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Support Vector Machine Active Learning Through Significance Space Construction
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AI
DOI：10.1109/LGRS.2010.2083630.png)
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Abstract
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Active learning is showing to be a useful approach to improve the efficiency of the classification process for remote sensing images. This letter introduces a new active learning strategy specifically developed for support vector machine (SVM) classification. It relies on the idea of the following: 1) reformulating the original classification problem into a new problem where it is needed to discriminate between significant and nonsignificant samples, according to a concept of significance which is proper to the SVM theory; and 2) constructing the corresponding significance space to suitably guide the selection of the samples potentially useful to better deal with the original classification problem. Experiments were conducted on both multi-and hyperspectral images. Results show interesting advantages of the proposed method in terms of convergence speed, stability, and sparseness.
Keywords:
Active learning
hyperspectral images
support vector machines (SVMs)
very-high resolution (VHR) images
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Automatic Ground-Truth Validation With Genetic Algorithms for Multispectral Image Classification
PREAI
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