Kernel Methods for Remote Sensing Data Analysis
Markus Rupp
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Description for Kernel Methods for Remote Sensing Data Analysis
Hardcover. 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.
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.
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 ... Read more
- 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.
Show LessProduct Details
Format
Hardback
Publication date
2009
Publisher
John Wiley & Sons Inc United Kingdom
Number of pages
434
Condition
New
Number of Pages
434
Place of Publication
New York, United States
ISBN
9780470722114
SKU
V9780470722114
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1
About Markus Rupp
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 professor in the Department of Electronics Engineering at the Universitat de Valencia, where he teaches electronics, advanced time series ... Read more
Reviews for Kernel Methods for Remote Sensing Data Analysis
"The editors and the contributors have thought through how best to introduce the various topics and discussions relevant for remote sensing of data analysis and they do it convincingly and compellingly. Their book will deservedly become a proud possession for researchers in the field." (Current Engineering Practice, 1 November 2010)