Classification Of Remotely Sensed Images
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Classification Of Remotely Sensed Images
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Author : I.L Thomas
language : en
Publisher: CRC Press
Release Date : 1987-01-01
Classification Of Remotely Sensed Images written by I.L Thomas and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-01-01 with Science categories.
Remotely sensed images are an indispensable tool to researchers in disciplines such as cartography, forestry and geology in which mapping by satellite or aircraft is often a far more acurate, efficient and cost-effective method than conventional survey. Most users of remote sensing data are now familiar with using photographic images prepared to standard prescriptions by others to relate colours or grey tones to what actually exists on the ground. This book is aimed at the discipline-oriented user who wishes to move away from these traditional photographic interpretation methods towards interactive analysis systems. Such systems permit the classification of the digital data in ways that enable locations and quantitative information on specific themes to be extracted and portrayed. The empasis is upon the integration of these new techniques into existing discipline skills: the user is not expected to become a 'computer operator'. The book covers the fundamental theory of image classification, and illustrates it with practical examples in the form of a structured outline of a total research project. It is therefore an essential reference manual to 'sit at the elbow' of the user working with an image processing system for remotely sensed data.
Land Cover Classification Of Remotely Sensed Images
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Author : S. Jenicka
language : en
Publisher: Springer Nature
Release Date : 2021-03-10
Land Cover Classification Of Remotely Sensed Images written by S. Jenicka and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-10 with Technology & Engineering categories.
The book introduces two domains namely Remote Sensing and Digital Image Processing. It discusses remote sensing, texture, classifiers, and procedures for performing the texture-based segmentation and land cover classification. The first chapter discusses the important terminologies in remote sensing, basics of land cover classification, types of remotely sensed images and their characteristics. The second chapter introduces the texture and a detailed literature survey citing papers related to texture analysis and image processing. The third chapter describes basic texture models for gray level images and multivariate texture models for color or remotely sensed images with relevant Matlab source codes. The fourth chapter focuses on texture-based classification and texture-based segmentation. The Matlab source codes for performing supervised texture based segmentation using basic texture models and minimum distance classifier are listed. The fifth chapter describes supervised and unsupervised classifiers. The experimental results obtained using a basic texture model (Uniform Local Binary Pattern) with the classifiers described earlier are discussed through the relevant Matlab source codes. The sixth chapter describes land cover classification procedure using multivariate (statistical and spectral) texture models and minimum distance classifier with Matlab source codes. A few performance metrics are also explained. The seventh chapter explains how texture based segmentation and land cover classification are performed using the hidden Markov model with relevant Matlab source codes. The eighth chapter gives an overview of spatial data analysis and other existing land cover classification methods. The ninth chapter addresses the research issues and challenges associated with land cover classification using textural approaches. This book is useful for undergraduates in Computer Science and Civil Engineering and postgraduates who plan to do research or project work in digital image processing. The book can serve as a guide to those who narrow down their research to processing remotely sensed images. It addresses a wide range of texture models and classifiers. The book not only guides but aids the reader in implementing the concepts through the Matlab source codes listed. In short, the book will be a valuable resource for growing academicians to gain expertise in their area of specialization and students who aim at gaining in-depth knowledge through practical implementations. The exercises given under texture based segmentation (excluding land cover classification exercises) can serve as lab exercises for the undergraduate students who learn texture based image processing.
Remote Sensing Image Classification In R
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Author : Courage Kamusoko
language : en
Publisher: Springer
Release Date : 2019-07-24
Remote Sensing Image Classification In R written by Courage Kamusoko and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with Technology & Engineering categories.
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.
Computer Processing Of Remotely Sensed Images
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Author : Paul M. Mather
language : en
Publisher: John Wiley & Sons
Release Date : 2005-12-13
Computer Processing Of Remotely Sensed Images written by Paul M. Mather 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 2005-12-13 with Science categories.
Remotely-sensed images of the Earth's surface provide a valuable source of information about the geographical distribution and properties of natural and cultural features. This fully revised and updated edition of a highly regarded textbook deals with the mechanics of processing remotely-senses images. Presented in an accessible manner, the book covers a wide range of image processing and pattern recognition techniques. Features include: New topics on LiDAR data processing, SAR interferometry, the analysis of imaging spectrometer image sets and the use of the wavelet transform. An accompanying CD-ROM with: updated MIPS software, including modules for standard procedures such as image display, filtering, image transforms, graph plotting, import of data from a range of sensors. A set of exercises, including data sets, illustrating the application of discussed methods using the MIPS software. An extensive list of WWW resources including colour illustrations for easy download. For further information, including exercises and latest software information visit the Author's Website at: http://homepage.ntlworld.com/paul.mather/ComputerProcessing3/
Digital Processing Of Remotely Sensed Images
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Author : Johannes G. Moik
language : en
Publisher:
Release Date : 1980
Digital Processing Of Remotely Sensed Images written by Johannes G. Moik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Government publications categories.
The foundations of image processing were reviewed. Imaging techniques are discussed and include: image resolution, image enhancement, image registration, image overlaying and mosaicking, image analysis and classification, and image data compression.
Techniques For Image Processing And Classifications In Remote Sensing
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Author : Robert A. Schowengerdt
language : en
Publisher: Academic Press
Release Date : 1983
Techniques For Image Processing And Classifications In Remote Sensing written by Robert A. Schowengerdt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Computers categories.
Digital image processing. Digital image classification. Remote sensing and image processing bibliography. Digital image data formats. The table look-up algorithm and interactive image processing. Examination quetions.
Classification Methods For Remotely Sensed Data
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Author : Paul Mather
language : en
Publisher: CRC Press
Release Date : 2001-12-06
Classification Methods For Remotely Sensed Data written by Paul Mather and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-12-06 with Technology & Engineering categories.
Remote sensing is an integral part of geography, GIS and cartography, used by academics in the field and professionals in all sorts of occupations. The 1990s saw the development of a range of new methods of classifying remote sensing images and data, both optical imaging and microwave imaging. This comprehensive survey of the various techniques pul
Techniques For Image Processing And Classifications In Remote Sensing
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Author : Robert A. Schowengerdt
language : en
Publisher: Academic Press
Release Date : 2012-12-02
Techniques For Image Processing And Classifications In Remote Sensing written by Robert A. Schowengerdt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-02 with Technology & Engineering categories.
Techniques for Image Processing and Classifications in Remote Sensing provides an introduction to the fundamentals of computer image processing and classification (commonly called ""pattern recognition"" in other applications). The book begins with a discussion of digital scanners and imagery, and two key mathematical concepts for image processing and classification—spatial filtering and statistical pattern recognition. This is followed by separate chapters on image processing and classification techniques that are widely used in the remote sensing community. The emphasis throughout is on techniques that assist in the analysis of images, not particular applications of these techniques. The book also has four appendixes, featuring a bibliography; an introduction to computer binary data representation and image data formats; a discussion of interactive image processing; and a selection of exam questions from the Image Processing Laboratory course at the University of Arizona. This book is intended for use as either a primary source in an introductory image processing course or as a supplementary text in an intermediate-level remote sensing course. The academic level addressed is upper-division undergraduate or beginning graduate, and familiarity with calculus and basic vector and matrix concepts is assumed.
Multispectral Satellite Image Understanding
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Author : Cem Ünsalan
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-18
Multispectral Satellite Image Understanding written by Cem Ünsalan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-18 with Computers categories.
This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas. Combining a theoretical framework with highly practical applications, the book describes a system for the effective detection of single houses and streets in very high resolution. Topics and features: with a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace Center; provides end-of-chapter summaries and review questions; presents a detailed review on remote sensing satellites; examines the multispectral information that can be obtained from satellite images, with a focus on vegetation and shadow-water indices; investigates methods for land-use classification, introducing precise graph theoretical measures over panchromatic images; addresses the problem of detecting residential regions; describes a house and street network-detection subsystem; concludes with a summary of the key ideas covered in the book.
Image Analysis Classification And Change Detection In Remote Sensing
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Author : Morton John Canty
language : en
Publisher: CRC Press
Release Date : 2019-03-11
Image Analysis Classification And Change Detection In Remote Sensing written by Morton John Canty and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-11 with Technology & Engineering categories.
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.