A Comprehensive study on Satellite Image Super-resolution using Diffusion and GAN based model
Abhishek Pandey, Anto Felix Immanuel, Karan Sahu, Nitin Mukesh Mukesh, D. Saxena · ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY · 2024
Object detection and feature extraction from satellite images is a crucial step while using satellite images for purposes like navigation, urban planning, weather monitoring, etc. While deep learning approaches are too common for object detection task, but when the satellite images are of low quality, the small objects are missed by detection model due to their size and visibility issue. In this paper we propose a study of two broad areas of Generative AI models namely GANs and Diffusion model and their ability to handle the low-resolution images to improve overall detection problem. We train SRGAN and Diffusion based super-resolution model on custom real-time datasets and present a comprehensive performance evaluation and analysis. We found that Diffusion model increased the object detection rate by almost 130% when compared with Raw image object detection.