Bruzzone, Lorenzo and Cossu, Roberto and Vernazza, Gianni (2002) Combining Parametric and Non-parametric Algorithms for a Partially Unsupervised Classification of Multitemporal Remote-Sensing Images. UNSPECIFIED.
Abstract
In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new image of the considered site. In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood (ML) classifier and a radial basis function (RBF) neural-network classifier, on the basis of the distribution of the new image to be classified. Experimental results carried out on a multitemporal and multisource remote-sensing data set confirm the effectiveness of the proposed system.
| Item Type: | Departmental Technical Report |
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| Department or Research center: | Information Engineering and Computer Science |
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| Subjects: | T Technology > T Technology (General) |
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| Uncontrolled Keywords: | multiple-classifier systems, unsupervised retraining algorithms, maximum-likelihood classifier, radial basis function neural networks, expectation-maximization algorithm. |
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| Additional Information: | To be published on Information Fusion |
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| Report Number: | DIT-02-024 |
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| Repository staff approval on: | 21 Jan 2003 |
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