MM811 Project Report: Cloud Detection and Removal in Satellite Images

TL;DR

This paper explores cloud removal in satellite images using AttentionGAN, comparing its performance with traditional GANs and auto-encoders to improve cloud-free image generation for better satellite data analysis.

Contribution

The study applies AttentionGAN to satellite cloud removal and provides a comparative analysis with traditional GANs and auto-encoders, advancing deep learning methods in this domain.

Findings

AttentionGAN outperforms traditional GANs and auto-encoders in cloud removal quality.

The approach demonstrates potential for enhancing satellite image clarity.

Results can inform future research and practical applications in satellite imagery analysis.

Abstract

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.