Cloud removal of visible images utilizing the fusion of SAR imagery and adversarial learning
In response to the problem of low data utilization caused by frequent cloud and fog occlusion and interference interpretation in optical remote sensing images,this paper proposes a"radar-optics"image conversion algorithm based on the all-weather and all-weather characteristics of SAR images.Taking SAR images as input,the algorithm utilizes the strong nonlinear mapping ability of adversarial networks combined with deep learning,as well as the characteristics of adversarial learning game based learning for arbitrary data distribution,to directly generate corresponding cloud free pseudo visible light images through SAR images in cloud covered areas.The experiment shows that this method has good removal effects on thin clouds,thick clouds,and large-scale clouds and mist,and can greatly improve the availability of optical remote sensing images.
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