Demir, Begüm and Persello, Claudio and Bruzzone, Lorenzo (2009) Batch Mode Active Learning Methods for the Interactive Classification of Remote Sensing Images. UNSPECIFIED. (Submitted)
This paper investigates different batch mode active learning techniques for the classification of remote sensing (RS) images with support vector machines (SVMs). This is done by generalizing to multiclass problems techniques defined for binary classifiers. The investigated techniques exploit different query functions, which are based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is associated to the confidence of the supervised algorithm in correctly classifying the considered sample, while the diversity criterion aims at selecting a set of unlabeled samples that are as more diverse (distant one another) as possible, thus reducing the redundancy among the selected samples. The combination of the two criteria results in the selection of the potentially most informative set of samples at each iteration of the active learning process. Moreover, we propose a novel query function that is based on a kernel clustering technique for assessing the diversity of samples and a new strategy for selecting the most informative representative sample from each cluster. The investigated and proposed techniques are theoretically and experimentally compared with state-of-the-art methods adopted for RS applications. This is accomplished by considering VHR multispectral and hyperspectral images. By this comparison we observed that the proposed method resulted in better accuracy with respect to other investigated and state-of-the art methods on the considered data sets. Furthermore, we derived some guidelines on the design of active learning systems for the classification of different types of RS images.
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