Kawulok M. Super-Resolution for Remote Sensing 2024

Textbook in PDF format

This book provides a comprehensive perspective over the landscape of super-resolution techniques developed for and applied to remotely-sensed images. The chapters tackle the most important problems that professionals face when dealing with super-resolution in the context of remote sensing. These are: evaluation procedures to assess the super-resolution quality; benchmark datasets (simulated and real-life); super-resolution for specific data modalities (e.g., panchromatic, multispectral, and hyperspectral images); single-image super-resolution, including generative adversarial networks; multi-image fusion (temporal and/or spectral); real-world super-resolution; and task-driven super-resolution. The book presents the results of several recent surveys on super-resolution specifically for the remote sensing community.
Preface
Acknowledgements
Contributors
Introduction to Super-Resolution for Remotely Sensed Hyperspectral Images
Real-World Unsupervised Remote Sensing Image Super-Resolution: Addressing Challenges, Solution, and Future Prospects
Advancements in Deep Learning-Based Super-resolution for Remote Sensing: A Comprehensive Review and Future Directions
Multi-Image Super-Resolution Using Graph Neural Networks
Effectiveness Analysis of Example-Based Machine Learning and Deep Learning Methods for Super-resolution Hyperspectral Images
Synergy of Images: Multi-Image Fusion Empowering Super-Resolution in Remote Sensing
Unsupervised Pansharpening Using ConvNets
A Comprehensive Overview of Satellite Image Fusion: From Classical Model-Based to Cutting-Edge Deep Learning Approaches
Super-Resolution for Spectral Image
Index