Abstract
This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training examples. This exacerbates the undesirable behaviors of neural networks such as memorization and sensitivity to out-of-distribution samples. This work addresses these issues with three contributions. First, we propose a simple, yet effective data augmentation routine, termed Spectral Mixup, to construct effective virtual training samples. Second, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the auxiliary task RGB image SR can provide additional supervision. Finally, we extend the network to a semi-supervised setting so that it can learn from datasets containing low-resolution HSIs only. With these contributions, our method is able to learn from heterogeneous datasets and lift the requirement for having a large amount of HD HSI training samples. Extensive experiments on four datasets show that our method outperforms existing methods significantly and underpin the relevance of our contributions. The code of this work will be released soon.
Abstract (translated)
URL
https://arxiv.org/abs/2101.07589
