Geschäftskunden Kundenprogramme
Vor Ort
Merkzettel Warenkorb
Produktbild: Hyperspectral Data Processing

Hyperspectral Data Processing Algorithm Design and Analysis

Fr. 293.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.04.2013

Verlag

John Wiley & Sons

Seitenzahl

1168

Maße (L/B/H)

25.7/18.5/5.8 cm

Gewicht

2064 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-471-69056-6

Beschreibung

Rezension

"I make a strong recommendation to anyone interested in hyperspectral image processing, and hyperspectral signal processing to make this book a common reference." ( Photogrammetric Engineering and Remote Sensing , 1 June 2015)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.04.2013

Verlag

John Wiley & Sons

Seitenzahl

1168

Maße (L/B/H)

25.7/18.5/5.8 cm

Gewicht

2064 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-471-69056-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

Kundinnen und Kunden meinen

0 Bewertungen

Informationen zu Bewertungen

Zur Abgabe einer Bewertung ist eine Anmeldung im Konto notwendig. Die Authentizität der Bewertungen wird von uns nicht überprüft. Wir behalten uns vor, Bewertungstexte, die unseren Richtlinien widersprechen, entsprechend zu kürzen oder zu löschen.

Die Bewertungen sind nach Format, Anzahl Sterne und Datum sortiert.

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kund*innen durch Ihre Meinung

Kundinnen und Kunden meinen

0 Bewertungen filtern
Die Leseprobe wird geladen.
  • Produktbild: Hyperspectral Data Processing
  • PREFACE xxiii

    1 OVERVIEWAND INTRODUCTION 1

    1.1 Overview 2

    1.2 Issues of Multispectral and Hyperspectral Imageries 3

    1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery 4

    1.4 Scope of This Book 7

    1.5 Book's Organization 10

    1.6 Laboratory Data to be Used in This Book 19

    1.7 Real Hyperspectral Images to be Used in this Book 20

    1.8 Notations and Terminologies to be Used in this Book 29

    I: PRELIMINARIES 31

    2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES 33

    2.1 Introduction 33

    2.2 Subsample Analysis 35

    2.3 Mixed Sample Analysis 45

    2.4 Kernel-Based Classification 57

    2.5 Conclusions 60

    3 THREE-DIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) ANALYSIS 63

    3.1 Introduction 63

    3.2 Neyman-Pearson Detection Problem Formulation 65

    3.3 ROC Analysis 67

    3.4 3D ROC Analysis 69

    3.5 Real Data-Based ROC Analysis 72

    3.6 Examples 78

    3.7 Conclusions 99

    4 DESIGN OF SYNTHETIC IMAGE EXPERIMENTS 101

    4.1 Introduction 102

    4.2 Simulation of Targets of Interest 103

    4.3 Six Scenarios of Synthetic Images 104

    4.4 Applications 112

    4.5 Conclusions 123

    5 VIRTUAL DIMENSIONALITY OF HYPERSPECTRAL DATA 124

    5.1 Introduction 124

    5.2 Reinterpretation of VD 126

    5.3 VD Determined by Data Characterization-Driven Criteria 126

    5.4 VD Determined by Data Representation-Driven Criteria 140

    5.5 Synthetic Image Experiments 144

    5.6 VD Estimated for Real Hyperspectral Images 155

    5.7 Conclusions 163

    6 DATA DIMENSIONALITY REDUCTION 168

    6.1 Introduction 168

    6.2 Dimensionality Reduction by Second-Order Statistics-Based Component Analysis Transforms 170

    6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis Transforms 179

    6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis Transforms 184

    6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis Transforms 190

    6.6 Dimensionality Reduction by Feature Extraction-Based Transforms 195

    6.7 Dimensionality Reduction by Band Selection 196

    6.8 Constrained Band Selection 197

    6.9 Conclusions 198

    II: ENDMEMBER EXTRACTION 201

    7 SIMULTANEOUS ENDMEMBER EXTRACTION ALGORITHMS (SM-EEAs) 207

    7.1 Introduction 208

    7.2 Convex Geometry-Based Endmember Extraction 209

    7.3 Second-Order Statistics-Based Endmember Extraction 228

    7.4 Automated Morphological Endmember Extraction (AMEE) 230

    7.5 Experiments 231

    7.6 Conclusions 239

    8 SEQUENTIAL ENDMEMBER EXTRACTION ALGORITHMS (SQ-EEAs) 241

    8.1 Introduction 241

    8.2 Successive N-FINDR (SC N-FINDR) 244

    8.3 Simplex Growing Algorithm (SGA) 244

    8.4 Vertex Component Analysis (VCA) 247

    8.5 Linear Spectral Mixture Analysis-Based SQ-EEAs 248

    8.6 High-Order Statistics-Based SQ-EEAS 252

    8.7 Experiments 254

    8.8 Conclusions 262

    9 INITIALIZATION-DRIVEN ENDMEMBER EXTRACTION ALGORITHMS (ID-EEAs) 265

    9.1 Introduction 265

    9.2 Initialization Issues 266

    9.3 Initialization-Driven EEAs 271

    9.4 Experiments 278

    9.5 Conclusions 283

    10 RANDOM ENDMEMBER EXTRACTION ALGORITHMS (REEAs) 287

    10.1 Introduction 287

    10.2 Random PPI (RPPI) 288

    10.3 Random VCA (RVCA) 290

    10.4 Random N-FINDR (RN-FINDR) 290

    10.5 Random SGA (RSGA) 292

    10.6 Random ICA-Based EEA (RICA-EEA) 292

    10.7 Synthetic Image Experiments 293

    10.8 Real Image Experiments 305

    10.9 Conclusions 313

    11 EXPLORATION ON RELATIONSHIPS AMONG ENDMEMBER EXTRACTION ALGORITHMS 316

    11.1 Introduction 316

    11.2 Orthogonal Projection-Based EEAs 318

    11.3 Comparative Study and Analysis Between SGA and VCA 330

    11.4 Does an Endmember Set Really Yield Maximum Simplex Volume? 339

    11.5 Impact of Dimensionality Reduction on EEAs 344

    11.6 Conclusions 348

    III: SUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS 351

    12 ORTHOGONAL SUBSPACE PROJECTION REVISITED 355

    12.1 Introduction 355

    12.2 Three Perspectives to Derive OSP 358

    12.3 Gaussian Noise in OSP 364

    12.4 OSP Implemented with Partial Knowledge 372

    12.5 OSP Implemented Without Knowledge 383

    12.6 Conclusions 390

    13 FISHER'S LINEAR SPECTRAL MIXTURE ANALYSIS 391

    13.1 Introduction 391

    13.2 Feature Vector-Constrained FLSMA (FVC-FLSMA) 392

    13.3 Relationship Between FVC-FLSMA and LCMV, TCIMF, and CEM 395

    13.4 Relationship Between FVC-FLSMA and OSP 396

    13.5 Relationship Between FVC-FLSMA and LCDA 396

    13.6 Abundance-Constrained Least Squares FLDA (ACLS-FLDA) 397

    13.7 Synthetic Image Experiments 398

    13.8 Real Image Experiments 402

    13.9 Conclusions 409

    14 WEIGHTED ABUNDANCE-CONSTRAINED LINEAR SPECTRAL MIXTURE ANALYSIS 411

    14.1 Introduction 411

    14.2 Abundance-Constrained LSMA (AC-LSMA) 413

    14.3 Weighted Least-Squares Abundance-Constrained LSMA 413

    14.4 Synthetic Image-Based Computer Simulations 419

    14.5 Real Image Experiments 426

    14.6 Conclusions 432

    15 KERNEL-BASED LINEAR SPECTRAL MIXTURE ANALYSIS 434

    15.1 Introduction 434

    15.2 Kernel-Based LSMA (KLSMA) 436

    15.3 Synthetic Image Experiments 441

    15.4 AVIRIS Data Experiments 444

    15.5 HYDICE Data Experiments 460

    15.6 Conclusions 462

    IV: UNSUPERVISED HYPERSPECTRAL IMAGE ANALYSIS 465

    16 HYPERSPECTRAL MEASURES 469

    16.1 Introduction 469

    16.2 Signature Vector-Based Hyperspectral Measures for Target Discrimanition and Identification 470

    16.3 Correlation-Weighted Hyperspectral Measures for Target Discrimanition and Identification 472

    16.4 Experiments 477

    16.5 Conclusions 482

    17 UNSUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS 483

    17.1 Introduction 483

    17.2 Least Squares-Based ULSMA 486

    17.3 Component Analysis-Based ULSMA 488

    17.4 Synthetic Image Experiments 490

    17.5 Real-Image Experiments 503

    17.6 ULSMAVersus Endmember Extraction 517

    17.7 Conclusions 524

    18 PIXEL EXTRACTION AND INFORMATION 526

    18.1 Introduction 526

    18.2 Four Types of Pixels 527

    18.3 Algorithms Selected to Extract Pixel Information 528

    18.4 Pixel Information Analysis via Synthetic Images 528

    18.5 Real Image Experiments 534

    18.6 Conclusions 539

    V: HYPERSPECTRAL INFORMATION COMPRESSION 541

    19 EXPLOITATION-BASED HYPERSPECTRAL DATA COMPRESSION 545

    19.1 Introduction 545

    19.2 Hyperspectral Information Compression Systems 547

    19.3 Spectral/Spatial Compression 549

    19.4 Progressive Spectral/Spatial Compression 557

    19.5 3D Compression 557

    19.6 Exploration-Based Applications 559

    19.7 Experiments 561

    19.8 Conclusions 580

    20 PROGRESSIVE SPECTRAL DIMENSIONALITY PROCESS 581

    20.1 Introduction 582

    20.2 Dimensionality Prioritization 584

    20.3 Representation of Transformed Components for DP 585

    20.4 Progressive Spectral Dimensionality Process 589

    20.5 Hyperspectral Compression by PSDP 597

    20.6 Experiments for PSDP 598

    20.7 Conclusions 608

    21 PROGRESSIVE BAND DIMENSIONALITY PROCESS 613

    21.1 Introduction 614

    21.2 Band Prioritization 615

    21.3 Criteria for Band Prioritization 617

    21.4 Experiments for BP 624

    21.5 Progressive Band Dimensionality Process 651

    21.6 Hyperspectral Compresssion by PBDP 653

    21.7 Experiments for PBDP 656

    21.8 Conclusions 662

    22 DYNAMIC DIMENSIONALITYALLOCATION 664

    22.1 Introduction 664

    22.2 Dynamic Dimensionality Allocaction 665

    22.3 Signature Discriminatory Probabilties 667

    22.4 Coding Techniques for Determining DDA 667

    22.5 Experiments for Dynamic Dimensionality Allocation 669

    22.6 Conclusions 682

    23 PROGRESSIVE BAND SELECTION 683

    23.1 Introduction 683

    23.2 Band De-Corrleation 684

    23.3 Progressive Band Selection 686

    23.4 Experiments for Progressive Band Selection 688

    23.5 Endmember Extraction 688

    23.6 Land Cover/Use Classification 690

    23.7 Linear Spectral Mixture Analysis 694

    23.8 Conclusions 715

    VI: HYPERSPECTRAL SIGNAL CODING 717

    24 BINARY CODING FOR SPECTRAL SIGNATURES 719

    24.1 Introduction 719

    24.2 Binary Coding 720

    24.3 Spectral Feature-Based Coding 723

    24.4 Experiments 725

    24.5 Conclusions 740

    25 VECTOR CODING FOR HYPERSPECTRAL SIGNATURES 741

    25.1 Introduction 741

    25.2 Spectral Derivative Feature Coding 743

    25.3 Spectral Feature Probabilistic Coding 755

    25.4 Real Image Experiments 764

    25.5 Conclusions 771

    26 PROGRESSIVE CODING FOR SPECTRAL SIGNATURES 772

    26.1 Introduction 772

    26.2 Multistage Pulse Code Modulation 774

    26.3 MPCM-Based Progressive Spectral Signature Coding 783

    26.4 NIST-GAS Data Experiments 786

    26.5 Real Image Hyperspectral Experiments 790

    26.6 Conclusions 796

    VII: HYPERSPECTRAL SIGNAL CHARACTERIZATION 797

    27 VARIABLE-NUMBERVARIABLE-BAND SELECTION FOR HYPERSPECTRAL SIGNALS 799

    27.1 Introduction 799

    27.2 Orthogonal Subspace Projection-Based Band Prioritization Criterion 801

    27.3 Variable-Number Variable-Band Selection 803

    27.4 Experiments 806

    27.5 Selection of Reference Signatures 819

    27.6 Conclusions 819

    28 KALMAN FILTER-BASED ESTIMATION FOR HYPERSPECTRAL SIGNALS 820

    28.1 Introduction 820

    28.2 Kalman Filter-Based Linear Unmixing 822

    28.3 Kalman Filter-Based Spectral Characterization Signal-Processing Techniques 824

    28.4 Computer Simulations Using AVIRIS Data 831

    28.5 Computer Simulations Using NIST-Gas Data 843

    28.6 Real Data Experiments 852

    28.7 Conclusions 857

    29 WAVELET REPRESENTATION FOR HYPERSPECTRAL SIGNALS 859

    29.1 Introduction 859

    29.2 Wavelet Analysis 860

    29.2.1 Multiscale Approximation 860

    29.2.2 Scaling Function 861

    29.2.3 Wavelet Function 862

    29.3 Wavelet-Based Signature Characterization Algorithm 863

    29.4 Synthetic Image-Based Computer Simulations 868

    29.5 Real Image Experiments 871

    29.6 Conclusions 875

    VIII: APPLICATIONS 877

    30 APPLICATIONS OF TARGET DETECTION 879

    30.1 Introduction 879

    30.2 Size Estimation of Subpixel Targets 880

    30.3 Experiments 881

    30.4 Concealed Target Detection 891

    30.5 Computer-Aided Detection and Classification Algorithm for Concealed Targets 892

    30.6 Experiments for Concealed Target Detection 893

    30.7 Conclusions 895

    31 NONLINEAR DIMENSIONALITY EXPANSION TO MULTISPECTRAL IMAGERY 897

    31.1 Introduction 897

    31.2 Band Dimensionality Expansion 899

    31.3 Hyperspectral Imaging Techniques Expanded by BDE 902

    31.4 Feature Dimensionality Expansion by Nonlinear Kernels 904

    31.5 BDE in Conjunction with FDE 909

    31.6 Multispectral Image Experiments 909

    31.7 Conclusion 918

    32 MULTISPECTRAL MAGNETIC RESONANCE IMAGING 920

    32.1 Introduction 920

    32.2 Linear Spectral Mixture Analysis for MRI 923

    32.3 Linear Spectral Random Mixture Analysis for MRI 928

    32.4 Kernel-Based Linear Spectral Mixture Analysis 933

    32.5 Synthetic MR Brain Image Experiments 933

    32.6 Real MR Brain Image Experiments 951

    32.7 Conclusions 955

    33 CONCLUSIONS 956

    33.1 Design Principles for Nonliteral Hyperspectral Imaging Techniques 956

    33.2 Endmember Extraction 964

    33.3 Linear Spectral Mixture Analysis 970

    33.4 Anomaly Detection 974

    33.5 Support Vector Machines and Kernel-Based Approaches 977

    33.6 Hyperspectral Compression 981

    33.7 Hyperspectral Signal Processing 984

    33.8 Applications 987

    33.9 Further Topics 987

    GLOSSARY 993

    APPENDIX: ALGORITHM COMPENDIUM 997

    REFERENCES 1052

    INDEX 1071