Kernel Methods for Remote Sensing Data Analysis

by Camps-Valls, Gustau; Bruzzone, Lorenzo

• ISBN13: 9780470722114
• ISBN10: 0470722118
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-12-02
• Publisher: Wiley

Summary

Written by well-regarded experts in the field, Kernel Methods for Remote Sensing Data Analysis presents the theoretical foundations of kernel methods relevant to the remote sensing domain. This practical guide to the design and implementation of these methods enables engineers, scientists, and researchers to develop a robust and principled approach to their particular application. The text includes state-of-the art-knowledge analyzing the methodological and practical challenges related to the application of KMs to remote sensing problems.

Author Biography

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 processing, machine learning for remote sensing and digital signal processing. His research interests are tied to the development of machine learning algorithms for signal and image processing, with special attention to adaptive systems, neural networks and kernel methods. He conducts and supervises research on the application of these methods to remote sensing image analysis and recognition, and image denoising and coding. Dr Camps-Valls is the author (or co-author) of 50 papers in referred international journals, more than 70 international conference papers, 15 book chapters, and is editor of other related books, such as Kernel Methods in Bioengineering, Signal and Image Processing (IGI, 2007). He has served as reviewer to many international journals, and on the Program Committees of SPIE Europe, IGARSS, IWANN and ICIP. Dr Camps-Valls was a member of the European Network on Intelligent Technologies for Smart Adaptive Systems (EUNITE), and the Spanish Thematic Networks on 'Pattern Recognition' and 'Biomedical Engineering'. He is active in the R+D sector through a large number of projects funded by both public and industrial partners, both at national and international levels. He is an Evaluator of project proposals and scientific organizations. Since 2003 he has been a member of the IEEE and SPIE. Since 2009 he has been a member of the machine Learning for Signal Processing (MLSP) Technical Committee of the IEEE Signal Processing Society. Visit http://www.uv.es/gcamps for more information.

Lorenzo Bruzzone received a laurea (M.S.) degree in electronic engineering (summa cum laude) ad a Ph.D. degree in telecommunications from the University of Genoa, Italy, in 1993 and 1998, respectively. From 1998 to 2000 he was a Postdoctoral researcher at the University of Genoa. In 2000 he joined the University of Trento, Italy, where he is currently a Full Professor telecommunications. He teaches remote sensing, pattern recognition, radar and electrical communications. Dr Bruzzone is the Head of the remote Sensing Laboratory in the Department of Information Engineering and Computer Science, University of Trento. His current research interests are in the area of remote-sensing image processing and recognition (analysis of multitemporal data, feature extraction and election, classification, regression and estimation, data fusion and machine learning). He conducts and supervises research on these topics within the frameworks of several national and international projects. He is an Evaluator of project proposals for many different governments (including the European Commission) and scientific organizations. He is the author (or co-author) of 74 scientific publication in referred international journals, more than 140 papers in conference proceedings and 7 book chapters. He is a referee for many international journals and has served on the Scientific Committees of several international conferences. He is a member of the Managing Committee of the Italian Inter-University Consortium on Telecommunications and a member of the Scientific Committee of the India-Italy Center for Advanced Research. Since 2009 he has been a member of the Administrative Committee of the IEEE Geoscience and Remote Sensing Society. Dr Bruzzone gained first place in the Student Prize Paper Competition of the 1998 IEEE International Geoscience and Remote Sensing Symposium (Seattle, July 1998). He was a recipient of the Recognition of IEEE Transactions on Geoscience and remote Sensing Best reviewers in 1999 and was a Guest Editor of a Special Issue of the IEEE Transactions on Geoscience and Remote Sensing on the subject of the analysis of multitemporal remote-sensing images (November 2003). He was the General Chair and Co-chair of the First and Second IEEE International Workshop on the Analysis of Multi-temporal remote-Sensing Images (MultiTemp), and is currently a member of the Permanent Steering Committee of this series of workshops. Since 2003, he has been the Chair of the SPIE Conference on Image and Signal Processing for Remote Sensing. From 2004 to 2006 he served as an Associate Editor for the IEEE Geoscience and Remote Sensing Letters, and currently is an Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing, and the Canadian Journal of Remote Sensing. He is a Senior member of IEEE, and also a member of the International Association for Pattern Recognition and of the Italian Association for Remote Sensing (AIT).

Table of Contents

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 Fisher's Discriminant with heterogeneous kernels

Introduction

Linear Fisher's Discriminant

Kernel Fisher Discriminant

Kernel Fisher's 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.