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Image classification usually requires the availability of reliable reference data collected for the considered image to train supervised classifiers. Unfortunately when time series of images are considered, this is seldom possible because of the costs associated with reference data collection. In most of the applications it is realistic to have reference data available for one or few images of a time...
This paper presents a novel system for automatically updating land-cover maps by classifying remote sensing image time series. The proposed system assumes that a reliable training set is available only for one of the images (i.e., the source domain) in the time series, whereas it is not for another image to be classified (i.e., the target domain). To effectively classify the target domain the proposed...
This paper presents a novel active-learning (AL) technique in the context of the cascade classification of multitemporal remote-sensing images for updating land-cover maps. The proposed AL technique is based on the selection of unlabeled samples that have maximum uncertainty on their labels assigned by cascade classification, and explicitly exploits temporal correlation between multitemporal images...
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