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.

Author Notes

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).

About the Editors

List of authors

Preface

Acknowledgments

List of symbols

List of abbreviations

I Introduction

1 Machine learning techniques in remote sensing data analysis

1.1 Introduction

1.2 Supervised classification: algorithms and applications

1.3 Conclusion

References

2 An introduction to kernel learning algorithms

2.1 Introduction

2.2 Kernels

2.3 The representer theorem

2.4 Learning with kernels

2.5 Conclusion

References

II Supervised image classification

3 The Support Vector Machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data

3.1 Introduction

3.2 Aspects of hyperspectral data and its acquisition

3.3 Hyperspectral remote sensing and supervised classification

3.4 Mathematical foundations of supervised classification

3.5 From structural risk minimization to a support vector machine algorithm

3.6 Benchmark hyperspectral data sets

3.7 Results

3.8 Using spatial coherence

3.9 Why do SVMs perform better than other methods?

3.10 Conclusions

References

4 On training and evaluation of SVM for remote sensing applications

4.1 Introduction

4.2 Classification for thematic mapping

4.3 Overview of classification by a SVM

4.4 Training stage

4.5 Testing stage

4.6 Conclusion

References

5 Kernel Fisher's Discriminant with heterogeneous kernels

5.1 Introduction

5.2 Linear Fisher's Discriminant

5.3 Kernel Fisher Discriminant

5.4 Kernel Fisher's Discriminant with heterogeneous kernels

5.5 Automatic kernel selection KFD algorithm

5.6 Numerical results

5.7 Conclusion

References

6 Multi-temporal image classification with kernels

6.1 Introduction

6.2 Multi-temporal classification and change detection with kernels

6.3 Contextual and multi-source data fusion with kernels

6.4 Multi-temporal/-source urban monitoring

6.5 Conclusions

References

7 Target detection with kernels

7.1 Introduction

7.2 Kernel learning theory

7.3 Linear subspace-based anomaly detectors and their kernel versions

7.4 Results

7.5 Conclusion

References

8 One-class SVMs for hyperspectral anomaly detection

8.1 Introduction

8.2 Deriving the SVDD

8.3 SVDD function optimization

8.4 SVDD algorithms for hyperspectral anomaly detection

8.5 Experimental results

8.6 Conclusions

References

III Semi-supervised image classification

9 A domain adaptation SVM and a circular validation strategy for land-cover maps updating

9.1 Introduction

9.2 Literature survey

9.3 Proposed domain adaptation SVM

9.4 Proposed circular validation strategy

9.5 Experimental results

9.6 Discussions and conclusion

References

10 Mean kernels for semi-supervised remote sensing image classification

10.1 Introduction

10.2 Semi-supervised classification with mean kernels

10.3 Experimental results

10.4 Conclusions

References

IV Function approximation and regression

11 Kernel methods for unmixing hyperspectral imagery

11.1 Introduction

11.2 Mixing models

11.3 Proposed kernel unmixing algorithm

11.4 Experimental results of the kernel unmixing algorithm

11.5 Development of physics-based kernels for unmixing

11.6 Physics-based kernel results

11.7 Summary

References

12 Kernel-based quantitative remote sensing inversion

12.1 Introduction

12.2 Typical kernel-based remote sensing inverse problems

12.3 Well-posedness and ill-posedness

12.4 Regularization

12.5 Optimization techniques

12.6 Kernel-based BRDF model inversion

12.7 Aerosol particle size distribution function retrieval

12.8 Conclusion

References

13 Land and sea surface temperature estimation by support vector regression

13.1 Introduction

13.2 Previous work

13.3 Methodology

13.4 Experimental results

13.5 Conclusions

References

V Kernel-based feature extraction

14 Kernel multivariate analysis in remote sensing feature extraction

14.1 Introduction

14.2 Multivariate analysis methods

14.3 Kernel multivariate analysis

14.4 Sparse Kernel OPLS

14.5 Experiments: pixel-based hyperspectral image classification

14.6 Conclusions

References

15 KPCA algorithm for hyperspectral target/anomaly detection

15.1 Introduction

15.2 Motivation

15.3 Kernel-based feature extraction in hyperspectral images

15.4 Kernel-based target detection in hyperspectral images

15.5 Kernel-based anomaly detection in hyperspectral images

15.6 Conclusions

References

16 Remote sensing data Classification with kernel nonparametric

feature extractions

16.1 Introduction

16.2 Related feature extractions

16.3 Kernel-based NWFE and FLFE

16.4 Eigenvalue resolution with regularization

16.5 Experiments

16.6 Comments and conclusions

References

Index

Available:*

On Order

Summary

Summary

Author Notes

Table of Contents