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A novel approach to active sampling is proposed for the semi automatic selection of training patterns in a given pool of candidates. In the method proposed, each candidate is ranked with a double criterion: first, informativeness of the candidate is assessed using the Support Vector Machine (SVM) real valued decision function. Then, diverse sampling is ensured by considering the relative position...
In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve...
In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated...
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