A Survey of Generative Adversarial Networks for Satellite Imagery: Applications, Image Types, Tasks, and Challenges

Abstract

Satellite images are crucial in diverse fields, including agriculture, urban planning, and environmental monitoring. However, traditional image processing methods are inadequate to address numerous challenges, like data-related, computational, and land cover-specific issues, which frequently impact the accuracy and quality of satellite imagery. Generative adversarial networks (GANs), a significant advancement in artificial intelligence (AI), have demonstrated potential for enhancing satellite image processing. Therefore, this research reviews various GAN variants, with their related applications and categorization. This survey emphasizes recent literature for the three years and reviews GAN applications, image types, tasks, and challenges. Moreover, present comparative performance results of recent GAN models designed specifically for satellite imagery, highlighting trends in architecture, loss, and training. In addition, state-of-the-art GANs show substantial enhancements in the satellite image areas. The research presents five tasks of GANs in satellite imagery processing, such as data augmentation, segmentation, image reconstruction, translation, and surveillance. The most common evaluation metrics used to assess the performance of GAN-based satellite imaging systems are presented in this study. Finally, future studies should concentrate on improving these models for broad applications, prioritizing real-time data processing and overcoming computational challenges.