Kernel Methods for Remote Sensing Data Analysis (eBook, PDF)

Redaktion: Camps-Valls, Gustau; Bruzzone, Lorenzo

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Wiley-Scrivener
• Seitenzahl: 434
• Erscheinungstermin: 17. August 2009
• Englisch
• ISBN-13: 9780470749005
• Artikelnr.: 37299008

Autorenporträt

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).

Inhaltsangabe

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 (Bjorn Waske,
Mathieu Fauvel, Jon Atli Benediktsson and Jocelyn Chanussot).
1.1 Introduction.
1.2 Supervised classification: algorithms and applications.
1.3 Conclusion.
Acknowledgments. 
References.
2 An introduction to kernel learning algorithms (Peter V. Gehler and
Bernhard Scholkopf).
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 (J. Anthony Gualtieri). 
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 (Giles
M. Foody).
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.
Acknowledgments. 
References.
5 Kernel Fisher's Discriminant with heterogeneous kernels (M. Murat Dundar
and Glenn Fung).
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 (Jordi Muñoz-Marí, Luis
Gómez-Choa, Manel Martínez-Ramón, José Luis Rojo-Álvarez, Javier
Calpe-Maravilla and Gustavo Camps-Valls).
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.
Acknowledgments. 
References.
7 Target detection with kernels (Nasser M. Nasrabadi).
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 (Amit Banerjee,
Philippe Burlina and Chris Diehl).
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 (Mattia Marconcini and Lorenzo Bruzzone).
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
(Luis Gómez-Chova, Javier Calpe-Maravilla, Lorenzo Bruzzone and Gustavo
Camps-Valls).
10.1 Introduction.
10.2 Semi-supervised classification with mean kernels.
10.3 Experimental results.
10.4 Conclusions.
Acknowledgments. 
References.
IV Function approximation and regression.
11 Kernel methods for unmixing hyperspectral imagery (Joshua Broadwater,
Amit Banerjee and Philippe Burlina).
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 (Yanfei Wang,
Changchun Yang and Xiaowen Li).
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.
Acknowledgments. 
References.
13 Land and sea surface temperature estimation by support vector regression
(Gabriele Moser and Sebastiano B. Serpico).
13.1 Introduction.
13.2 Previous work.
13.3 Methodology.
13.4 Experimental results.
13.5 Conclusions.
Acknowledgments. 
References.
V Kernel-based feature extraction.
14 Kernel multivariate analysis in remote sensing feature extraction
(Jerónimo Arenas-Garciá and Kaare Brandt Petersen).
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.
Acknowledgments. 
References.
15 KPCA algorithm for hyperspectral target/anomaly detection (Yanfeng Gu).
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.
Acknowledgments 
References.
16 Remote sensing data Classification with kernel nonparametric feature
extractions (Bor-Chen Kuo, Jinn-Min Yang and Cheng-Hsuan Li).
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.