Spectral-Spatial Classification of Hyperspectral Remote Sensing Images
1st Edition
Published on 30. September 2015
280 pages
978-1-60807-813-4 (ISBN)
Description
This comprehensive new resource brings you up to date on recent developments in the classification of hyperspectral images using both spectral and spatial information, including advanced statistical approaches and methods. The inclusion of spatial information to traditional approaches for hyperspectral classification has been one of the most active and relevant innovative lines of research in remote sensing during recent years. This book gives you insight into several important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. This book also shows you how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide.
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Jon Atli Benediktsson | Pedram Ghamisi
Spectral-Spatial Classification of Hyperspectral Remote Sensing Images
Book
08/2015
Artech House Publishers
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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
