31.5 BDE in Conjunction with FDE
by Chein-I Chang
Hyperspectral Data Processing: Algorithm Design and Analysis
- Cover
- Title Page
- Copyright
- Dedication
- Preface
- Chapter 1: Overview and Introduction
- 1.1 Overview
- 1.2 Issues of Multispectral and Hyperspectral Imageries
- 1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery
- 1.4 Scope of This Book
- 1.5 Book's Organization
- 1.6 Laboratory Data to be Used in This Book
- 1.7 Real Hyperspectral Images to be Used in this Book
- 1.8 Notations and Terminologies to be Used in this Book
- I: Preliminaries
- Chapter 2: Fundamentals of Subsample and Mixed Sample Analyses
- Chapter 3: Three-Dimensional Receiver Operating Characteristics (3D ROC) Analysis
- Chapter 4: Design of Synthetic Image Experiments
- Chapter 5: Virtual Dimensionality of Hyperspectral Data
- Chapter 6: Data Dimensionality Reduction
- 6.1 Introduction
- 6.2 Dimensionality Reduction by Second-Order Statistics-Based Component Analysis Transforms
- 6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis Transforms
- 6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis Transforms
- 6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis Transforms
- 6.6 Dimensionality Reduction by Feature Extraction-Based Transforms
- 6.7 Dimensionality Reduction by Band Selection
- 6.8 Constrained Band Selection
- 6.9 Conclusions
- II: Endmember Extraction
- Chapter 7: Simultaneous Endmember Extraction Algorithms (SM-EEAs)
- Chapter 8: Sequential Endmember Extraction Algorithms (SQ-EEAs)
- Chapter 9: Initialization-Driven Endmember Extraction Algorithms (ID-EEAs)
- Chapter 10: Random Endmember Extraction Algorithms (REEAs)
- Chapter 11: Exploration on Relationships among Endmember Extraction Algorithms
- III: Supervised Linear Hyperspectral Mixture Analysis
- Chapter 12: Orthogonal Subspace Projection Revisited
- Chapter 13: Fisher's Linear Spectral Mixture Analysis
- 13.1 Introduction
- 13.2 Feature Vector-Constrained FLSMA (FVC-FLSMA)
- 13.3 Relationship Between FVC-FLSMA and LCMV, TCIMF, and CEM
- 13.4 Relationship Between FVC-FLSMA and OSP
- 13.5 Relationship Between FVC-FLSMA and LCDA
- 13.6 Abundance-Constrained Least Squares FLDA (ACLS-FLDA)
- 13.7 Synthetic Image Experiments
- 13.8 Real Image Experiments
- 13.9 Conclusions
- Chapter 14: Weighted Abundance-Constrained Linear Spectral Mixture Analysis
- Chapter 15: Kernel-Based Linear Spectral Mixture Analysis
- IV: Unsupervised Hyperspectral Image Analysis
- V: Hyperspectral Information Compression
- VI: Hyperspectral Signal Coding
- VII: Hyperspectral Signal Characterization
- VIII: Applications
- Chapter 30: Applications of Target Detection
- Chapter 31: Nonlinear Dimensionality Expansion to Multispectral Imagery
- Chapter 32: Multispectral Magnetic Resonance Imaging
- Chapter 33: Conclusions
- 33.1 Design Principles for Nonliteral Hyperspectral Imaging Techniques
- 33.2 Endemember Extraction
- 33.3 Linear Spectral Mixture Analysis
- 33.4 Anomaly Detection
- 33.5 Support Vector Machines and Kernel-Based Approaches
- 33.6 Hyperspectral Compression
- 33.7 Hyperspectral Signal Processing
- 33.8 Applications
- 33.9 Further Topics
- Glossary
- Appendix: Algorithm Compendium
- References
- Index
31.5 BDE in Conjunction with FDE
It is obvious that BDE can also be implemented in conjunction with FDE by taking advantages provided by both BDE and FDE. Figure 31.6 depicts various combinations of BDE with FDE.
Figure 31.6 BDE in conjunction with FDE.
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