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Conference Paper

Active learning for classification of remote sensing images


Persello,  C
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bruzzone, L., & Persello, C. (2009). Active learning for classification of remote sensing images. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009) (pp. III-693-III-696). Piscataway, NJ, USA: IEEE.

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This paper presents an analysis of active learning techniques for the classification of remote sensing images and proposes a novel active learning method based on support vector machines (SVMs). The proposed method exploits a query function for the inclusion of batches of unlabeled samples in the training set, which is based on the evaluation of two criteria: uncertainty and diversity. This query function adopts a stochastic approach to the selection of unlabeled samples, which is based on a function of uncertainty estimated from the distribution of errors on the validation set (which is assumed available for the model selection of the SVM classifier). Experimental results carried out on a very high resolution image confirm the effectiveness of the proposed active learning technique, which results more accurate than standard methods.