  Data augmentation is a popular technique which helps improve generalization
capabilities of deep neural networks. It plays a pivotal role in remote-sensing
scenarios in which the amount of high-quality ground truth data is limited, and
acquiring new examples is costly or impossible. This is a common problem in
hyperspectral imaging, where manual annotation of image data is difficult,
expensive, and prone to human bias. In this letter, we propose online data
augmentation of hyperspectral data which is executed during the inference
rather than before the training of deep networks. This is in contrast to all
other state-of-the-art hyperspectral augmentation algorithms which increase the
size (and representativeness) of training sets. Additionally, we introduce a
new principal component analysis based augmentation. The experiments revealed
that our data augmentation algorithms improve generalization of deep networks,
work in real-time, and the online approach can be effectively combined with
offline techniques to enhance the classification accuracy.
