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Hyperspectral Data Processing : Algorithm Design and Analysis.
Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral sign...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chicester :
Wiley,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- HYPERSPECTRAL DATA PROCESSING: Algorithm Design and Analysis; CONTENTS; PREFACE; 1 OVERVIEWAND INTRODUCTION; 1.1 Overview; 1.2 Issues of Multispectral and Hyperspectral Imageries; 1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery; 1.3.1 Misconception: Hyperspectral Imaging is a Natural Extension of Multispectral Imaging; 1.3.2 Pigeon-Hole Principle: Natural Interpretation of Hyperspectral Imaging; 1.4 Scope of This Book; 1.5 Book's Organization; 1.5.1 Part I: Preliminaries; 1.5.2 Part II: Endmember Extraction; 1.5.3 Part III: Supervised Linear Hyperspectral Mixture Analysis.
- 1.5.4 Part IV: Unsupervised Hyperspectral Analysis1.5.5 Part V: Hyperspectral Information Compression; 1.5.6 Part VI: Hyperspectral Signal Coding; 1.5.7 Part VII: Hyperspectral Signal Feature Characterization; 1.5.8 Applications; 1.5.8.1 Chapter 30: Applications of Target Detection; 1.5.8.2 Chapter 31: Nonlinear Dimensionality Expansion to Multispectral Imagery; 1.5.8.3 Chapter 32: Multispectral Magnetic Resonance Imaging; 1.6 Laboratory Data to be Used in This Book; 1.6.1 Laboratory Data; 1.6.2 Cuprite Data; 1.6.3 NIST/EPA Gas-Phase Infrared Database.
- 1.7 Real Hyperspectral Images to be Used in this Book1.7.1 AVIRIS Data; 1.7.1.1 Cuprite Data; 1.7.1.2 Purdue's Indiana Indian Pine Test Site; 1.7.2 HYDICE Data; 1.8 Notations and Terminologies to be Used in this Book; I: PRELIMINARIES; 2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES; 2.1 Introduction; 2.2 Subsample Analysis; 2.2.1 Pure-Sample Target Detection; 2.2.2 Subsample Target Detection; 2.2.2.1 Adaptive Matched Detector (AMD); 2.2.2.2 Adaptive Subspace Detector (ASD); 2.2.3 Subsample Target Detection: Constrained Energy Minimization (CEM); 2.3 Mixed Sample Analysis.
- 2.3.1 Classification with Hard Decisions2.3.1.1 Fisher's Linear Discriminant Analysis (FLDA); 2.3.1.2 Support Vector Machines (SVM); 2.3.2 Classification with Soft Decisions; 2.3.2.1 Orthogonal Subspace Projection (OSP); 2.3.2.2 Target-Constrained Interference-Minimized Filter (TCIMF); 2.4 Kernel-Based Classification; 2.4.1 Kernel Trick Used in Kernel-Based Methods; 2.4.2 Kernel-Based Fisher's Linear Discriminant Analysis (KFLDA); 2.4.3 Kernel Support Vector Machine (K-SVM); 2.5 Conclusions; 3 THREE-DIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) ANALYSIS; 3.1 Introduction.
- 3.2 Neyman-Pearson Detection Problem Formulation3.3 ROC Analysis; 3.4 3D ROC Analysis; 3.5 Real Data-Based ROC Analysis; 3.5.1 How to Generate ROC Curves from Real Data; 3.5.2 How to Generate Gaussian-Fitted ROC Curves; 3.5.3 How to Generate 3D ROC Curves; 3.5.4 How to Generate 3D ROC Curves for Multiple Signal Detection and Classification; 3.6 Examples; 3.6.1 Hyperspectral Imaging; 3.6.1.1 Hyperspectral Target Detection; 3.6.1.2 Linear Hyperspectral Mixture Analysis; 3.6.2 Magnetic Resonance (MR) Breast Imaging; 3.6.2.1 Breast Tumor Detection; 3.6.2.2 Brain Tissue Classification.