Kernel methods for remote sensing data analysis / / edited by Gustavo Camps-Valls, Lorenzo Bruzzone.
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
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E-Book
| Person | |
|---|---|
| Beteiligte Person(en) | |
| Ort, Verlag, Jahr |
Chichester, West Sussex ; Hoboken, NJ
: Wiley
, 2009
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| Umfang | 1 online resource (444 p.) |
| ISBN | 1-282-29170-X 9786612291708 0-470-74899-0 0-470-74900-8 |
| Sprache | Englisch |
| Zusatzinfo | Description based upon print version of record. |
| Zusatzinfo | Kernel Methods for Remote Sensing Data Analysis; Contents; 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; 1.1.1 Challenges in remote sensing; 1.1.2 General concepts of machine learning; 1.1.3 Paradigms in remote sensing; 1.2 Supervised classification: algorithms and applications; 1.2.1 Bayesian classification strategy; 1.2.2 Neural networks; 1.2.3 Support Vector Machines (SVM); 1.2.4 Use of multiple classifiers; 1.3 Conclusion; Acknowledgments References2 An introduction to kernel learning algorithms; 2.1 Introduction; 2.2 Kernels; 2.2.1 Measuring similarity with kernels; 2.2.2 Positive definite kernels; 2.2.3 Constructing the reproducing kernel Hilbert space; 2.2.4 Operations in RKHS; 2.2.5 Kernel construction; 2.2.6 Examples of kernels; 2.3 The representer theorem; 2.4 Learning with kernels; 2.4.1 Support vector classification; 2.4.2 Support vector regression; 2.4.3 Gaussian processes; 2.4.4 Multiple kernel learning; 2.4.5 Structured prediction using kernels; 2.4.6 Kernel principal component analysis 2.4.7 Applications of support vector algorithms2.4.8 Available software; 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; 3.4.1 Empirical risk minimization; 3.4.2 General bounds for a new risk minimization principle; 3.4.3 Structural risk minimization 3.5 From structural risk minimization to a support vector machine algorithm3.5.1 SRM for hyperplane binary classifiers; 3.5.2 SVM algorithm; 3.5.3 Kernel method; 3.5.4 Hyperparameters; 3.5.5 A toy example; 3.5.6 Multi-class classifiers; 3.5.7 Data centring; 3.6 Benchmark hyperspectral data sets; 3.6.1 The 4 class subset scene; 3.6.2 The 16 class scene; 3.6.3 The 9 class scene; 3.7 Results; 3.7.1 SVM implementation; 3.7.2 Effect of hyperparameter d; 3.7.3 Measure of accuracy of results; 3.7.4 Classifier results for the 4 class subset scene and the 16 class full scene 3.7.5 Results for the 9 class scene and comparison of SVM with other classifiers3.7.6 Effect of training set size; 3.7.7 Effect of simulated noisy data; 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; 4.4.1 General recommendations on sample size; 4.4.2 Training a SVM; 4.4.3 Summary on training; 4.5 Testing stage; 4.5.1 General issues in testing 4.5.2 Specific issues for SVM classification |
| Zusatzinfo | English |
| Online-Zugang | Wiley Online Library EBS 2024 |
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