next generation hyperspectral image analysis (9 Ergebnisse)

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive guide that bridges the gap between foundational principles and cutting-edge advancements in hyperspectral imaging and deep learning. With contributions from leading international experts, this book covers a wide range of topics essential for researchers, engineers, and professionals in the field.This book begins with an introduction to hyperspectral imaging and deep learning, setting the stage for more advanced discussions. Subsequent chapters delve into neural network architectures, graph-based methods, generative models, and the application of transformers in hyperspectral imaging. Each chapter not only presents theoretical insights but also practical applications, making complex concepts accessible and relevant.Readers will discover methods to optimize deep learning models through techniques like quantization and pruning, ensuring efficiency without sacrificing performance. Additionally, this book addresses the practical challenges of managing and processing large volumes of hyperspectral data, offering strategies for data storage, management, and parallel processing.Exclusive online resources, including example codes, tutorials, and hyperspectral datasets, complement the comprehensive content, enabling readers to apply what they learn in real-world scenarios. This book is an indispensable resource for anyone looking to harness the power of hyperspectral technology to drive innovation and solve complex problems.

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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a comprehensive guide that bridges the gap between foundational principles and cutting-edge advancements in hyperspectral imaging and deep learning. With contributions from leading international experts, this book covers a wide range of topics essential for researchers, engineers, and professionals in the field.This book begins with an introduction to hyperspectral imaging and deep learning, setting the stage for more advanced discussions. Subsequent chapters delve into neural network architectures, graph-based methods, generative models, and the application of transformers in hyperspectral imaging. Each chapter not only presents theoretical insights but also practical applications, making complex concepts accessible and relevant.Readers will discover methods to optimize deep learning models through techniques like quantization and pruning, ensuring efficiency without sacrificing performance. Additionally, this book addresses the practical challenges of managing and processing large volumes of hyperspectral data, offering strategies for data storage, management, and parallel processing.Exclusive online resources, including example codes, tutorials, and hyperspectral datasets, complement the comprehensive content, enabling readers to apply what they learn in real-world scenarios. This book is an indispensable resource for anyone looking to harness the power of hyperspectral technology to drive innovation and solve complex problems. 269 pp. Englisch.

Buch. Zustand: Neu. Next-Generation Hyperspectral Image Analysis | Using Deep Learning Method | Juan M. Haut (u. a.) | Buch | xvi | Englisch | 2026 | Springer | EAN 9789819520374 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is a comprehensive guide that bridges the gap between foundational principles and cutting-edge advancements in hyperspectral imaging and deep learning. With contributions from leading international experts, this book covers a wide range of topics essential for researchers, engineers, and professionals in the field. This book begins with an introduction to hyperspectral imaging and deep learning, setting the stage for more advanced discussions. Subsequent chapters delve into neural network architectures, graph-based methods, generative models, and the application of transformers in hyperspectral imaging. Each chapter not only presents theoretical insights but also practical applications, making complex concepts accessible and relevant. Readers will discover methods to optimize deep learning models through techniques like quantization and pruning, ensuring efficiency without sacrificing performance. Additionally, this book addresses the practical challenges of managing and processing large volumes of hyperspectral data, offering strategies for data storage, management, and parallel processing. Exclusive online resources, including example codes, tutorials, and hyperspectral datasets, complement the comprehensive content, enabling readers to apply what they learn in real-world scenarios. This book is an indispensable resource for anyone looking to harness the power of hyperspectral technology to drive innovation and solve complex problems.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 288 pp. Englisch.

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