Full Description
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.
Contents
Chapter 1 Introduction to Super-Resolution for Remotely Sensed Hyperspectral Images.- Chapter 2 Real-World Unsupervised Remote Sensing Image Super-Resolution: Addressing Challenges, Solution and Future Prospects.- Chapter 3 Advancements in Deep Learning-Based Super-Resolution for Remote Sensing: A Comprehensive Review and Future Directions.- Chapter 4 Multi-Image Super-Resolution Using Graph Neural Networks.- Chapter 5 Effectiveness Analysis of Example-Based Machine Learning and Deep Learning Methods for Super-Resolution Hyperspectral Images.- Chapter 6 Synergy of Images: Multi-Image Fusion Empowering Super-Resolution in Remote Sensing.- Chapter 7 Unsupervised Pansharpening using ConvNets.- Chapter 8 A comprehensive overview of satellite image fusion: From classical model-based to cutting-edge deep learning approaches.- Chapter 9 Super-Resolution for Spectral Image.
