Active Learning Improved by Neighborhoods and Superpixels for Hyperspectral Image Classification

Active learning (AL) is a promising solution to hyperspectral image classification with very few initial labeled samples. Although previous AL heuristics have exhibited encouraging results, some challenges are still open. On the one hand, traditional AL heuristics measured uncertainty only in feature domain (i.e., spectral or spectral-spatial features) with a pixelwise manner, which ignores the spatial uncertainty. On the other hand, traditional batch-mode AL methods rarely considered spatial homogeneity, since they selected a batch of samples from the candidates, which will induce redundancy unavoidably. To overcome these issues, we first propose an enhanced uncertainty measure considering the neighborhood information. We then propose to use simple linear iterative clustering for generating superpixels, where the selected batch samples are constrained to be from different superpixels, which improves the diversity of the selected samples. The experimental results with two popular hyperspectral data sets indicate that the proposed methods can significantly improve the classification accuracy compared with the traditional methods.