ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and
  Multispectral Data Fusion
Jinghui Qin Lihuang Fang Ruitao Lu Liang Lin Yukai Shi
Deep learning-based hyperspectral image (HSI) super-resolution, which aims to
generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image
(HSI) and multispectral image (MSI) with deep neural networks (DNNs), has
attracted lots of attention. However, neural networks require large amounts of
training data, hindering their application in real-world scenarios. In this
letter, we propose a novel adversarial automatic data augmentation framework
ADASR that automatically optimizes and augments HSI-MSI sample pairs to enrich
data diversity for HSI-MSI fusion. Our framework is sample-aware and optimizes
an augmentor network and two downsampling networks jointly by adversarial
learning so that we can learn more robust downsampling networks for training
the upsampling network. Extensive experiments on two public classical
hyperspectral datasets demonstrate the effectiveness of our ADASR compared to
the state-of-the-art methods.