LEADER 03992nam 22006255 450 001 9910896536003321 005 20250807153009.0 010 $a9783031681066 010 $a3031681061 024 7 $a10.1007/978-3-031-68106-6 035 $a(CKB)36357526800041 035 $a(MiAaPQ)EBC31727213 035 $a(Au-PeEL)EBL31727213 035 $a(DE-He213)978-3-031-68106-6 035 $a(EXLCZ)9936357526800041 100 $a20241015d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSuper-Resolution for Remote Sensing /$fedited by Michal Kawulok, Jolanta Kawulok, Bogdan Smolka, M. Emre Celebi 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (392 pages) 225 1 $aUnsupervised and Semi-Supervised Learning,$x2522-8498 311 08$a9783031681059 311 08$a3031681053 327 $aChapter 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. 330 $aThis book provides a comprehensive perspective over the landscape of super-resolution techniques developed for and applied to remotely-sensed images. The chapters tackle the most important problems that professionals face when dealing with super-resolution in the context of remote sensing. These are: evaluation procedures to assess the super-resolution quality; benchmark datasets (simulated and real-life); super-resolution for specific data modalities (e.g., panchromatic, multispectral, and hyperspectral images); single-image super-resolution, including generative adversarial networks; multi-image fusion (temporal and/or spectral); real-world super-resolution; and task-driven super-resolution. The book presents the results of several recent surveys on super-resolution specifically for the remote sensing community. Focuses on reconstruction accuracy compared with ground truth rather than on generating a visually-attractive outcome; Explains how to apply super-resolution to a variety of image modalities inherent to remote sensing; Gathers the description of training datasets and benchmarks that are based on remotely-sensed images. 410 0$aUnsupervised and Semi-Supervised Learning,$x2522-8498 606 $aComputational intelligence 606 $aTelecommunication 606 $aComputer vision 606 $aComputational Intelligence 606 $aCommunications Engineering, Networks 606 $aComputer Vision 615 0$aComputational intelligence. 615 0$aTelecommunication. 615 0$aComputer vision. 615 14$aComputational Intelligence. 615 24$aCommunications Engineering, Networks. 615 24$aComputer Vision. 676 $a006.3 700 $aKawulok$b Michal$01766939 701 $aKawulok$b Jolanta$01766940 701 $aSmo?ka$b Bogdan$01885586 701 $aCelebi$b M. Emre$01448701 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910896536003321 996 $aSuper-Resolution for Remote Sensing$94520873 997 $aUNINA