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Generative adversarial network-based super-resolution reconstruction of remote sensing images
Longbao Wang1,2, LiSheng Liu3, Qing Yu3
1College of Computer Science and Software Engineering, Hohai University, Nanjing, China. wlb@hhu.edu.cn.
Scientific Reports
|March 4, 2026
Summary
This study introduces a lightweight Super-Densely Connected Generative Adversarial Network (SDGAN) to improve remote sensing image quality. The new model enhances resolution and reduces processing time for better urban planning and resource management.
Area of Science:
- Remote Sensing
- Computer Vision
- Artificial Intelligence
Background:
- Satellite imaging is vital for resource management and urban planning.
- Existing super-resolution methods face challenges with image defects, computational inefficiency, and limited generalization.
Purpose of the Study:
- To develop a lightweight framework for enhancing remote sensing image quality.
- To address limitations of current super-resolution techniques in handling image defects and computational demands.
Main Methods:
- Proposed SDGAN (Super-Densely Connected Generative Adversarial Network) framework.
- Integration of a super-dense residual module with 2D convolution kernels.
- Utilized a dual-discriminator system with attention U-Net for improved efficiency.
Main Results:
- Achieved state-of-the-art performance on UCMerced-LandUse and WHU-RS19 datasets.
- Demonstrated significant runtime reductions: 21.72% and 14.47% over baselines.
- Effectively balanced reconstruction quality with computational efficiency.
Conclusions:
- SDGAN offers an efficient and effective solution for super-resolution in remote sensing.
- The framework minimizes artifacts and enhances local feature representation.
- SDGAN shows promise for real-world applications in urban planning and resource management.
