Kernel Methods for Remote Sensing Data Analysis

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

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

"The editors and the contributors have thought through how best to introduce the various topics and discussions relevant for remote sensing of data analysis and they do it convincingly and compellingly. Their book will deservedly become a proud possession for researchers in the field." (Current Engineering Practice, 1 November 2010)