Kernel Methods Remote Sensing (20 results)

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Hardcover. Condition: new. Hardcover. Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods.Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection.Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification.Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition. Editors and contributors are experts in the field of kernel methods (KMs) for remote sensing. Provides state of the art knowledge, analysing the methodological and practical challenges related to the application of KMs to remote sensing problems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condition: New. Editors and contributors are experts in the field of kernel methods (KMs) for remote sensing. Provides state of the art knowledge, analysing the methodological and practical challenges related to the application of KMs to remote sensing problems. Editor(s): Camps-Valls, Gustavo; Bruzzone, Lorenzo. Num Pages: 434 pages, Illustrations. BIC Classification: RGW. Category: (P) Professional & Vocational. Dimension: 248 x 176 x 29. Weight in Grams: 932. . 2009. 1st Edition. Hardcover. . . . .

Hardcover. Condition: new. Hardcover. Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods.Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection.Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification.Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition. Editors and contributors are experts in the field of kernel methods (KMs) for remote sensing. Provides state of the art knowledge, analysing the methodological and practical challenges related to the application of KMs to remote sensing problems. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condition: New. Gustavo Camps-Valls was born in Valencia, Spain in 1972, and received a B.Sc. degree in Physics (1996), a B.Sc. degree in Electronics Engineering (1998), and a Ph.D. degree in Physics (2002) from the Universitat de Valencia. He is currently an associate pro.

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Hardcover. Condition: Brand New. 1st edition. 434 pages. 10.00x7.00x1.00 inches. In Stock.

Condition: New. Editors and contributors are experts in the field of kernel methods (KMs) for remote sensing. Provides state of the art knowledge, analysing the methodological and practical challenges related to the application of KMs to remote sensing problems. Editor(s): Camps-Valls, Gustavo; Bruzzone, Lorenzo. Num Pages: 434 pages, Illustrations. BIC Classification: RGW. Category: (P) Professional & Vocational. Dimension: 248 x 176 x 29. Weight in Grams: 932. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

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Buch. Condition: Neu. Neuware - Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection.Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges:\* Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods.\* Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection.\* Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification.\* Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs.\* Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions.This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Hardcover. Condition: new. Hardcover. Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods.Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection.Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification.Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition. Editors and contributors are experts in the field of kernel methods (KMs) for remote sensing. Provides state of the art knowledge, analysing the methodological and practical challenges related to the application of KMs to remote sensing problems. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.