MULTISPECTRAL CLASSIFICATION BY A MODULAR NEURAL-NETWORK ARCHITECTURE

Deals with the application of a modular neural network system to classification of remote sensed data characterized by a high number of spectral bands. The classification task was separated into two phases: (i) unsupervised data compression by a linear propagation network (LPN); (ii) supervised feature classification by a multi layer perceptron (MLP). In this work the unsupervised LPN module has been introduced to speed up the training phase of the MPL module. The performance of the MLP classifier trained, respectively, with the original uncompressed data and with the data preprocessed by the LPN module have been compared in terms of classification accuracy and computation time. The experimental results prove that even though the overall classification accuracy is comparable in both the experiments, the convergence time spent in the MLP training with the compressed data has been significantly reduced}, keywords={feedforward neural nets;geophysical signal processing;geophysical techniques;geophysics computing;image classification;image colour analysis;learning (artificial intelligence);multilayer perceptrons;neural nets;optical information processing;remote sensing;feature classification;feedforward neural network;geophysical measurement technique;image classification;land surface;linear propagation network;modular neural network architecture;multilayer perceptron;multispectral method;neural net;optical image processing;optical imaging;terrain mapping;training;unsupervised data compression;Artificial neural networks;Computer architecture;Convergence;Data analysis;Data compression;Ear;Image coding;Image processing;Neural networks;Remote sensing}