Kernel Methods for Remote Sensing Data Analysis (eBook, PDF)
Redaktion: Camps-Valls, Gustau; Bruzzone, Lorenzo
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Kernel Methods for Remote Sensing Data Analysis (eBook, PDF)
Redaktion: Camps-Valls, Gustau; Bruzzone, Lorenzo
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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,…mehr
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- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 434
- Erscheinungstermin: 17. August 2009
- Englisch
- ISBN-13: 9780470749005
- Artikelnr.: 37299008
- Verlag: John Wiley & Sons
- Seitenzahl: 434
- Erscheinungstermin: 17. August 2009
- Englisch
- ISBN-13: 9780470749005
- Artikelnr.: 37299008
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.
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.