9780470749005

Summary

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.

About the Editors
List of authors
Preface
Acknowledgments
List of symbols
List of abbreviations
Introduction
Machine learning techniques in remote sensing data analysis
Introduction
Supervised classification: algorithms and applications
Conclusion
References
An introduction to kernel learning algorithms
Introduction
Kernels
The representer theorem
Learning with kernels
Conclusion
References
Supervised image classification
The Support Vector Machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data
Introduction
Aspects of hyperspectral data and its acquisition
Hyperspectral remote sensing and supervised classification
Mathematical foundations of supervised classification
From structural risk minimization to a support vector machine algorithm
Benchmark hyperspectral data sets
Results
Using spatial coherence
Why do SVMs perform better than other methods?
Conclusions
References
On training and evaluation of SVM for remote sensing applications
Introduction
Classification for thematic mapping
Overview of classification by a SVM
Training stage
Testing stage
Conclusion
References
Kernel Fishers Discriminant with heterogeneous kernels
Introduction
Linear Fishers Discriminant
Kernel Fisher Discriminant
Kernel Fishers Discriminant with heterogeneous kernels
Automatic kernel selection KFD algorithm
Numerical results
Conclusion
References
Multi-temporal image classification with kernels
Introduction
Multi-temporal classification and change detection with kernels
Contextual and multi-source data fusion with kernels
Multi-temporal/-source urban monitoring
Conclusions
References
Target detection with kernels
Introduction
Kernel learning theory
Linear subspace-based anomaly detectors and their kernel versions
Results
Conclusion
References
One-class SVMs for hyperspectral anomaly detection
Introduction
Deriving the SVDD
SVDD function optimization
SVDD algorithms for hyperspectral anomaly detection
Experimental results
Conclusions
References
Semi-supervised image classification
A domain adaptation SVM and a circular validation strategy for land-cover maps updating
Introduction
Literature survey
Proposed domain adaptation SVM
Proposed circular validation strategy
Experimental results
Discussions and conclusion
References
Mean kernels for semi-supervised remote sensing image classification
Introduction
Semi-supervised classification with mean kernels
Experimental results
Conclusions
References
Function approximation and regression
Kernel methods for unmixing hyperspectral imagery
Introduction
Mixing models
Proposed kernel unmixing algorithm
Experimental results of the kernel unmixing algorithm
Development of physics-based kernels for unmixing
Physics-based kernel results
Summary
References
Kernel-based quantitative remote sensing inversion
Introduction
Typical kernel-based remote sensing inverse problems
Well-posedness and ill-posedness
Regularization
Optimization techniques
Kernel-based BRDF model inversion
Aerosol particle size distribution function retrieval
Conclusion
References
Land and sea surface temperature estimation by support vector regression
Introduction
Previous work
Methodology
Experimental results
Conclusions
References
Kernel-based feature extraction
Kernel multivariate analysis in remote sensing feature extraction
Introduction
Multivariate analysis methods
Kernel multivariate analysis
Sparse Kernel OPLS
Experiments: pixel-based hyperspectral image classification
Conclusions
References
KPCA algorithm for hyperspectral target/anomaly detection
Introduction
Motivation
Kernel-based feature extraction in hyperspectral images
Kernel-based target detection in hyperspectral images
Kernel-based anomaly detection in hyperspectral images
Conclusions
References
Remote sensing data Classification with kernel nonparametric feature extractions
Introduction
Related feature extractions
Kernel-based NWFE and FLFE
Eigenvalue resolution with regularization
Experiments
Comments and conclusions
References
Index
Table of Contents provided by Publisher. All Rights Reserved.

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Kernel Methods for Remote Sensing Data Analysis

0470749008

Summary

Table of Contents

Supplemental Materials

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