
Spectral-Spatial Classification of Hyperspectral Remote Sensing Images
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Content
- Intro
- Spectral-Spatial Classification of Hyperspectral Remote Sensing Images
- Contents
- Foreword
- Acknowledgments
- Chapter 1 Introduction
- 1.1 INTRODUCTION TO HYPERSPECTRAL IMAGING SYSTEMS
- 1.2 HIGH-DIMENSIONAL DATA
- 1.2.1 Geometrical and Statistical Properties of High Dimensional Data and the Need for Feature Reduction
- 1.2.2 Conventional Spectral Classifiers and the Importance of Considering Spatial Information
- 1.3 SUMMARY
- Chapter 2 Classification Approaches
- 2.1 CLASSIFICATION
- 2.2 STATISTICAL CLASSIFICATION
- 2.2.1 Support Vector Machines
- 2.2.2 Neural Network Classifiers
- 2.2.3 Decision Tree Classifiers
- 2.3 MULTIPLE CLASSIFIERS
- 2.3.1 Boosting
- 2.3.2 Bagging
- 2.3.3 Random Forest
- 2.4 THE ECHO CLASSIFIER
- 2.5 ESTIMATION OF CLASSIFICATION ERROR
- 2.5.1 Confusion Matrix
- 2.5.2 Average Accuracy (AA)
- 2.6 SUMMARY
- Chapter 3 Feature Reduction
- 3.1 FEATURE EXTRACTION (FE)
- 3.1.1 Principal Component Analysis (PCA)
- 3.1.2 Independent Component Analysis
- 3.1.3 Discriminant Analysis Feature Extraction (DAFE)
- 3.1.4 Decision Boundary Feature Extraction (DBFE)
- 3.1.5 Nonparametric Weighted Feature Extraction (NWFE)
- 3.2 FEATURE SELECTION
- 3.2.1 Supervised and Unsupervised Feature Selection Techniques
- 3.2.2 Evolutionary-Based Feature Selection Techniques
- 3.2.3 Genetic Algorithm (GA)-Based Feature Selection
- 3.2.4 Particle Swarm Optimization (PSO)-Based Feature Selection
- 3.2.5 Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO)-Based Feature Selection
- 3.2.6 FODPSO-Based Feature Selection
- 3.3 SUMMARY
- Chapter 4 Spatial Information Extraction Using Segmentation
- 4.1 SOME APPROACHES FOR THE INTEGRATION OF SPECTRAL AND SPATIAL INFORMATION
- 4.1.1 Feature Fusion into a Stacked Vector
- 4.1.2 Composite Kernel
- 4.1.3 Spectral-Spatial Classification Using Majority Voting
- 4.2 CLUSTERING APPROACHES
- 4.2.1 K-Means
- 4.2.2 Fuzzy C-Means Clustering (FCM)
- 4.2.3 Particle Swarm Optimization (PSO)-Based FCM (PSO-FCM)
- 4.3 EXPECTATION MAXIMIZATION (EM)
- 4.4 MEAN-SHIFT SEGMENTATION (MSS)
- 4.5 WATERSHED SEGMENTATION (WS)
- 4.6 HIERARCHICAL SEGMENTATION (HSEG)
- 4.7 SEGMENTATION AND CLASSIFICATION USING AUTOMATICALLY SELECTED MARKERS
- 4.7.1 Marker Selection Using Probabilistic SVM
- 4.7.2 Multiple Classifier Approach for Marker Selection
- 4.7.3 Construction of a Minimum Spanning Forest (MSF)
- 4.8 THRESHOLDING-BASED SEGMENTATION TECHNIQUES
- 4.8.1 Image Thresholding
- 4.8.2 Classification Based on Thresholding-Based Image Segmentation
- 4.8.3 Experimental Evaluation of Different Spectral-Spatial Classification Approaches Based on Different Segmentation Methods
- 4.9 SUMMARY
- Chapter 5 Morphological Profile
- 5.1 MATHEMATICAL MORPHOLOGY (MM)
- 5.1.1 Morphological Operators
- 5.1.2 Morphological Profile (MP)
- 5.1.3 Morphological Neighborhood
- 5.1.4 Spectral-Spatial Classification
- 5.2 SUMMARY
- Chapter 6 Attribute Profiles
- 6.1 FUNDAMENTAL PROPERTIES
- 6.2 MORPHOLOGICAL ATTRIBUTE FILTER (AF)
- 6.2.1 Attribute Profile and Its Extension to Hyperspectral Images
- 6.3 SPECTRAL-SPATIAL CLASSIFICATION BASED ON AP
- 6.3.1 Strategy 1
- 6.3.2 Strategy 2
- 6.4 SUMMARY
- Chapter 7 Conclusion and Future Works
- 7.1 CONCLUSIONS
- 7.2 PERSPECTIVES
- Appendix A: CEM Clustering
- Appendix B: Spectral Angle Mapper (SAM)
- Appendix C: Prim's Algorithm
- Appendix D: Data Sets Description
- Abbreviations and Acronyms
- Bibliography
- About the Authors
- Index
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