A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps

Abstract

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.KeywordsRadial Basis FunctionRadial Basis Function Neural NetworkMultispectral ImageUnsupervised ClassificationMultiple Classifier SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.