Feature extraction and pattern classification of remote sensing data by a modular neural system

A modular neural network architecture has been used for the classification of remote sensed data in two experiments carried out to study two different but rather usual situations in real remote sensing applications. Such situations concern the availability of high-dimensional data in the first setting and an imperfect data set with a limited number of features in the second. The learning task of the supervised multilayer perceptron classifier has been made more efficient by preprocessing the input with unsupervised neural modules for feature discovery. The linear propagation network is introduced in the first experiment to evaluate the effectiveness of the neural data compression stage before classification, whereas in the second experiment data clustering before labeling is evaluated by the Kohonen self-organizing feature map network. The results of the two experiments confirm that modular learning performs better than nonmodular learning with respect to both learning quality and speed.