Titlebook: Super-Resolution for Remote Sensing; Michal Kawulok,Jolanta Kawulok,M. Emre Celebi Book 2024 The Editor(s) (if applicable) and The Author(

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Unsupervised Pansharpening Using ConvNets,e and better data. No single sensor provides all the information of interest, which motivates the growing appeal of data fusion. Due to the limitations of the sensors, the acquired images cannot have simultaneously high spatial and spectral resolution. To overcome this problem, two coupled sensors c

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Super-Resolution for Spectral Image, and recognition. However, in the process of obtaining image data, due to the influence of many uncertain factors, such as sensor system and external conditions, the quality of spectral image is reduced, which is not conducive to the effective discrimination of objects. Super-resolution is one of th

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Introduction to Super-Resolution for Remotely Sensed Hyperspectral Images,t popular approaches, loss functions, hyperspectral datasets, and evaluation methods. Subsequently, the focus shifts to techniques specifically devised for hyperspectral imagery. These encompass single-image super-resolution, hyperspectral and multispectral image fusion, pansharpening, and multi-image super-resolution.

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Matteo Ciotola,Giuseppe Scarpases that may be important in understanding diversification of insect species in heterogeneous environments in space and time. This book is a valuable resource e978-4-431-54260-5978-4-431-54261-2Series ISSN 2192-2179 Series E-ISSN 2192-2187

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Real-World Unsupervised Remote Sensing Image Super-Resolution: Addressing Challenges, Solution, andfficulty of designing effective super-resolution models. Additionally, adapting algorithms to diverse landscapes and imaging conditions poses a substantial challenge. By addressing challenges through innovative solutions and envisioning future prospects, researchers can unlock the full potential of

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Advancements in Deep Learning-Based Super-resolution for Remote Sensing: A Comprehensive Review andallenges such as the limitations of current remote sensing datasets and the discrepancies between low-resolution (LR) and high-resolution (HR) mappings were also discussed. By providing a thorough review of DL-based SISR and MISR methods, this review offers insights into recent advancements and futu

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Effectiveness Analysis of Example-Based Machine Learning and Deep Learning Methods for Super-resolus also known as pan-sharpening methods (IHS and PCA); (2) example-based machine learning methods, especially, dictionary learning-based sparse representation methods (K-SVD, ODL, and Bayesian); and (3) deep learning-based methods (CNN). This research contributes valuable information on the comparati