Super Resolution For Remote Sensing
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Super Resolution For Remote Sensing
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Author : Michal Kawulok
language : en
Publisher: Springer Nature
Release Date : 2024-10-14
Super Resolution For Remote Sensing written by Michal Kawulok and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-14 with Computers categories.
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.
Super Resolution For Remote Sensing Applications Using Deep Learning Techniques
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Author : G. Rohith
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2022-12-14
Super Resolution For Remote Sensing Applications Using Deep Learning Techniques written by G. Rohith and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-14 with Computers categories.
Satellite image processing is crucial in detecting vegetation, clouds, and other atmospheric applications. Due to sensor limitations and pre-processing, remotely sensed satellite images may have interpretability concerns as to specific portions of the image, making it hard to recognise patterns or objects and posing the risk of losing minute details in the image. Existing imaging processors and optical components are expensive to counterfeit, have interpretability issues, and are not necessarily viable in real applications. This book exploits the usage of deep learning (DL) components in feature extraction to boost the minute details of images and their classification implications to tackle such problems. It shows the importance of super-resolution in improving the spatial details of images and aiding digital aerial photography in pan-sharpening, detecting signatures correctly, and making precise decisions with decision-making tools.
High Spatial Resolution Remote Sensing
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Author : Yuhong He
language : en
Publisher: CRC Press
Release Date : 2018-06-27
High Spatial Resolution Remote Sensing written by Yuhong He and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-27 with Technology & Engineering categories.
High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remote sensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation. To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution Remote Sensing: Data, Analysis, and Applications addresses some key questions: What are the challenges of using new sensors and new platforms? What are the cutting-edge methods for fine-level information extraction from high spatial resolution images? How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes? The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remote sensing data and recent developments with a focus on UAVs. This work addresses the issues related to high spatial image processing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remote sensing can support the extraction of detailed information needed in different systems. Using various high spatial resolution data, the third part of this book covers a range of unique applications, from grasslands to wetlands, karst areas, and cherry orchard trees.
Very High Resolution Vhr Satellite Imagery
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Author : Francisco Eugenio
language : en
Publisher: MDPI
Release Date : 2019-11-06
Very High Resolution Vhr Satellite Imagery written by Francisco Eugenio and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with Science categories.
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.
Subpixel Mapping For Remote Sensing Images
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Author : Peng Wang
language : en
Publisher: CRC Press
Release Date : 2022-12-15
Subpixel Mapping For Remote Sensing Images written by Peng Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-15 with Technology & Engineering categories.
Subpixel mapping is a technology that generates a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at the subpixel scale. This book provides readers with a complete overview of subpixel image processing methods, basic principles, and different subpixel mapping techniques based on single or multi-shift remote sensing images. Step-by-step procedures, experimental contents, and result analyses are explained clearly at the end of each chapter. Real-life applications are a great resource for understanding how and where to use subpixel mapping when dealing with different remote sensing imaging data. This book will be of interest to undergraduate and graduate students, majoring in remote sensing, surveying, mapping, and signal and information processing in universities and colleges, and it can also be used by professionals and researchers at different levels in related fields.
Development Of Super Resolution Techniques For Finer Scale Remote Sensing Image Mapping
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Author : Feng Li
language : en
Publisher:
Release Date : 2009
Development Of Super Resolution Techniques For Finer Scale Remote Sensing Image Mapping written by Feng Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Algorithms categories.
In this thesis, methods for achieving finer scale multi-spectral classification through the use of super resolution (SR) techniques are investigated. A new super resolution algorithm Maximum a Posteriori based on the universal hidden Markov tree model (MAP-uHMT) is developed which can be applied successfully to super-resolve each multi-spectral channel before classification by standard methods. It is believed that this is the first time that a true super resolution algorithm has been applied to multi-spectral classification, and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This improved elastic image registration method can deal with a global affine warping and local shift translations based on coarse to fine pyramid levels. The experimental results show that it can provide good registration accuracy in less computational time than comparable methods. Maximum a posteriori (MAP) is adopted to deal with the ill-conditioned problem of super resolution, wherein a prior is constructed based on the universal hidden Markov tree (uHMT) model in the wavelet domain. In order to test this prior for MAP estimation, it is first tested in the simpler and typically ill-conditioned problem of image denoising. Experimental results illustrate that this new image denoising method achieves good performance for the test images. The new prior is then extended to SR. By combining with the new elastic image registration algorithm, MAP-uHMT can super resolve both some natural video frames and remote sensing images. Test results with both synthetic data and real data show that this method achieves super resolution both visually and quantitatively. In order to show that MAPuHMT is also applicable more widely, it is tested on a sequence of long-range surveillance images captured under conditions of atmospheric turbulence distortion. The results suggest that super resolution may have been achieved in this application also.
Deep Learning For Multi Sensor Earth Observation
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Author : Sudipan Saha
language : en
Publisher: Elsevier
Release Date : 2025-02-03
Deep Learning For Multi Sensor Earth Observation written by Sudipan Saha and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Technology & Engineering categories.
Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning. - Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality - Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences - Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice
Super Resolution Imaging
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Author : Peyman Milanfar
language : en
Publisher: CRC Press
Release Date : 2017-12-19
Super Resolution Imaging written by Peyman Milanfar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Computers categories.
With the exponential increase in computing power and broad proliferation of digital cameras, super-resolution imaging is poised to become the next "killer app." The growing interest in this technology has manifested itself in an explosion of literature on the subject. Super-Resolution Imaging consolidates key recent research contributions from eminent scholars and practitioners in this area and serves as a starting point for exploration into the state of the art in the field. It describes the latest in both theoretical and practical aspects of direct relevance to academia and industry, providing a base of understanding for future progress. Features downloadable tools to supplement material found in the book Recent advances in camera sensor technology have led to an increasingly larger number of pixels being crammed into ever-smaller spaces. This has resulted in an overall decline in the visual quality of recorded content, necessitating improvement of images through the use of post-processing. Providing a snapshot of the cutting edge in super-resolution imaging, this book focuses on methods and techniques to improve images and video beyond the capabilities of the sensors that acquired them. It covers: History and future directions of super-resolution imaging Locally adaptive processing methods versus globally optimal methods Modern techniques for motion estimation How to integrate robustness Bayesian statistical approaches Learning-based methods Applications in remote sensing and medicine Practical implementations and commercial products based on super-resolution The book concludes by concentrating on multidisciplinary applications of super-resolution for a variety of fields. It covers a wide range of super-resolution imaging implementation techniques, including variational, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. This versatile book can be used as the basis for short courses for engineers and scientists, or as part of graduate-level courses in image processing.
Proceedings Of The 7th International Symposium Of Space Optical Instruments And Applications
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Author : H. Paul Urbach
language : en
Publisher: Springer Nature
Release Date : 2023-08-30
Proceedings Of The 7th International Symposium Of Space Optical Instruments And Applications written by H. Paul Urbach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-30 with Science categories.
This proceedings book contains selected and expanded contributions presented at the 7th International Symposium of Space Optical Instruments and Applications, held in Beijing, China, on Oct 21–23, 2022. The meeting was organized by the Sino-Holland Space Optical Instruments Joint Laboratory and supported by Beijing Institute and Space Mechanics and Electricity. In the recent years, space optical payloads are advancing toward high spatial resolution, high temporal resolution, high radiometric resolution, and high spectral resolution and becoming more and more intelligent. Commercial remote sensing industry has made steady progress in terms of the scope of satellite systems and applications. Meanwhile, space optical remote sensing data has been extensively applied to monitoring of resources, meteorology, ocean, environment, disaster reduction, and many other fields. The symposium focused on key innovations of space-based optical instruments and applications and the newest developments in theory, technology, and applications in optics, in both China and Europe. It thus provided a platform for exchanges on the latest research and current and planned optical missions. The major topics covered in these conference proceedings are: 1) Advanced space optical remote sensing application technology. 2) Deep space exploration and astronomical observation technology. 3) Advanced space optical remote sensing instrument technology. 4) Commercial optical observation technology and services.
Deep Neural Network For Super Resolution Of Multitemporal Remote Sensing Images
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Author : Pol Masó Ayats
language : en
Publisher:
Release Date : 2020
Deep Neural Network For Super Resolution Of Multitemporal Remote Sensing Images written by Pol Masó Ayats and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Since few years ago, artificial intelligence (AI) has become a spotlight technology in which a lot of people are interested in. Most of them want to do research and use it to solve a huge variety of modern and difficult computing problems which could be associated with a wide variety of interesting fields. As soon as AI has improved, convolutional neural networks (CNN) have taken an excellent role in the world of image processing, in particular, for Remote Sensing applications. Nevertheless, artificial intelligence for multi-image superresolution from multi-temporal imagery has received little attention so far. In this work, it is proposed a CNN, which exploits both spatial and temporal correlations in the low-resolution images by using two different convolutional layers (2D and 3D convolutions) to combine multiple satellite images from the same scene which are taken in different temporal moments. The experiments have been carried out using a dataset generated by Sentinel-2 (European Space Agency satellite) images captured over 2 different places over the world, New York and El Cairo. This model aims to obtain super-resolution images from five low-resolution images, or less, being aware of the number of input images that the CNN has.