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An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve...
This paper reports on monitoring land cover in the urban and sub-urban area of Rome, Italy, by multi-temporal ERS 1-2 SLC SAR images. The identification of the SAR image parameters, including backscattering, degree of interferometric coherence and textural pixel-based features to be exploited in classification, is discussed. The information extracted from the SAR images is fused and processed by a...
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