| 书目名称 | Super-Resolution for Remote Sensing |
| 编辑 | Michal Kawulok,Jolanta Kawulok,M. Emre Celebi |
| 视频video | http://file.papertrans.cn/886/885287/885287.mp4 |
| 概述 | 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 t |
| 丛书名称 | Unsupervised and Semi-Supervised Learning |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2024 |
| 关键词 | hyperspectral imaging; remote sensing; satellite imagery; Super resolution; deep learning; multispectral |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-68106-6 |
| isbn_softcover | 978-3-031-68108-0 |
| isbn_ebook | 978-3-031-68106-6Series ISSN 2522-848X Series E-ISSN 2522-8498 |
| issn_series | 2522-848X |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手机版|小黑屋|
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