Neural Networks in Multiple Classifier Systems for Remote-Sensing Image Classification

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Summary In recent years there has been a growing interest in the development of super-vised classification techniques with higher classification reliability of satelliteimages. The superiority of one technique over the others cannot be claimed.Many experimental results showed that the classification accuracy dependsmore on the particular application than on the technique chosen to performthe task. Moreover in many applications it is very difficult to design a clas-sification system that exhibits the required accuracy for the final classifica-tion product. Therefore a new technique is emerging that considers multipleclassifier systems (MCSs) instead of a single classification technique. Neuralnetworks can participate effectively in a MCS in several ways. This chapterfocuses on the role of neural networks in MCSs, which can be either: (1) anindividual classifier among the classifiers ensemble, (2) the fusion center thatintegrates the decisions of individual classifiers, (3) the selector that pickssome classifiers’ decisions and ignores the others, (4) or the selector and fuserat the same time.The chapter surveys key neural and non-neural methods in this area anddevelops some new ones. It also considers several architectures of neural net-works including traditional feed-forward neural networks, probabilistic neuralnetworks and radial-basis nets. All methods are evaluated on two real remote-sensing datasets, one of which is the standard benchmark Satimage dataset.