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Deep Learning For Remote Sensing Images With Open Source Software
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Book Synopsis Deep Learning for Remote Sensing Images with Open Source Software by : Rémi Cresson
Download or read book Deep Learning for Remote Sensing Images with Open Source Software written by Rémi Cresson and published by CRC Press. This book was released on 2020-07-16 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.
Book Synopsis Deep Learning for Remote Sensing Images with Open Source Software by : Rémi Cresson
Download or read book Deep Learning for Remote Sensing Images with Open Source Software written by Rémi Cresson and published by . This book was released on 2020 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In today's world, deep learning source codes and a plethora of open access geospatial images are available, but readers are missing the educational tools. This is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches are generic and adapted to suit many applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps readers link together the theory and practical use of existing tools and data to create their own remote sensing data processing"--
Book Synopsis Deep Learning for Remote Sensing Images with Open Source Software by : Rémi Cresson
Download or read book Deep Learning for Remote Sensing Images with Open Source Software written by Rémi Cresson and published by CRC Press. This book was released on 2020-07-15 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.
Book Synopsis Remote Sensing Image Classification in R by : Courage Kamusoko
Download or read book Remote Sensing Image Classification in R written by Courage Kamusoko and published by Springer. This book was released on 2019-07-24 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification. This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification. R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.
Book Synopsis Re-envisioning Remote Sensing Applications by : Ripudaman Singh
Download or read book Re-envisioning Remote Sensing Applications written by Ripudaman Singh and published by CRC Press. This book was released on 2021-03-05 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Re-envisioning Remote Sensing Applications: Perspectives from Developing Countries aims at discussing varied applications of remote sensing, with respect to upcoming technologies with diverse themes. Organized into four sections of overlapping areas of research, the book covers chapters with themes related to agriculture, soil and land degradation studies; hydrology, microclimates and climate change impacts; land use/land cover analysis applications; resource analysis and bibliometric studies, culminating with future research agenda. All the topics are supported via case studies and spatial data analysis. Features: Provides the applications of remote sensing in all fields through varied case studies and spatial data analysis Includes soil and land degradation, microclimates, and climate change impacts Covers remote sensing applications in broad areas of agriculture, hydrology, land use/land cover change and resource analysis Discusses usage of GPS-enabled smartphones and digital gadgets used for mapping and spatial analysis Explores future research agenda for applications of remote sensing in post-COVID scenario This book is of interest to researchers and graduate students in environmental sciences, remote sensing, GIS, agricultural scientists and managers, forestry scientists and managers, and water resources scientists and managers.
Book Synopsis Image Analysis, Classification and Change Detection in Remote Sensing by : Morton John Canty
Download or read book Image Analysis, Classification and Change Detection in Remote Sensing written by Morton John Canty and published by CRC Press. This book was released on 2019-03-11 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty’s fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.
Book Synopsis Re-envisioning Advances in Remote Sensing by : Ripudaman Singh
Download or read book Re-envisioning Advances in Remote Sensing written by Ripudaman Singh and published by CRC Press. This book was released on 2022-03-17 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Re-envisioning Advances in Remote Sensing: Urbanization, Disasters and Planning aims at portraying varied advancements in remote sensing applications, particularly in the fields of urbanization, disaster management and regional planning perspectives. The book is organized into three sections of overlapping areas of research covering chief remote sensing applications. Apart from introducing the advances in remote sensing through Indian remote sensing developments, it depicts the broader themes of: urbanization and its impacts; geospatial technology for disaster management; and, remote sensing applications in models and planning. It also provides outlook to future research agenda for remote sensing. Features: • Depicts advances in remote sensing in major fields through applications of geospatial technologies. • Covers remote sensing applications in varied aspects of urbanization, urban problems and disasters. • Includes advancements in remote sensing in model building and planning perspectives. • Analyses the usage of smartphones and other digital devices in mapping urban problems and monitoring disaster risks. • Explores future agenda for remote sensing advances and its ever-widening horizon. This book would be of interest to all the researchers and graduate students pursuing studies in the fields of remote sensing, GIS, geospatial technologies, urbanizations, disaster management, regional planning, environmental sciences, natural resource management and related fields.
Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Earth Observation Data Cubes by : Gregory Giuliani
Download or read book Earth Observation Data Cubes written by Gregory Giuliani and published by . This book was released on 2020 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10.
Book Synopsis Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images by : Yakoub Bazi
Download or read book Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images written by Yakoub Bazi and published by MDPI. This book was released on 2021-06-15 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence and IoT by : Kalaiselvi Geetha Manoharan
Download or read book Artificial Intelligence and IoT written by Kalaiselvi Geetha Manoharan and published by Springer Nature. This book was released on 2021-02-12 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book projects a futuristic scenario that is more existent than they have been at any time earlier. To be conscious of the bursting prospective of IoT, it has to be amalgamated with AI technologies. Predictive and advanced analysis can be made based on the data collected, discovered and analyzed. To achieve all these compatibility, complexity, legal and ethical issues arise due to automation of connected components and gadgets of widespread companies across the globe. While these are a few examples of issues, the authors’ intention in editing this book is to offer concepts of integrating AI with IoT in a precise and clear manner to the research community. In editing this book, the authors’ attempt is to provide novel advances and applications to address the challenge of continually discovering patterns for IoT by covering various aspects of implementing AI techniques to make IoT solutions smarter. The only way to remain pace with this data generated by the IoT and acquire the concealed acquaintance it encloses is to employ AI as the eventual catalyst for IoT. IoT together with AI is more than an inclination or existence; it will develop into a paradigm. It helps those researchers who have an interest in this field to keep insight into different concepts and their importance for applications in real life. This has been done to make the edited book more flexible and to stimulate further interest in topics. All these motivated the authors toward integrating AI in achieving smarter IoT. The authors believe that their effort can make this collection interesting and highly attract the student pursuing pre-research, research and even master in multidisciplinary domain.
Book Synopsis Image Analysis, Classification and Change Detection in Remote Sensing by : Morton John Canty
Download or read book Image Analysis, Classification and Change Detection in Remote Sensing written by Morton John Canty and published by CRC Press. This book was released on 2019 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty's fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.
Book Synopsis Image Fusion in Remote Sensing by : Arian Azarang
Download or read book Image Fusion in Remote Sensing written by Arian Azarang and published by Springer Nature. This book was released on 2022-05-31 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and spectral (multispectral) images that are captured by different sensors on satellites. This book addresses image fusion approaches for remote sensing applications. Both conventional and deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches include component substitution, multi-resolution, and model-based algorithms. Then, the recently developed deep learning approaches involving single-objective and multi-objective loss functions are discussed. Experimental results are provided comparing conventional and deep learning approaches in terms of both low-resolution and full-resolution objective metrics that are commonly used in remote sensing. The book is concluded by stating anticipated future trends in pansharpening or image fusion in remote sensing.
Book Synopsis Handbook Of Pattern Recognition And Computer Vision (6th Edition) by : Chen Chi Hau
Download or read book Handbook Of Pattern Recognition And Computer Vision (6th Edition) written by Chen Chi Hau and published by World Scientific. This book was released on 2020-04-04 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning to Understand Remote Sensing Images by : Qi Wang
Download or read book Learning to Understand Remote Sensing Images written by Qi Wang and published by MDPI. This book was released on 2019-09-30 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-16 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Proceedings of the International Conference on Big Data, IoT, and Machine Learning by : Mohammad Shamsul Arefin
Download or read book Proceedings of the International Conference on Big Data, IoT, and Machine Learning written by Mohammad Shamsul Arefin and published by Springer Nature. This book was released on 2021-12-03 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.
