论文标题
从Sentinel-2生成合成的多光谱卫星图像
Generating Synthetic Multispectral Satellite Imagery from Sentinel-2
论文作者
论文摘要
多光谱卫星图像为许多环境和社会经济应用提供了全球规模的有价值的数据。但是，基于这些图像的构建监督的机器学习模型可能需要地面参考标签，这些标签在全球规模上不可用。在这里，我们提出了一个生成模型，以基于Sentinel-2数据生成多分辨率的多光谱图像。由此产生的合成图像与人类的真实图像没有区别。这项技术为将来的工作铺平了道路，以生成标记的合成图像，可用于数据扩大数据稀缺区域和应用程序。
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.
