Techniques For Image Processing And Classifications In Remote Sensing

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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.

Techniques Of Image Processing And Classification In Remote Sensing

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Author : Robert A. Schowengerdt
language : en
Publisher:
Release Date : 1983

Techniques Of Image Processing And Classification In Remote Sensing written by Robert A. Schowengerdt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Image processing categories.

Image Processing And Gis For Remote Sensing

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Author : Jian Guo Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-04

Image Processing And Gis For Remote Sensing written by Jian Guo Liu 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 2016-01-04 with Technology & Engineering categories.

Following the successful publication of the 1st edition in 2009, the 2nd edition maintains its aim to provide an application-driven package of essential techniques in image processing and GIS, together with case studies for demonstration and guidance in remote sensing applications. The book therefore has a “3 in 1” structure which pinpoints the intersection between these three individual disciplines and successfully draws them together in a balanced and comprehensive manner. The book conveys in-depth knowledge of image processing and GIS techniques in an accessible and comprehensive manner, with clear explanations and conceptual illustrations used throughout to enhance student learning. The understanding of key concepts is always emphasised with minimal assumption of prior mathematical experience. The book is heavily based on the authors’ own research. Many of the author-designed image processing techniques are popular around the world. For instance, the SFIM technique has long been adopted by ASTRIUM for mass-production of their standard “Pan-sharpen” imagery data. The new edition also includes a completely new chapter on subpixel technology and new case studies, based on their recent research.

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.

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.

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.

Image Analysis Classification And Change Detection In Remote Sensing

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Author : Morton J. Canty
language : en
Publisher: CRC Press
Release Date : 2014-06-06

Image Analysis Classification And Change Detection In Remote Sensing written by Morton J. 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 2014-06-06 with Mathematics categories.

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes. See What’s New in the Third Edition: Inclusion of extensive code in Python, with a cloud computing example New material on synthetic aperture radar (SAR) data analysis New illustrations in all chapters Extended theoretical development The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power. The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.

Signal And Image Processing For Remote Sensing

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Author : C.H. Chen
language : en
Publisher: CRC Press
Release Date : 2012-02-22

Signal And Image Processing For Remote Sensing written by C.H. Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-22 with Computers categories.

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting info

Remote Sensing

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Author : Robert A. Schowengerdt
language : en
Publisher: Elsevier
Release Date : 2006-08-28

Remote Sensing written by Robert A. Schowengerdt and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-28 with Technology & Engineering categories.

Remote sensing is a technology that engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Normally this is accomplished through the use of a satellite or aircraft. Remote Sensing, in its third edition, seamlessly connects the art and science of earth remote sensing with the latest interpretative tools and techniques of computer-aided image processing. Newly expanded and updated, this edition delivers more of the applied scientific theory and practical results that helped the previous editions earn wide acclaim and become classroom and industry standards. Dr. Schowengerdt presents an advanced unified framework and rationale that uniquely empowers the reader with the latest critical thinking skills and prerequisite knowledge needed to successfully design, develop and incorporate maintainable remote sensing solutions for real-world application. Advanced remote sensing image processing techniques such as hyperspectral image analysis, fusion of multisensor images and digital elevation model extraction from stereo imagery are discussed theoretically in terms of spectral, spatial, and geometric models. An expanded exercise section is also included at the end of each chapter allowing for the greatest level of mastery ever. - Features a new lively discussion of the NASA EOS satellites, Terra and Aqua, and the commercial satellites IKONOS and Quickbird - New larger format provides additional access to 32 PAGE - FULL COLOR plate insert and improved readability - Additional data processing algorithms help connect and enhance the collective understanding of engineering design and remotely sensed data

Satellite Image Analysis Clustering And Classification

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Author : Surekha Borra
language : en
Publisher: Springer
Release Date : 2019-02-08

Satellite Image Analysis Clustering And Classification written by Surekha Borra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-08 with Technology & Engineering categories.

Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.