03991nam 22006255 450 991089653600332120250807153009.09783031681066303168106110.1007/978-3-031-68106-6(CKB)36357526800041(MiAaPQ)EBC31727213(Au-PeEL)EBL31727213(DE-He213)978-3-031-68106-6(EXLCZ)993635752680004120241015d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierSuper-Resolution for Remote Sensing /edited by Michal Kawulok, Jolanta Kawulok, Bogdan Smolka, M. Emre Celebi1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (392 pages)Unsupervised and Semi-Supervised Learning,2522-84989783031681059 3031681053 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.Unsupervised and Semi-Supervised Learning,2522-8498Computational intelligenceTelecommunicationComputer visionComputational IntelligenceCommunications Engineering, NetworksComputer VisionComputational intelligence.Telecommunication.Computer vision.Computational Intelligence.Communications Engineering, Networks.Computer Vision.006.3Kawulok Michal1766939Kawulok Jolanta1766940Smolka Bogdan1766941Celebi M. Emre1448701MiAaPQMiAaPQMiAaPQBOOK9910896536003321Super-Resolution for Remote Sensing4211726UNINA