A CONDITIONAL GENERATIVE ADVERSARIAL NETWORK TO FUSE SAR AND MULTISPECTRAL OPTICAL DATA FOR CLOUD REMOVAL FROM SENTINEL-2 IMAGES

被引：0
|
作者：
Grohnfeldt, Claas [1 ]
Schmitt, Michael [1 ]
Zhu, Xiaoxiang [1 ,2 ]
机构：
[1] Tech Univ Munich, Signal Proc Earth Observat, Munich, Germany
[2] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany
来源：
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年
关键词：
SAR; optical remote sensing; data fusion; deep learning; generative adversarial network (GAN); cloud-removal;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we present the first conditional generative adversarial network (cGAN) architecture that is specifically designed to fuse synthetic aperture radar (SAR) and optical multi-spectral (MS) image data to generate cloud-and haze-free MS optical data from a cloud-corrupted MS input and an auxiliary SAR image. Experiments on Sentinel-2 MS and Sentinel-1 SAR data confirm that our extended SAR-Opt-cGAN model utilizes the auxiliary SAR information to better reconstruct MS images than an equivalent model which uses the same architecture but only single-sensor MS data as input.
引用
收藏
页码：1726 / 1729
页数：4
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