Deep Learning for Remote Sensing Images with Open Source Software PDF Download

Author: Rémi Cresson
Publisher: CRC Press
ISBN: 100009359X
Category : Technology & Engineering
Languages : en
Pages : 152

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Book Description
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.

Author: Rémi Cresson
Publisher:
ISBN: 9780367858483
Category : Image processing
Languages : en
Pages : 158

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Book Description
"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"--

Author: Rémi Cresson
Publisher: CRC Press
ISBN: 1000093611
Category : Technology & Engineering
Languages : en
Pages : 158

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Book Description
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.

Author: Courage Kamusoko
Publisher: Springer
ISBN: 9811380120
Category : Technology & Engineering
Languages : en
Pages : 189

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Book Description
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.

Author: C.H. Chen
Publisher: CRC Press
ISBN: 1040031250
Category : Technology & Engineering
Languages : en
Pages : 433

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Book Description
Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly significant with the use of deep learning based techniques to solve remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing. Features Includes all new content and does not replace the previous edition Covers machine learning approaches in both signal and image processing for remote sensing Studies deep learning methods for remote sensing information extraction that is found in other books Explains SAR, microwave, seismic, GPR, and hyperspectral sensors and all sensors considered Discusses improved pattern classification approaches and compressed sensing approaches Provides ample examples of each aspect of both signal and image processing This book is intended for university academics, researchers, postgraduate students, industry, and government professionals who use remote sensing and its applications.

Author: Ripudaman Singh
Publisher: CRC Press
ISBN: 1000347184
Category : Technology & Engineering
Languages : en
Pages : 254

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Book Description
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.

Author: Morton John Canty
Publisher: CRC Press
ISBN: 0429875355
Category : Technology & Engineering
Languages : en
Pages : 508

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Book Description
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.

Author: Ripudaman Singh
Publisher: CRC Press
ISBN: 1000531473
Category : Technology & Engineering
Languages : en
Pages : 333

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Book Description
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.

Author: Gregory Giuliani
Publisher:
ISBN: 9783039280933
Category : Geography (General)
Languages : en
Pages : 302

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Book Description
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.

Author: Yakoub Bazi
Publisher: MDPI
ISBN: 3036509860
Category : Science
Languages : en
Pages : 438

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Book Description
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.