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.
翻译:对于卫星图象来说,云层的存在是一个问题,因为云层掩盖了一半以上至三分之二的地面信息,这个问题在无噪音环境中造成许多需要无缝监测的数据和其他应用的可靠性问题。从图像中清除云层,同时保持背景像素完整,可有助于解决上述问题。最近,深层学习方法通过展示有希望的结果,对云的清除进行研究变得很受欢迎,其中General Aversarial Networks(GAN)表现得相当好。在这个项目中,我们的目标是利用“注意GAN”处理从卫星图像中清除云层的问题,然后通过利用传统的GANs和自动生成器复制结果来比较我们的结果。我们使用RICE数据集,该项目的结果可以用来开发需要无云卫星图像的应用。此外,我们的结果可以有助于进一步的研究改进。