Kernel Methods for Remote Sensing Data Analysis

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 ebook: Kernel Methods for Remote Sensing Data Analysis
ISBN: 9780470749005
parent-ISBN: 9780470722114
Publisher: John Wiley & Sons, Ltd.
Internet download file size: 7041 kb
Pages: 440
Published: 09-2009
Released online for download: 09-03-2009
Editor: Camps-Valls, Gustavo
Editor: Bruzzone, Lorenzo

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Chapter One

Machine learning techniques in remote sensing data analysis

Bj�rn Waske, Mathieu Fauvel, Jon Atli Benediktsson and Jocelyn Chanussot

Several applications have been developed in the field of remote sensing image analysis during the last decades. Besides well-known statistical approaches, many recent methods are based on techniques taken from the field of machine learning. A major aim of machine learning algorithms in remote sensing is supervised classification, which is perhaps the most widely used image classification approach. In this chapter a brief introduction to machine learning and the different paradigms in remote sensing is given. Moreover this chapter briefly discusses the use of recent developments in supervised classification techniques such as neural networks, support vector machines and multiple classifier systems.

1.1 Introduction

1.1.1 Challenges in remote sensing

Owing to the recent development of different Earth observation platforms with increased spatial and spectral resolution as well as higher revisit times, remote sensing provides more detaile ... read full excerpt from Kernel Methods for Remote Sensing Data Analysis ebook

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