论文标题
MM811项目报告：卫星图像中的云检测和去除
MM811 Project Report: Cloud Detection and Removal in Satellite Images
论文作者
论文摘要
对于卫星图像，云的存在提出了一个问题，因为云层掩盖了地面信息的一半以上。此问题导致许多问题在无噪声环境中可靠性传达数据和其他需要无缝监控的应用程序。从图像中删除云，同时保持背景像素完整可以帮助解决上述问题。最近，深度学习方法通过证明有希望的结果来研究云的去除而变得流行，其中生成的对抗网络（GAN）显示出相当好的性能。在这个项目中，我们旨在使用注意力gan来解决从卫星图像中删除云，然后通过再现使用传统gan和自动编码器获得的结果来比较我们的结果。我们使用米饭数据集。该项目的结果可用于开发需要无云卫星图像的应用程序。此外，我们的结果可能有助于进一步的研究改进。
For satellite images, the presence of clouds presents a problem as clouds obscure more than half to two-thirds of the ground information. This problem causes many issues for reliability in a noise-free environment to communicate data and other applications that need seamless monitoring. Removing the clouds from the images while keeping the background pixels intact can help address the mentioned issues. Recently, deep learning methods have become popular for researching cloud removal by demonstrating promising results, among which Generative Adversarial Networks (GAN) have shown considerably better performance. In this project, we aim to address cloud removal from satellite images using AttentionGAN and then compare our results by reproducing the results obtained using traditional GANs and auto-encoders. We use RICE dataset. The outcome of this project can be used to develop applications that require cloud-free satellite images. Moreover, our results could be helpful for making further research improvements.
