An ensemble active learning approach for spectral-spatial classification of hyperspectral images

Augmenting spectral features with spatial features for hyperspectral image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial features from neighboring pixels. However, the resulting high dimensional input data, which are often difficult and expensive to obtain, require large quantities of labeled data to train a robust supervised classifier. To alleviate the “curse of dimensionality”, we propose an ensemble based active learning approach that incorporates spatial features for each feature subset (view) independently. Specifically, in each view, the spatial features are extracted from an optimum segmentation selected from the hierarchical segmentation (HSeg). The proposed approach is applied to a benchmark hyperspectral data set, and the experimental results demonstrate the efficacy of the proposed method compared to other state-of-the-art active learning classification methods.