Deep Learning-Based Super-Resolution Techniques for Enhancing Satellite Imagery

Abstract

The rapid development of deep learning technologies has opened new avenues for improving the resolution of satellite imagery, which is essential for various applications, including environmental monitoring and urban planning. Super-resolution (SR) techniques leverage deep learning models to reconstruct high-resolution images from their low-resolution counterparts, thereby enhancing the details and features present in satellite images. This paper reviews the state-of-the-art deep learning-based super-resolution techniques, examining their methodologies, performance metrics, and applications in enhancing satellite imagery. Key deep learning models, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), are discussed, highlighting their effectiveness in various contexts. Additionally, the paper explores the implications of these technologies in monitoring environmental changes, urban expansion, and disaster management. The challenges faced in implementing these techniques, such as computational cost and data availability, are also addressed, providing insights into future research directions and potential solutions.