Classification Methods For Remotely Sensed Data

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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

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 : 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.

Remote Sensing Handbook Three Volume Set

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Author : Prasad Thenkabail
language : en
Publisher: CRC Press
Release Date : 2018-10-03

Remote Sensing Handbook Three Volume Set written by Prasad 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 2018-10-03 with Technology & Engineering categories.

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

Remote Sensing Of Natural Resources

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Author : Guangxing Wang
language : en
Publisher: CRC Press
Release Date : 2013-07-12

Remote Sensing Of Natural Resources written by Guangxing Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-12 with Nature categories.

Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for evaluation of natural resources, including sampling design, land use and land cover classification, natural landscape and ecosystem assessment, forestry, agriculture, biomass and carbon-cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping. The book combines review articles with case studies that demonstrate recent advances and developments of methods, techniques, and applications of remote sensing, with each chapter on a specific area of natural resources. Through a comprehensive examination of the wide range of applications of remote sensing technologies to natural resources, the book provides insight into advanced remote sensing systems, technologies, and algorithms for researchers, scientists, engineers, and decision makers.

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.

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 Water Resources, Disasters, and Urban Studies. This volume demonstrates the experience, utility, methods, and models used in studying a wide array of remotely sensed data characterization, classification, and accuracies for terrestrial applications. Leading experts on global geographic coverage, study areas, and array of satellite and sensors contribute to this unique handbook. This theoretical as well as highly practical book represents a thorough history of advancement in the field over last 50 years, bringing us to where we are now, and highlighting future possibilities. Highlights include: Fundamental and advanced topics in remote-sensing satellites and sensors Remote sensing data calibration, normalization, harmonization, and synthesis Optical, Radar, LiDAR, thermal, hyperspectral, and other satellite sensors, normalization of remotely sensed data, and data degradations Digital image processing, urban image classification, and image classification methods in land use\land cover, cropland, change detection studies Enhanced vegetation indices and standardization of vegetation indices Object-based image analysis (OBIA) and geospatial data integration LiDAR data processing and applications Geoprocessing, GIS, and GIScience GNSS applications Crowdsourcing and cloud computing Google Earth for Earth Sciences Map accuracies Remote-sensing law or space law, and a host of other topics.

Image And Signal Processing For Remote Sensing

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Author :
language : en
Publisher:
Release Date : 2004

Image And Signal Processing For Remote Sensing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Image processing categories.

Machine Processing Of Remotely Sensed Data

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Author : S. K. Mengel
language : en
Publisher:
Release Date : 1985

Machine Processing Of Remotely Sensed Data written by S. K. Mengel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with Remote sensing categories.

Sport Materials Modelling And Simulation

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Author : Yan Wen Wu
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-02-12

Sport Materials Modelling And Simulation written by Yan Wen Wu and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-12 with Technology & Engineering categories.

Selected, peer reviewed papers from the 2011 International Conference on Sport Material, Modelling and Simulation (ICSMMS 2011), January 5-6, 2011, Wuhan, P.R. China