Generating Synthetic Multispectral Satellite Imagery from Sentinel-2
arXiv:2012.03108
Abstract
Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale. Here, we propose a generative model to produce multi-resolution multi-spectral imagery based on Sentinel-2 data. The resulting synthetic images are indistinguishable from real ones by humans. This technique paves the road for future work to generate labeled synthetic imagery that can be used for data augmentation in data scarce regions and applications.
Presented at the AI for Earth Sciences Workshop at NeurIPS 2020
References in corpus (5)
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Improved Training of Wasserstein GANs
- Improved Techniques for Training GANs
- MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
- SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro