  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.
