Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Classification Methods For Remotely Sensed Data
Download Classification Methods For Remotely Sensed Data full books in PDF, epub, and Kindle. Read online Classification Methods For Remotely Sensed Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Classification Methods for Remotely Sensed Data by : Paul Mather
Download or read book Classification Methods for Remotely Sensed Data written by Paul Mather and published by CRC Press. This book was released on 2001-12-06 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Classification Methods for Remotely Sensed Data, Second Edition by : Brandt Tso
Download or read book Classification Methods for Remotely Sensed Data, Second Edition written by Brandt Tso and published by CRC Press. This book was released on 2009-05-12 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keeping abreast of new developments, this new edition provides a comprehensive and up-to-date review of the entire field of classification methods applied to remotely sensed data. It provides seven fully revised chapters and two new chapters covering support vector machines (SVM) and decision trees.
Book Synopsis Classification Methods for Remotely Sensed Data by : Paul Mather
Download or read book Classification Methods for Remotely Sensed Data written by Paul Mather and published by CRC Press. This book was released on 2016-04-19 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publishing of the first edition of Classification Methods for Remotely Sensed Data in 2001, the field of pattern recognition has expanded in many new directions that make use of new technologies to capture data and more powerful computers to mine and process it. What seemed visionary but a decade ago is now being put to use and refined in
Book Synopsis Assessing the Accuracy of Remotely Sensed Data by : Russell G. Congalton
Download or read book Assessing the Accuracy of Remotely Sensed Data written by Russell G. Congalton and published by CRC Press. This book was released on 2008-12-12 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accuracy assessment of maps derived from remotely sensed data has continued to grow since the first edition of this groundbreaking book. As a result, the much-anticipated new edition is significantly expanded and enhanced to reflect growth in the field. The new edition features three new chapters, including: Fuzzy accuracy assessmentPositional accu
Book Synopsis Computer Processing of Remotely-Sensed Images by : Paul M. Mather
Download or read book Computer Processing of Remotely-Sensed Images written by Paul M. Mather and published by John Wiley & Sons. This book was released on 2005-12-13 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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/
Book Synopsis Classification Methods for Remotely Sensed Data by : Taskin Kavzoglu
Download or read book Classification Methods for Remotely Sensed Data written by Taskin Kavzoglu and published by CRC Press. This book was released on 2024-09-04 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. New in this edition: Provides comprehensive background on the theory of deep learning and its application to remote sensing data. Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications. Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies. Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models. This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc.
Book Synopsis Remotely Sensed Data Characterization, Classification, and Accuracies by : Ph.D., Prasad S. Thenkabail
Download or read book Remotely Sensed Data Characterization, Classification, and Accuracies written by Ph.D., Prasad S. Thenkabail and published by CRC Press. This book was released on 2015-10-02 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: A volume in the Remote Sensing Handbook series, Remotely Sensed Data Characterization, Classification, and Accuracies documents the scientific and methodological advances that have taken place during the last 50 years. The other two volumes in the series are Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, and Remote Sensing of
Book Synopsis Remote Sensing by : Robert A. Schowengerdt
Download or read book Remote Sensing written by Robert A. Schowengerdt and published by Elsevier. This book was released on 2012-12-02 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a completely updated, greatly expanded version of the previously successful volume by the author. The Second Edition includes new results and data, and discusses a unified framework and rationale for designing and evaluating image processing algorithms. Written from the viewpoint that image processing supports remote sensing science, this book describes physical models for remote sensing phenomenology and sensors and how they contribute to models for remote-sensing data. The text then presents image processing techniques and interprets them in terms of these models. Spectral, spatial, and geometric models are used to introduce advanced image processing techniques such as hyperspectral image analysis, fusion of multisensor images, and digital elevationmodel extraction from stereo imagery. The material is suited for graduate level engineering, physical and natural science courses, or practicing remote sensing scientists. Each chapter is enhanced by student exercises designed to stimulate an understanding of the material. Over 300 figuresare produced specifically for this book, and numerous tables provide a rich bibliography of the research literature.
Book Synopsis Remote Sensing Digital Image Analysis by : John A. Richards
Download or read book Remote Sensing Digital Image Analysis written by John A. Richards and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 :Ioannis Kanellopoulos Publisher :Springer Science & Business Media ISBN 13 :3642590411 Total Pages :292 pages Book Rating :4.6/5 (425 download)
Book Synopsis Neurocomputation in Remote Sensing Data Analysis by : Ioannis Kanellopoulos
Download or read book Neurocomputation in Remote Sensing Data Analysis written by Ioannis Kanellopoulos and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: A state-of-the-art view of recent developments in the use of artificial neural networks for analysing remotely sensed satellite data. Neural networks, as a new form of computational paradigm, appear well suited to many of the tasks involved in this image analysis. This book demonstrates a wide range of uses of neural networks for remote sensing applications and reports the views of a large number of European experts brought together as part of a concerted action supported by the European Commission.
Author :Pramod K. Varshney Publisher :Springer Science & Business Media ISBN 13 :9783540216681 Total Pages :344 pages Book Rating :4.2/5 (166 download)
Book Synopsis Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data by : Pramod K. Varshney
Download or read book Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data written by Pramod K. Varshney and published by Springer Science & Business Media. This book was released on 2004-08-12 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first of its kind, this book reviews image processing tools and techniques including Independent Component Analysis, Mutual Information, Markov Random Field Models and Support Vector Machines. The book also explores a number of experimental examples based on a variety of remote sensors. The book will be useful to people involved in hyperspectral imaging research, as well as by remote-sensing data like geologists, hydrologists, environmental scientists, civil engineers and computer scientists.
Book Synopsis Classification of Remotely Sensed Images by : I.L Thomas
Download or read book Classification of Remotely Sensed Images written by I.L Thomas and published by CRC Press. This book was released on 1987-01-01 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Remote Sensing Handbook - Three Volume Set by : Prasad Thenkabail
Download or read book Remote Sensing Handbook - Three Volume Set written by Prasad Thenkabail and published by CRC Press. This book was released on 2018-10-03 with total page 2304 pages. Available in PDF, EPUB and Kindle. Book excerpt: A volume in the three-volume Remote Sensing Handbook series, Remote Sensing of Water Resources, Disasters, and Urban Studies documents the scientific and methodological advances that have taken place during the last 50 years. The other two volumes in the series are Remotely Sensed Data Characterization, Classification, and Accuracies, and Land Reso
Book Synopsis Kernel Methods for Remote Sensing Data Analysis by : Gustau Camps-Valls
Download or read book Kernel Methods for Remote Sensing Data Analysis written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2009-09-03 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.
Book Synopsis Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification by : Anil Kumar
Download or read book Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification written by Anil Kumar and published by CRC Press. This book was released on 2020-07-19 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 Land Cover Classification of Remotely Sensed Images by : S. Jenicka
Download or read book Land Cover Classification of Remotely Sensed Images written by S. Jenicka and published by Springer Nature. This book was released on 2021-03-10 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
