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.

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 Fishers Discriminant with heterogeneous kernels 5.1 Introduction 5.2 Linear Fishers Discriminant 5.3 Kernel Fisher Discriminant 5.4 Kernel Fishers 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