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Cloud Removal of Satellite Images Using Convolutional Neural Network With Reliable Cloudy Image Synthesis Model

机译:基于卷积神经网络的云图像合成模型的云图去除

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Cloudy pixels in satellite images degrade the visibility of captured surface structure. We propose a novel cloudy image synthesis model and develop a cloud removal algorithm using convolutional neural network. We extract the cloud masks from real cloudy satellite images and from real sky images with clouds. Then we investigate the characteristics of real cloudy images and devise a reliable cloudy image synthesis model which considers the background surface color, misalignement of channel images, and blur in clouds. We train a hierarchical cloud removal network using the synthetic cloudy images. Experimental results demonstrate that the proposed algorithm removes the clouds from cloudy satellite images faithfully and outperforms the existing methods.
机译:卫星图像中的多云像素降低了捕获的表面结构的可见性。我们提出了一种新颖的混浊图像综合模型,并使用卷积神经网络开发云移除算法。我们从真正的多云卫星图像中提取云面具,并用云从真正的天空图像提取。然后,我们调查真正的多云图像的特征,并设计了可靠的多云图像综合模型,它考虑了背景表面颜色,信道图像的错位和云层的模糊。我们使用合成阴天图像培训分层云移除网络。实验结果表明,所提出的算法忠实地从多云的卫星图像中取出云,优于现有方法。

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