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.
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|
|Department or Research center: ||Information Engineering and Computer Science|
|Subjects: ||T Technology > T Technology (General)|
|Uncontrolled Keywords: ||multiple-classifier systems, unsupervised retraining algorithms, maximum-likelihood classifier, radial basis function neural networks, expectation-maximization algorithm.|
|Additional Information: ||To be published on Information Fusion|
|Report Number: ||DIT-02-024|
|Repository staff approval on: ||21 Jan 2003|
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