MM811 Project Report: Cloud Detection and Removal in Satellite Images
This addresses the problem of cloud obstruction for applications requiring reliable satellite monitoring, but it is incremental as it builds on existing deep learning methods.
The project tackled cloud removal in satellite images, which obscures over half of ground information, by using AttentionGAN and comparing it to traditional GANs and auto-encoders on the RICE dataset.
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.