04425nam a22005775i 4500 978-3-031-68106-6 DE-He213 20251030113143.0 cr nn 008mamaa 241015s2024 sz | s |||| 0|eng d 9783031681066 978-3-031-68106-6 10.1007/978-3-031-68106-6 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Super-Resolution for Remote Sensing [electronic resource] / edited by Michal Kawulok, Jolanta Kawulok, Bogdan Smolka, M. Emre Celebi. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIV, 384 p. 144 illus., 125 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Unsupervised and Semi-Supervised Learning, 2522-8498 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. 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. Computational intelligence. 64052 Telecommunication. Computer vision. Computational Intelligence. 64052 Communications Engineering, Networks. Computer Vision. Kawulok, Michal. editor. edt http://id.loc.gov/vocabulary/relators/edt 86037 Kawulok, Jolanta. editor. edt http://id.loc.gov/vocabulary/relators/edt 86038 Smolka, Bogdan. editor. edt http://id.loc.gov/vocabulary/relators/edt 86039 Celebi, M. Emre. editor. (orcid)0000-0002-2721-6317 https://orcid.org/0000-0002-2721-6317 edt http://id.loc.gov/vocabulary/relators/edt 86040 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031681059 Printed edition: 9783031681073 Printed edition: 9783031681080 Unsupervised and Semi-Supervised Learning, 2522-8498 68420 https://doi.org/10.1007/978-3-031-68106-6 ZDB-2-INR ZDB-2-SXIT 64208 64208