Semi-supervised multi-metric active learning for classification of hyperspectral images

Augmenting spectral features with spatial features for hyperspectral image classification can often improve the classification accuracy. However, the resulting high dimensionality and the typically scarce quantities of labeled samples impose significant challenges for supervised techniques. To alleviate these two issues simultaneously, in this paper, a semi-supervised multi-metric learning method is proposed for feature extraction and combined with active learning (AL) into a unique framework. In particular, the proposed metric learning approach learns distinct projection matrices jointly, and each metric is assigned to one type of feature. Moreover, the proposed regularizer helps avoid overfitting by taking advantage of the unlabeled data information. Finally, different types of features are projected into a common feature space in which AL is performed in conjunction with k-nearest neighbor (kNN) classification. Experiments on two benchmark hyperspectral datasets illustrate the effectiveness of the proposed framework compared to other state-of-the-art AL classification methods.