Satellite Image Classification Methods
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Artificial Intelligence Techniques For Satellite Image Analysis
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Author : D. Jude Hemanth
language : en
Publisher: Springer Nature
Release Date : 2019-11-13
Artificial Intelligence Techniques For Satellite Image Analysis written by D. Jude Hemanth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-13 with Computers categories.
The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.
Satellite Image Classification Methods
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-03-09
Satellite Image Classification Methods written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-09 with Technology & Engineering categories.
Embark on a journey into the realm of satellite image classification with this comprehensive guide, unlocking the secrets of extracting valuable information from vast amounts of Earth observation data. Delve into the fundamental principles and cutting-edge techniques of this transformative technology, empowering yourself to harness the wealth of knowledge hidden within satellite imagery. Discover the intricacies of spectral, textural, and contextual analysis, and unravel the complexities of machine learning algorithms, object-based image analysis, and hyperspectral image classification. Explore specialized domains such as radar and SAR image classification, gaining insights into their unique characteristics and applications. Witness the synergistic power of multi-sensor and multi-temporal data integration, unlocking enhanced classification accuracy and temporal analysis capabilities. Delve into the importance of accuracy assessment and validation, ensuring the reliability and trustworthiness of your classification results. With real-world case studies and illustrative examples, this book brings the concepts and techniques to life, showcasing the practical applications of satellite image classification in diverse domains. From land cover mapping and environmental monitoring to disaster response and urban planning, see how this technology drives informed decision-making and addresses real-world challenges. Whether you are a student seeking a comprehensive understanding of satellite image classification, a researcher delving into advanced techniques, or a practitioner seeking to harness the power of this technology, this book is your ultimate resource. Its interdisciplinary approach and comprehensive coverage make it an essential guide for anyone seeking to unlock the transformative potential of satellite image classification. Immerse yourself in the world of satellite image classification and uncover the hidden insights within Earth observation data. This book is your key to unlocking the transformative power of this technology, empowering you to make informed decisions and drive positive change. If you like this book, write a review!
Satellite Image Analysis
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Author : Surekha Borra
language : en
Publisher:
Release Date : 2019
Satellite Image Analysis written by Surekha Borra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 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.
Machine Learning And Data Mining In Aerospace Technology
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer
Release Date : 2019-07-02
Machine Learning And Data Mining In Aerospace Technology written by Aboul Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-02 with Computers categories.
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry 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.
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.
Fundamentals And Methods Of Machine And Deep Learning
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Author : Pradeep Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-02
Fundamentals And Methods Of Machine And Deep Learning written by Pradeep Singh 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 2022-03-02 with Computers categories.
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
An Evaluation Of Satellite Image Classification Techniques To Assess Spruce Fir Regeneration In Northern Maine
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Author : Steven A. Sader
language : en
Publisher:
Release Date : 1989
An Evaluation Of Satellite Image Classification Techniques To Assess Spruce Fir Regeneration In Northern Maine written by Steven A. Sader and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Fir categories.
Introductory Digital Image Processing
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Author : John R. Jensen
language : en
Publisher:
Release Date : 2005
Introductory Digital Image Processing written by John R. Jensen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. This revision of Introductory Digital Image Processing: A Remote Sensing Perspective continues to focus on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Prentice Hall Series Geographic Information Science.