Taskin Kavzoglu, Brandt Tso, Paul M. Mather

Classification Methods for Remotely Sensed Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 444
• Erscheinungstermin: 4. September 2024
• Englisch
• ISBN-13: 9781040099056
• Artikelnr.: 72271491

Autorenporträt

Professor Taskin Kavzoglu is a senior researcher in remote sensing with more than 25 years of research experience in Earth observation/remote sensing. He has published more than 150 papers in peer-reviewed journals and international conference proceedings. He received his M.Sc. in Geographical Information Systems and Ph.D. in Remote Sensing from Nottingham University, UK. Currently, he is a professor at Gebze Technical University, Turkey and a member of Turkish Academy of Sciences. He was awarded an ESRI project award in 2019 and best paper award by American Society for Photogrammetry and Remote Sensing (ASPRS) in 2020. He currently serves on the editorial boards of several international journals.

Dr. Brandt Tso is a retired scientist in Taiwan. He received his Ph.D. degree from the School of Geography, The University of Nottingham, U.K., under the supervision of Professor Paul M. Mather. In 2003, Dr. Tso was a postdoctoral fellow at the Remote Sensing Laboratory, Physics Department, Naval Postgraduate School, Monterey, California, U.S.A. Dr. Tso was an associate professor in the information science department, Management College, National Defense University, Taiwan. He has published numerous research papers.

Professor Paul M. Mather (Deceased) graduated from the University of Cambridge in 1966 and in 1969 received his Ph.D. from The University of Nottingham, UK. where he continued as a lecturer, senior lecturer, and full professor from 1988 until he retired in 2006 as an Emeritus Professor. He received the Back Award from the Royal Geographical Society for his work in remote sensing in 1992, and in 2002 was awarded the Order of the British Empire (OBE) by Her Majesty Queen Elizabeth II for services to remote sensing. He lectured in a number of countries around the world.

Inhaltsangabe

1. Fundamentals of Remote Sensing. 2. Pattern Recognition Principles. 3.
Dimensionality Reduction: Feature Extraction and Selection. 4. Multisource
Image Fusion and Classification. 5. Support Vector Machines. 6. Decision
Trees. 7. Deep Learning. 8. Object-Based Image Analysis. 9. Hyperparameter
Optimization. 10. Accuracy Assessment and Model Explainability.