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Deep learning-based hyperspectral image (HSI) super-resolution, which aims togenerate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image(HSI) and multispectral image (MSI) with deep neural networks (DNNs), hasattracted lots of attention. However, neural networks require large amounts oftraining data, hindering their application in real-world scenarios. In thisletter, we propose a novel adversarial automatic data augmentation frameworkADASR that automatically optimizes and augments HSI-MSI sample pairs to enrichdata diversity for HSI-MSI fusion. Our framework is sample-aware and optimizesan augmentor network and two downsampling networks jointly by adversariallearning so that we can learn more robust downsampling networks for trainingthe upsampling network. Extensive experiments on two public classicalhyperspectral datasets demonstrate the effectiveness of our ADASR compared tothe state-of-the-art methods.

ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion