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Kernel Methods for Remote Sensing Data Analysis
eBook Publisher: John Wiley & Sons
Imprint: John Wiley & Sons, Ltd.
Format: Adobe Encrypted (DRM)
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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.
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| Title of Technology eBook: Kernel Methods for Remote Sensing Data Analysis | |
| Release Date: 09-03-2009 | |
| Publisher: John Wiley & Sons, Ltd. |
This eBook download is available in the following formats:
| Parent title | Kernel Methods for Remote Sensing... |
|---|---|
| Encrypted (DRM) | Yes |
| SKU | 9780470749005 |
| File size | 7041 |
| Security | n/a |
| Printing | Not allowed |
| Copying | Not allowed |
| Read aloud | No Sys requirements Download reader |
| Devices | Samsung Tablet, Apple Ipad & Iphone, Barnes & Noble Nook, Kobo eReader, Aluratek Libre, Iliad, Nokia, Blackberry, Hanlin |
| Note | Excellent navigation features are available via Adobe such as bookmarks and a quick access table of contents. Text search is easily accessible. An Adobe DRM-protected file is different than a pdf file in that it uses Adobe DRM (Digital Rights Management) technology, which authors and publishers use to protect their content from illegal online distribution and to set certain privileges such as restrictions on copying and printing. |
Kernel Methods for Remote Sensing Data Analysis
Chapter One
Machine learning techniques in remote sensing data analysisBj
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