Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes
Fully automated interpretation and understanding of remotely sensed data by a computer has been a challenge for many decades, and many approaches have been developed over the years. Significant advances in knowledge-based image understanding, machine learning and artificial intelligence has led to this topic being the focus of much research in recent years.

This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Thereby, both classical knowledge-based as well as modern machine learning-oriented concepts are described. A field such as this is specialized and dynamic and also interdisciplinary and multilayered. Written by an international team of experts, the book has therefore been split into parts dealing with the concepts and applications, and the focus is on elucidating the complementarity of different lines of research rather than providing the complete set of scientific approaches.

Part A of this book gives insight into the basic theories and concepts of feature extraction, image understanding and the respective assessment strategies as well as into geometric, radiometric and sensor-related fundamentals of remote sensing technology. Part B focuses on various scientific and practical applications of remote sensing data analysis. These range from the automatic detailed reconstruction of complex 3D environments to visual tracking of objects in image sequences as well as monitoring natural and anthropogenic long-term processes on a regional scale. Part C sketches recent trends in automatic analysis of remote sensing data.

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Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes
Fully automated interpretation and understanding of remotely sensed data by a computer has been a challenge for many decades, and many approaches have been developed over the years. Significant advances in knowledge-based image understanding, machine learning and artificial intelligence has led to this topic being the focus of much research in recent years.

This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Thereby, both classical knowledge-based as well as modern machine learning-oriented concepts are described. A field such as this is specialized and dynamic and also interdisciplinary and multilayered. Written by an international team of experts, the book has therefore been split into parts dealing with the concepts and applications, and the focus is on elucidating the complementarity of different lines of research rather than providing the complete set of scientific approaches.

Part A of this book gives insight into the basic theories and concepts of feature extraction, image understanding and the respective assessment strategies as well as into geometric, radiometric and sensor-related fundamentals of remote sensing technology. Part B focuses on various scientific and practical applications of remote sensing data analysis. These range from the automatic detailed reconstruction of complex 3D environments to visual tracking of objects in image sequences as well as monitoring natural and anthropogenic long-term processes on a regional scale. Part C sketches recent trends in automatic analysis of remote sensing data.

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Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes

Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes

Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes

Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes

Overview

Fully automated interpretation and understanding of remotely sensed data by a computer has been a challenge for many decades, and many approaches have been developed over the years. Significant advances in knowledge-based image understanding, machine learning and artificial intelligence has led to this topic being the focus of much research in recent years.

This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Thereby, both classical knowledge-based as well as modern machine learning-oriented concepts are described. A field such as this is specialized and dynamic and also interdisciplinary and multilayered. Written by an international team of experts, the book has therefore been split into parts dealing with the concepts and applications, and the focus is on elucidating the complementarity of different lines of research rather than providing the complete set of scientific approaches.

Part A of this book gives insight into the basic theories and concepts of feature extraction, image understanding and the respective assessment strategies as well as into geometric, radiometric and sensor-related fundamentals of remote sensing technology. Part B focuses on various scientific and practical applications of remote sensing data analysis. These range from the automatic detailed reconstruction of complex 3D environments to visual tracking of objects in image sequences as well as monitoring natural and anthropogenic long-term processes on a regional scale. Part C sketches recent trends in automatic analysis of remote sensing data.

Product Details

ISBN-13: 9781849951289
Publisher: Whittles Publishing
Publication date: 09/07/2021
Pages: 352
Product dimensions: 6.42(w) x 9.66(h) x 1.01(d)

Table of Contents

Part A: Methodology Introduction Object, data and sensor modelling Feature extraction from images and point clouds: Fundamentals, advances and trends A short survey on supervised classification in remote Context-based classification Toward a framework for quality assessment in remote sensing applications Part B: Application From raw 3D point clouds to semantic objects Traffic extraction and characterization from optical remote sensing data Object extraction in image sequences A process-based model approach to predict future land-use changes and link biodiversity with soil erosion in Chile Interferometric SAR Image analysis for 3D building reconstruction Detection and classification of collapsed buildings after a strong earthquake by means of laser scanning and image analysis A settlement process analysis in coastal Benin – confronting scarce data availability in developing countries Part C: Conclusion Benchmarking – a basic requirement for effective performance evaluation
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