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Superpixel-based active learning for the classification of hyperspectral images

It is important to select representative patterns for successful classification of remotely sensing images. In the paper, a superpixel-based active learning (shorted as SPAL) algorithm is proposed to iteratively select informative unlabeled samples for labeling by experts. In particular, new representation of a remote sensing image is generated by grouping the data into perceptually meaningful atomic regions, i.e., superpixels which have both spectral similarity and spatial adjacency. Then, active learning is iteratively exploited to select informative samples to enlarge the training dataset. At each iteration, the unlabeled samples are firstly sorted according to a prefixed uncertain strategy. Then, the few highest uncertain samples are selected on the assumption that all the samples selected at each iteration are from different superpixels in order to guarantee the diversity. The proposed SPAL algorithm is validated by real hyperspectral images. Experimental results show that the SPAL can improve the classification accuracies as well as significant enhancement of computational efficiency.