Super-Resolution for Remote Sensing
Buch in deiner Nähe kaufen
Beschreibung
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.
- Focuses on reconstruction accuracy compared with ground truth rather than on generating a visually-attractive outcome;
- Explains how to apply super-resolution to a variety of image modalities inherent to remote sensing;
- Gathers the description of training datasets and benchmarks that are based on remotely-sensed images.
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.
Focuses on reconstruction accuracy compared with ground truth rather than on generating a visually-attractive outcome Explains how to apply super-resolution to a variety of image modalities inherent to remote sensing Gathers the description of training datasets and benchmarks that are based on remotely-sensed images
Autor*in
Michal Kawulok
Themen in »Super-Resolution for Remote Sensing«
hyperspectral imaging remote sensing satellite imagery Super resolution deep learning multispectral images