Training- and Test-Time Data Augmentation for Hyperspectral Image Segmentation

Data augmentation helps improve generalization capabilities of deep neural networks when only limited ground-truth training data are available. In this letter, we propose test-time augmentation of hyperspectral data, which is executed during the inference rather than before the training of deep networks. We introduce two augmentation techniques, which can be applied at both training time and test time. The experiments revealed that our augmentations boost generalization of deep models and work in real time, and the test-time approach can be combined with training-time techniques to enhance the classification accuracy.