Discriminative CNN via metric learning for hyperspectral classification

Convolutional neural networks (CNNs) have been demonstrated to be capable of learning effective spatial-spectral features for hyperspectral classification. However, traditional CNNs are mainly trained using classification errors in decision domain. In this paper, a metric learning based training strategy is proposed to further enhance feature separability by training CNNs in feature domain as well as decision domain. Specifically, a metric learning loss function is designed to train CNNs in the second last fully connected feature layer, instead of the last fully connected decision layer. As a result, both within-class feature similarity and between-class feature separability can be enhanced even with a small amount of training samples. Experimental results over two benchmark hyperspectral data sets demonstrate that the proposed metric learning strategy is very effective to explore more discriminative features and its performance obviously outperforms several state-of-art CNNs for classification of hyperspectral images.