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.

翻译：基于深度学习的高光谱图像超分辨率通过融合高光谱图像与多光谱图像并利用深度神经网络生成高空间分辨率的高光谱图像，已引起广泛关注。然而，神经网络需要大量训练数据，这限制了其在实际场景中的应用。本文提出一种新颖的对抗性自动数据增强框架ADASR，该框架能够自动优化并增强高光谱-多光谱图像样本对，从而丰富数据多样性以支持高光谱-多光谱融合。我们的框架具有样本感知特性，通过对抗学习联合优化一个增强器网络和两个下采样网络，使得我们能够训练更鲁棒的下采样网络，进而用于上采样网络的训练。在两个公开经典高光谱数据集上的大量实验表明，与最先进方法相比，我们的ADASR框架具有有效性。