Spatial correlated information based batch mode active learning method for remote sensing image classification

Batch-mode active learning approaches are dedicated to the problem of training sample set selection, where a batch of unlabeled samples is queried at each iteration by considering both uncertainty and diversity criteria. However, the current batch-mode approaches do not consider spatial correlation between adjacent queries pixels, thus they spend some unnecessary time costs and are accompanied by relatively high annotation costs. This paper employs mean shift segmentation to describe the spatial correlation information which is used to select most diverse samples in the geographic space and to automatically label part of the pixels that need querying. As a result, the labeling costs can be lowered sharply. Meanwhile, the number of new queries in each iteration is adaptive to the distribution of the uncertain samples, which can reduce the iterations. Experimental results obtained in the classification of a hyperspectral image confirm the effectiveness of the proposed technique.