Classification Methods Remotely Sensed by Kavzoglu Taskin (20 results)

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Hardcover. Condition: new. Hardcover. 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. The new 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, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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. The new 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, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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. The new 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, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: Brand New. 3rd edition. 520 pages. 10.00x7.00x10.00 inches. In Stock.

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 pro.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The new 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, and presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods.