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Classification Methods For Remotely Sensed Data

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Classification Methods For Remotely Sensed Data

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Author : Taskin Kavzoglu
language : en
Publisher: CRC Press
Release Date : 2024-09-04

Classification Methods For Remotely Sensed Data written by Taskin Kavzoglu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-04 with Technology & Engineering categories.

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.

Classification Methods For Remotely Sensed Data Second Edition

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Author : Brandt Tso
language : en
Publisher: CRC Press
Release Date : 2009-05-12

Classification Methods For Remotely Sensed Data Second Edition written by Brandt Tso and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-12 with Business & Economics categories.

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.

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

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/

Neurocomputation In Remote Sensing Data Analysis

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Author : Ioannis Kanellopoulos
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Neurocomputation In Remote Sensing Data Analysis written by Ioannis Kanellopoulos 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 2012-12-06 with Computers categories.

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.

Kernel Methods For Remote Sensing Data Analysis

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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-03

Kernel Methods For Remote Sensing Data Analysis written by Gustau Camps-Valls 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 2009-09-03 with Technology & Engineering categories.

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.

Remotely Sensed Data Characterization Classification And Accuracies

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Author : Ph.D., Prasad S. Thenkabail
language : en
Publisher: CRC Press
Release Date : 2015-10-02

Remotely Sensed Data Characterization Classification And Accuracies written by Ph.D., Prasad S. Thenkabail and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-02 with Technology & Engineering categories.

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

Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification

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Author : Anil Kumar
language : en
Publisher: CRC Press
Release Date : 2020-07-19

Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification written by Anil Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-19 with Computers categories.

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.

A Comparison Of Four Algorithms For Classification Of Remotely Sensed Data

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Author : Jeffrey A. Wasrud
language : en
Publisher:
Release Date : 1988

A Comparison Of Four Algorithms For Classification Of Remotely Sensed Data written by Jeffrey A. Wasrud and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Remote sensing categories.

Classification Methods For Remotely Sensed Data

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Author : Paul Mather
language : en
Publisher: CRC Press
Release Date : 2016-04-19

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

Classification Methods For Remotely Sensed Data

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