Akash-Ramjyothi / Cloud-and-Fog-removal-System-using-Generative-Adversarial-Networks-Satellite-Image-Processing

Developed an AI System based on Generative Adversarial Networks (GANs) to predict and remove the Clouds and Fog from an Image captured from Satellite. Gets input of a Satellite Image with Clouds and outputs a predicted landscape without clouds. Demo prototype for my internship at ISRO Hyderabad campus National Remote Sensing Centre. Actual project has been configured and tested through Bhuvan.

Cloud and Fog removal System using Generative Adversarial Networks - Satellite Image Processing

📜 Description:

Developed an AI System based on Generative Adversarial Networks (GANs) to predict and remove the Clouds and Fog from an Image captured from Satellite. Gets input of a Satellite Image with Clouds and outputs a predicted landscape without clouds. Demo prototype for my internship at Indian Space Research Organization (ISRO)'s Hyderabad campus National Remote Sensing Centre (NRSC). Actual project has been configured and tested through Bhuvan.

Sample Demo:

Demo 1:

Demo 2:

Fog Removal Demo:

👦 Developed By:

Akash Ramjyothi

               ☎️ PH:[phone].

📃 ISRO Internship Certificate:

📜 Research Paper published:

https://hal.archives-ouvertes.fr/hal-03462652

📜 Internship Final Report link:

https://drive.google.com/file/d/1huYYi0UTJC2ODtjjyxIQqs8vwjzXRDTL/view?usp=sharing

💥 How to Contribute?

• Take a look at the Existing Issues or create your own Issues!
• Wait for the Issue to be assigned to you after which you can start working on it.
• Fork the Repo and create a Branch for any Issue that you are working upon.
• Create a Pull Request which will be promptly reviewed and suggestions would be added to improve it.
• Add Screenshots to help me know what this Code is all about.

🌐 References Used:

• https://arxiv.org/abs/2012.12180
• https://ieeexplore.ieee.org/document/8519033
• https://openaccess.thecvf.com/content_WACV_2020/papers/Sarukkai_Cloud_Removal_from_Satellite_Images_using_Spatiotemporal_Generator_Networks_WACV_2020_paper.pdf