Real-World Unsupervised Remote Sensing Image Super-Resolution: Addressing Challenges, Solution, and Future Prospects

The field of remote sensing plays a crucial role in various applications, ranging from environmental monitoring to urban planning. The growing demand for high-resolution imagery in real-world scenarios has motivated the advancement of super-resolution techniques that enhance the spatial information of remote-sensing images. Super-resolution aims to recover finer details and textures that are not readily differentiable in the original low-resolution image. This chapter dives into real-world unsupervised remote sensing image super-resolution, focusing on the challenges, innovative solutions, and promising future prospects. Challenges in this domain stem from the inherent complexities of remote sensing data, including varying weather conditions, sensor limitations, geometric distortions, the absence of paired high-resolution and low-resolution images for training neural networks, and the difficulty of designing effective super-resolution models. Additionally, adapting algorithms to diverse landscapes and imaging conditions poses a substantial challenge. By addressing challenges through innovative solutions and envisioning future prospects, researchers can unlock the full potential of super-resolution techniques by integrating domain adaptation techniques that could improve the model’s generalization across different regions and imaging conditions, contributing to more accurate and detailed remote sensing imagery for a wide array of applications.