Batch-mode active learning (AL) approaches are dedicated to the training sample set selection for classification, regression, and retrieval problems, where a batch of unlabeled samples is queried at each iteration by considering both the uncertainty and diversity criteria. However, for remote sensingapplications, the conventional methods do not consider the spatial coherence between the training samples, which will lead to the unnecessary cost. Based on the above two points, this paper proposes a spatial coherence-based batch-mode AL method.
First, mean shift clustering is used for the diversity criterion, and thus the number of new queries can be varied in the different iterations. Second, the spatial coherence is represented by a two-level segmentation map which is used to automatically label part of the new queries. To get a stable and correct second-level segmentation map, a new merging strategy is proposed for the mean shift segmentation. The experimental results with two real remotesensing image data sets confirm the effectiveness of the proposed techniques, compared with the other state-of-the-art methods.