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.
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Bruzzone, L., Cossu, R. (2001). A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_26
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DOI: https://doi.org/10.1007/3-540-48219-9_26
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