Active learning for classification of remote sensing images

Bruzzone, L; Persello, C

doi:10.1109/IGARSS.2009.5417857

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Active learning for classification of remote sensing images

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5417857
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Bruzzone, L., & Persello, C. (2009). Active learning for classification of remote sensing images. In 2009 IEEE International Geoscience and Remote Sensing Symposium (pp. III-693-III-696). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C3FB-C

Abstract

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.