the SOFM prototypes followed by necessary modifications of the training algorithms (supervised techniques); (c) clustering of neurons on maps instead of clustering the original data (unsupervised techniques); and (d) fast indexed classification. Finally, a demonstration of this methodology involving the design of multiple classifiers is performed on Land-Cover classification of multispectral satellite image data showing increased speed with respect to both training and classification times.
