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In this paper, we address the problem of semi-automatic definition of training sets for the classification of remotely sensed images. We propose two approaches based on active learning aiming at removing both the proximal (low diversity) and the dense (low exploration during iterations) sampling redundancies. The first is encountered when several samples carrying similar spectral information are selected by the algorithm, while the second occurs when the heuristic is unable to explore undiscovered parts of the feature space during iterations. For this purpose, kernel k-means is used to cluster a set of uncertain candidates in the same space spanned by the kernel function defined in the SVM classification step. Two heuristics are proposed to maximize the speed of convergence to high classification accuracies: The first is based on binary hierarchical partitioning of the set of selected uncertain samples, while the second extends this approach by considering memory in the selection and thus dynamically adapts to the problem throughout the iterations. Experiments on both VHR and hyperspectral imagery confirm fast convergence of the algorithm, that outperforms state-of-the-art sampling schemes.