Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images
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Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images
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Author : Yakoub Bazi
language : en
Publisher: MDPI
Release Date : 2021-06-15
Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images written by Yakoub Bazi and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-15 with Science categories.
The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images
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Author : Yakoub Bazi
language : en
Publisher:
Release Date : 2021
Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images written by Yakoub Bazi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer--at least partially--such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
Hyperspectral Image Analysis
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Author : Saurabh Prasad
language : en
Publisher: Springer
Release Date : 2020-06-09
Hyperspectral Image Analysis written by Saurabh Prasad and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-09 with Computers categories.
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Hybrid And Advanced Technologies
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Author : S. Prasad Jones Christydass
language : en
Publisher: CRC Press
Release Date : 2025-03-21
Hybrid And Advanced Technologies written by S. Prasad Jones Christydass and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-21 with Technology & Engineering categories.
The proceedings of the International Conference on Hybrid and Advanced Technologies (ICHAT 2024) present a rich repository of cutting-edge research on the various applications of machine learning, deep learning and AI in cybersecurity, healthcare, agriculture and communication systems. It highlights the revolutionary potential of data science in transforming traditional practices, improving efficiency and accuracy across diverse domains and addressing complex real-world challenges. These proceedings contain innovative neural-network models for agriculture that can predict tractor fuel consumption and optimize smart irrigation, besides suggesting greenhouse automation for enhanced agricultural productivity. It also provides a roadmap for IoT-based monitoring systems for asthma patients and machine learning approaches for early detection of diabetes, cancer and aquatic plant ailments. Through an array of practical examples and comparative studies, the book further highlights advancements in machine learning for enhancing palm vein authentication, combating fake news, keeping data safe and improving customer segmentation in e-commerce. The findings would be instrumental in combating critical global issues and foster a deeper understanding of the role of AI in image processing, cybersecurity, medical diagnostics and intelligent systems in the future. This will be a highly interesting guide to researchers, data scientists and practicing professionals in the fields of artificial intelligence, machine learning and cybersecurity. It will also be of interest to healthcare professionals, agricultural scientists and technology enthusiasts in fostering global collaborations, exploring future challenges and opportunities and introducing state-of-the-art technologies to streamline processes.
Deep Learning For The Earth Sciences
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-18
Deep Learning For The Earth Sciences written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-18 with Technology & Engineering categories.
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Sustainable Development Perspectives In Earth Observation
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Author : Mukunda Behera
language : en
Publisher: Elsevier
Release Date : 2025-02-03
Sustainable Development Perspectives In Earth Observation written by Mukunda Behera 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 Science categories.
Earth Observation: Sustainable Development Perspectives offers expert insight to the latest progress made in terrestrial, oceanic, and atmospheric processes, and their inter-linkage in the face of changing climate using earth observation. Reviewing contemporary research, this volume will address various challenges faced in agricultural research and education, crop production forecasting, agroforestry, floriculture, horticulture, crop insurance and marine resources utilization for sustainable development in the warming world. The editors examine the advances made in understanding the changing dynamics of various ecosystems such as land use, water resources, ecosystem productivity and biodiversity. The book addresses ocean-atmosphere interactions, modes of climate variations such as, El Nino and Indian Ocean dipole, extreme events, tropical cyclone, summer monsoon and distribution of organic matter and interlinks among various ocean-atmosphere phenomena. The use of advanced data sets, measurements techniques, modeling and analytics protocols, analyses methods and interpretations are also discussed. For those working towards sustainability using earth observation, this text is a valuable resource for understanding the changing dynamics of the environment in the face of climate change. - Analyzes various challenges faced in agricultural research and education, crop production forecasting, agroforestry, vegetation phenology, forest cover resilience, high altitude wetlands, aerosols, greenhouse gases and coastal management - Explores ocean–atmosphere interactions, modes of climate variations, such as El Nino and Indian Ocean Dipole, extreme events, tropical cyclone, summer monsoon, and distribution of organic matter and their interlinks among various ocean–atmosphere phenomena - Examines various biogeophysical processes using Earth observation for better climate mitigation and adaptation
Machine Learning Paradigms
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Author : George A. Tsihrintzis
language : en
Publisher: Springer
Release Date : 2019-07-06
Machine Learning Paradigms written by George A. Tsihrintzis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-06 with Computers categories.
This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Advanced Technologies For Industrial Applications
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Author : Rohit Thanki
language : en
Publisher: Springer Nature
Release Date : 2023-06-27
Advanced Technologies For Industrial Applications written by Rohit Thanki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-27 with Technology & Engineering categories.
This book provides information on advanced communication technology used in Industry 4.0 and 5.0. The book covers a variety of technologies such as signal processing, system designing, computer vision, and artificial intelligence and explains their benefits, usage, and market values in Industry 4.0 and 5.0. The authors present technological tools for industrial applications and give examples of their usage of system design, modeling, artificial intelligence, internet of things and robotics. This book covers the impact of these technologies in various industrial applications and provides future technological tools that will be helpful in future planning and development. The book is pertinent to researchers, academics, professionals, planners, and student’s interest in Industry 5.0.
Remote Sensing And Digital Image Processing With R
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Author : Marcelo de Carvalho Alves
language : en
Publisher: CRC Press
Release Date : 2023-06-30
Remote Sensing And Digital Image Processing With R written by Marcelo de Carvalho Alves and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Technology & Engineering categories.
This new textbook on remote sensing and digital image processing of natural resources includes numerous, practical problem-solving exercises and applications of sensors and satellite systems using remote sensing data collection resources, and emphasizes the free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications using R language and R packages, by engaging students in learning theory through hands-on, real-life projects. All chapters are structured with learning objectives, computation, questions, solved exercises, resources, and research suggestions. Features Explains the theory of passive and active remote sensing and its applications in water, soil, vegetation, and atmosphere. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. Includes case studies from different environments with free software algorithms and an R toolset for active learning and a learn-by-doing approach. Provides hands-on exercises at the end of each chapter and encourages readers to understand the potential and the limitations of the environments, remote sensing targets, and process. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution data sources for target recognition with image processing techniques. While the focus of the book is on environmental and agriculture engineering, it can be applied widely to a variety of subjects such as physical, natural, and social sciences. Students in upper-level undergraduate or graduate programs, taking courses in remote sensing, geoprocessing, civil and environmental engineering, geosciences, environmental sciences, electrical engineering, biology, and hydrology will also benefit from the learning objectives in the book. Professionals who use remote sensing and digital processing will also find this text enlightening.
Artificial Intelligence In Remote Sensing For Disaster Management
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Author : Neelam Dahiya
language : en
Publisher: John Wiley & Sons
Release Date : 2025-05-28
Artificial Intelligence In Remote Sensing For Disaster Management written by Neelam Dahiya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-28 with Computers categories.
Invest in Artificial Intelligence in Remote Sensing for Disaster Management to gain invaluable insights into cutting-edge AI technologies and their transformative role in effectively monitoring and managing natural disasters. Artificial Intelligence in Remote Sensing for Disaster Management examines the involvement of advanced tools and technologies such as Artificial Intelligence in disaster management with remote sensing. Remote sensing offers cost-effective, quick assessments and responses to natural disasters. In the past few years, many advances have been made in the monitoring and mapping of natural disasters with the integration of AI in remote sensing. This volume focuses on AI-driven observations of various natural disasters including landslides, snow avalanches, flash floods, glacial lake outburst floods, and earthquakes. There is currently a need for sustainable development, near real-time monitoring, forecasting, prediction, and management of natural resources, flash floods, sea-ice melt, cyclones, forestry, and climate changes. This book will provide essential guidance regarding AI-driven algorithms specifically developed for disaster management to meet the requirements of emerging applications.