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This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and...
In the last few years, integrating spatial information into active learning framework has been gaining growing interest in the remote sensing community to optimize the collection of training sample set for supervised image classification. We address this problem from two directions. One of the directions focus on improving the classifier's performance to reduce the need for training samples. For this...
In this paper, we address the following problem: given an unlabeled hyperspectral image, how to select a good subset of samples for labeling as training set in hyperspectral data classification. We apply submodular active selection on a RBF kernel similarity based graph through using submodular function optimization. Submodular functions provide theoretical performance guarantees while at the same...
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