Optimal combinations of pattern classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for combining classifiers employing shared and distinct pattern representations
Pattern Recognition Letters - special issue on pattern recognition in practice V
Adaptive confidence transform based classifier combination for Chinese character recognition
Pattern Recognition Letters
Pattern Recognition Letters
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Parallel consensual neural networks
IEEE Transactions on Neural Networks
A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
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In this paper, the problem of unsupervised retraining of supervised classifiers for the analysis of multitemporal remote-sensing images is considered. In particular, two techniques are proposed for the unsupervised updating of the parameters of the maximum-likelihood and the radial basis function neural-network classifiers, on the basis of the distribution of a new image to be classified. Given the complexity inherent with the task of unsupervised retraining, the resulting classifiers are intrinsically less reliable and accurate than the corresponding supervised approaches, especially for complex data sets. In order to overcome this drawback, we propose to use methodologies for the combination of different classifiers to increase the accuracy and the reliability of unsupervised retraining classifiers. This allows one to obtain in an unsupervised way classification performances close to the ones of supervised approaches.