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: John A. Richards
Publisher: Springer Science & Business Media
ISBN: 3662024624
Category : Technology & Engineering
Languages : en
Pages : 297

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Book Description
With the widespread availability of satellite and aircraft remote sensing image data in digital form, and the ready access most remote sensing practitioners have to computing systems for image interpretation, there is a need to draw together the range of digital image processing procedures and methodologies commonly used in this field into a single treatment. It is the intention of this book to provide such a function, at a level meaningful to the non-specialist digital image analyst, but in sufficient detail that algorithm limitations, alternative procedures and current trends can be appreciated. Often the applications specialist in remote sensing wishing to make use of digital processing procedures has had to depend upon either the mathematically detailed treatments of image processing found in the electrical engineering and computer science literature, or the sometimes necessarily superficial treatments given in general texts on remote sensing. This book seeks to redress that situation. Both image enhancement and classification techniques are covered making the material relevant in those applications in which photointerpretation is used for information extraction and in those wherein information is obtained by classification.

Author: John A. Richards
Publisher: Springer Science & Business Media
ISBN: 3642300626
Category : Technology & Engineering
Languages : en
Pages : 494

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Book Description
Remote Sensing Digital Image Analysis provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived remotely retrieved data. Since the first edition of the book there have been significant developments in the algorithms used for the processing and analysis of remote sensing imagery; nevertheless many of the fundamentals have substantially remained the same. This new edition presents material that has retained value since those early days, along with new techniques that can be incorporated into an operational framework for the analysis of remote sensing data. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image processing in remote sensing. The presentation level is for the mathematical non-specialist. Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a level commensurate with their background. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter.

Author: Marcelo de Carvalho Alves
Publisher: CRC Press
ISBN: 1000895440
Category : Technology & Engineering
Languages : en
Pages : 224

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Book Description
This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest. Features Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. 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 information. Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources.

Author: Marcelo de Carvalho Alves
Publisher: CRC Press
ISBN: 100089536X
Category : Technology & Engineering
Languages : en
Pages : 537

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Book Description
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.

Author: John R. Jensen
Publisher: Prentice Hall
ISBN: 9780134058160
Category : Science
Languages : en
Pages : 544

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Book Description
For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.

Author: John R. Jensen
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 362

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Book Description
For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. This text focuses exclusively on the art and science of digital image processing of satellite and aircraft-derived remotely-sensed data for resource management. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Prentice Hall Series Geographic Information Science.

Author: John R. Jensen
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 584

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Book Description
For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. This revision of Introductory Digital Image Processing: A Remote Sensing Perspective continues to focus on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Prentice Hall Series Geographic Information Science.

Author: R.M. Hord
Publisher: Elsevier
ISBN: 0323162355
Category : Science
Languages : en
Pages : 270

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Book Description
Digital Image Processing of Remotely Sensed Data presents a practical approach to digital image processing of remotely sensed data, with emphasis on application examples and algorithms. It explains where to get the data and what is available and what preprocessing is needed to prepare the imagery for processing. Research topics are described to indicate the limitations of computer methods. This book is comprised of seven chapters and begins with a summary of basic concepts used in remote sensing and digital imagery, followed by a discussion on sources of remotely sensed data. Two essential hardware ingredients in a digital image processing system, a computer and a display device, are then considered, along with the algorithms used in digital image processing. Examples of how digital image processing algorithms have been applied to real imagery for specific objectives are given, including the Kentucky water impoundment experiment and the land-use mapping initiative in Washington, D.C. The next section is devoted to research topics such as digital image shape detection; edge detection and regionalized terrain classification from satellite photography; and digital image enhancement for maximum interpretability using linear programming. This monograph will be of value to professional regional planners, natural resource managers, and others in fields ranging from hydrology and forestry to agronomy and geology.

Author: Marcelo de Carvalho Alves
Publisher: CRC Press
ISBN: 1000895394
Category : Technology & Engineering
Languages : en
Pages : 189

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Book Description
This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest. Features Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. 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 information. Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources.