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
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
The ``Test and Select'' Approach to Ensemble Combination
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
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
Machine learning approaches for high-resolution urban land cover classification: a comparative study
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
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We propose a system for a regular updating of land-cover maps based on the use of temporal series of remote sensing images. Such a system is composed of an ensemble of partially unsupervised classifiers integrated in a multiple classifier architecture. The updating problem is formulated under the complex constraint that for some images of the considered multitemporal series no ground-truth information is available. With respect to the authors' previous works on this topic [1-3], the novel contribution of this paper consists in: i) developing partially unsupervised classification algorithms defined in the framework of a cascade-classifier approach; ii) defining a specific strategy for the generation of an ensemble of classifiers, which exploits the peculiarities of the cascade-classifier approach. These novel aspects result in the definition of more robust and accurate classification systems.