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14 December 1999 Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote sensing images
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Abstract
Several applications of supervised classification of remote- sensing images involve the periodical mapping of a fixed set of land-cover classes on a specific geographical area. These applications require the availability of a training set (and hence of ground-truth information) for each new image analyzed. However, the collection of ground truth information is a complex and expensive process that only in few cases can be performed every time that a new image is acquired. This represents a serious drawback of classical supervised classifiers. In order to overcome such a drawback, an unsupervised retraining technique for supervised maximum- likelihood (ML) classifiers is proposed in this paper. Such a technique, which is based on the Expectation-Maximization (EM) algorithm, allows the statistical parameters of an already trained ML classifier to be updated so that a new image, for which a training set is not available, can be classified with an acceptable accuracy. Experiments, which have been carried out on a multitemporal data set, confirm the effectiveness of the proposed technique.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lorenzo Bruzzone and Diego Fernandez-Prieto "Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote sensing images", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); https://doi.org/10.1117/12.373254
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