ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion
This work addresses data scarcity in hyperspectral image fusion for remote sensing applications, but it is incremental as it builds on existing adversarial and augmentation techniques.
The paper tackles the problem of limited training data for deep learning-based hyperspectral image super-resolution by proposing ADASR, an adversarial auto-augmentation framework that automatically optimizes and augments HSI-MSI sample pairs to enrich data diversity, achieving improved performance on two public datasets compared to state-of-the-art methods.
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.