Learning To Understand Remote Sensing Images

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Learning To Understand Remote Sensing Images

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Author : Qi Wang
language : en
Publisher: MDPI
Release Date : 2019-09-30

Learning To Understand Remote Sensing Images written by Qi Wang and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-30 with Computers categories.

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Learning To Understand Remote Sensing Images Volume 1

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Author : Qi Wang
language : en
Publisher:
Release Date : 2019

Learning To Understand Remote Sensing Images Volume 1 written by Qi Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Electronic books categories.

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification

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Author : Anil Kumar
language : en
Publisher: CRC Press
Release Date : 2020-07-19

Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification written by Anil Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-19 with Computers categories.

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

Second International Conference On Image Processing And Capsule Networks

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Author : Joy Iong-Zong Chen
language : en
Publisher: Springer Nature
Release Date : 2021-09-09

Second International Conference On Image Processing And Capsule Networks written by Joy Iong-Zong Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-09 with Technology & Engineering categories.

This book includes the papers presented in 2nd International Conference on Image Processing and Capsule Networks [ICIPCN 2021]. In this digital era, image processing plays a significant role in wide range of real-time applications like sensing, automation, health care, industries etc. Today, with many technological advances, many state-of-the-art techniques are integrated with image processing domain to enhance its adaptiveness, reliability, accuracy and efficiency. With the advent of intelligent technologies like machine learning especially deep learning, the imaging system can make decisions more and more accurately. Moreover, the application of deep learning will also help to identify the hidden information in volumetric images. Nevertheless, capsule network, a type of deep neural network, is revolutionizing the image processing domain; it is still in a research and development phase. In this perspective, this book includes the state-of-the-art research works that integrate intelligent techniques with image processing models, and also, it reports the recent advancements in image processing techniques. Also, this book includes the novel tools and techniques for deploying real-time image processing applications. The chapters will briefly discuss about the intelligent image processing technologies, which leverage an authoritative and detailed representation by delivering an enhanced image and video recognition and adaptive processing mechanisms, which may clearly define the image and the family of image processing techniques and applications that are closely related to the humanistic way of thinking.

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.

A Selection Of Image Understanding Techniques

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Author : Yu-Jin Zhang
language : en
Publisher: CRC Press
Release Date : 2023-01-31

A Selection Of Image Understanding Techniques written by Yu-Jin Zhang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-31 with Computers categories.

This book offers a comprehensive introduction to seven commonly used image understanding techniques in modern information technology. Readers of various levels can find suitable techniques to solve their practical problems and discover the latest development in these specific domains. The techniques covered include camera model and calibration, stereo vision, generalized matching, scene analysis and semantic interpretation, multi-sensor image information fusion, content-based visual information retrieval, and understanding spatial-temporal behavior. The book provides aspects from the essential concepts overview and basic principles to detailed introduction, explanation of the current methods and their practical techniques. It also presents discussions on the research trends and latest results in conjunction with new development of technical methods. This is an excellent read for those who do not have a subject background in image technology but need to use these techniques to complete specific tasks. These essential information will also be useful for their further study in the relevant fields.

Meta Heuristic Optimization Techniques

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Author : Anuj Kumar
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-01-19

Meta Heuristic Optimization Techniques written by Anuj Kumar and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-19 with Computers categories.

This book offer a thorough overview of the most popular and researched meta-heuristic optimization techniques and nature inspired algorithms. Their wide applicability makes them a hot research topic and an efficient tool for the solution of complex optimization problems in various field of sciences, engineering and in numerous industries.

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.

Urban Informatics

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Author : Wenzhong Shi
language : en
Publisher: Springer Nature
Release Date : 2021-04-06

Urban Informatics written by Wenzhong Shi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-06 with Social Science categories.

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.

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.