A Conditional Generative Adversarial Network To Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM(2018)
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.
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关键词
SAR, optical remote sensing, data fusion, deep learning, generative adversarial network (GAN), cloud-removal
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