Next-Generation Hyperspectral Image Analysis
Using Deep Learning Method
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Beschreibung
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.
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.
Is a comprehensive guide from theoretical basics to advanced applications Provides access to example codes, tutorials, and hyperspectral datasets for immediate hands-on practice Offers a global and authoritative perspective in hyperspectral image analysis
Autor*in
Juan M. Haut
Themen in »Next-Generation Hyperspectral Image Analysis«
Hyperspectral Deep Learning Neural Networks Unmixing Classification Segmentation Cloud Computing Graphics Processing Units (GPUs)