{"product_id":"hyperspectral-imaging-algorithm-design-and-analysis-9780471690566","title":"Hyperspectral Imaging  Algorithm  Design and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHyperspectral 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“I make a strong recommendation to anyone interested in hyperspectral image processing, and hyperspectral signal processing to make this book a common reference.”  (\u003ci\u003ePhotogrammetric Engineering and Remote Sensing\u003c\/i\u003e, 1 June 2015)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview and Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Overview 2\u003c\/p\u003e \u003cp\u003e1.2 Issues of Multispectral and Hyperspectral Imageries 3\u003c\/p\u003e \u003cp\u003e1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery 4\u003c\/p\u003e \u003cp\u003e1.4 Scope of This Book 7\u003c\/p\u003e \u003cp\u003e1.5 Book’s Organization 10\u003c\/p\u003e \u003cp\u003e1.6 Laboratory Data to be Used in This Book 19\u003c\/p\u003e \u003cp\u003e1.7 Real Hyperspectral Images to be Used in this Book 20\u003c\/p\u003e \u003cp\u003e1.8 Notations and Terminologies to be Used in this Book 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI: Preliminaries 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Fundamentals of Subsample and Mixed Sample Analyses 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 Subsample Analysis 35\u003c\/p\u003e \u003cp\u003e2.3 Mixed Sample Analysis 45\u003c\/p\u003e \u003cp\u003e2.4 Kernel-Based Classification 57\u003c\/p\u003e \u003cp\u003e2.5 Conclusions 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Three-dimensional Receiver Operating Characteristics (3d Roc) Analysis 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 63\u003c\/p\u003e \u003cp\u003e3.2 Neyman–Pearson Detection Problem Formulation 65\u003c\/p\u003e \u003cp\u003e3.3 ROC Analysis 67\u003c\/p\u003e \u003cp\u003e3.4 3D ROC Analysis 69\u003c\/p\u003e \u003cp\u003e3.5 Real Data-Based ROC Analysis 72\u003c\/p\u003e \u003cp\u003e3.6 Examples 78\u003c\/p\u003e \u003cp\u003e3.7 Conclusions 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Design of Synthetic Image Experiments 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 102\u003c\/p\u003e \u003cp\u003e4.2 Simulation of Targets of Interest 103\u003c\/p\u003e \u003cp\u003e4.3 Six Scenarios of Synthetic Images 104\u003c\/p\u003e \u003cp\u003e4.4 Applications 112\u003c\/p\u003e \u003cp\u003e4.5 Conclusions 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Virtual Dimensionality of Hyperspectral Data 124\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 124\u003c\/p\u003e \u003cp\u003e5.2 Reinterpretation of VD 126\u003c\/p\u003e \u003cp\u003e5.3 VD Determined by Data Characterization-Driven Criteria 126\u003c\/p\u003e \u003cp\u003e5.4 VD Determined by Data Representation-Driven Criteria 140\u003c\/p\u003e \u003cp\u003e5.5 Synthetic Image Experiments 144\u003c\/p\u003e \u003cp\u003e5.6 VD Estimated for Real Hyperspectral Images 155\u003c\/p\u003e \u003cp\u003e5.7 Conclusions 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Data Dimensionality Reduction 168\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 168\u003c\/p\u003e \u003cp\u003e6.2 Dimensionality Reduction by Second-Order Statistics-Based Component Analysis Transforms 170\u003c\/p\u003e \u003cp\u003e6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis Transforms 179\u003c\/p\u003e \u003cp\u003e6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis Transforms 184\u003c\/p\u003e \u003cp\u003e6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis Transforms 190\u003c\/p\u003e \u003cp\u003e6.6 Dimensionality Reduction by Feature Extraction-Based Transforms 195\u003c\/p\u003e \u003cp\u003e6.7 Dimensionality Reduction by Band Selection 196\u003c\/p\u003e \u003cp\u003e6.8 Constrained Band Selection 197\u003c\/p\u003e \u003cp\u003e6.9 Conclusions 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII: Endmember Extraction 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Simultaneous Endmember Extraction Algorithms (SM-EEAs) 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 208\u003c\/p\u003e \u003cp\u003e7.2 Convex Geometry-Based Endmember Extraction 209\u003c\/p\u003e \u003cp\u003e7.3 Second-Order Statistics-Based Endmember Extraction 228\u003c\/p\u003e \u003cp\u003e7.4 Automated Morphological Endmember Extraction (AMEE) 230\u003c\/p\u003e \u003cp\u003e7.5 Experiments 231\u003c\/p\u003e \u003cp\u003e7.6 Conclusions 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Sequential Endmember Extraction Algorithms (SQ-EEAs) 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 241\u003c\/p\u003e \u003cp\u003e8.2 Successive N-FINDR (SC N-FINDR) 244\u003c\/p\u003e \u003cp\u003e8.3 Simplex Growing Algorithm (SGA) 244\u003c\/p\u003e \u003cp\u003e8.4 Vertex Component Analysis (VCA) 247\u003c\/p\u003e \u003cp\u003e8.5 Linear Spectral Mixture Analysis-Based SQ-EEAs 248\u003c\/p\u003e \u003cp\u003e8.6 High-Order Statistics-Based SQ-EEAS 252\u003c\/p\u003e \u003cp\u003e8.7 Experiments 254\u003c\/p\u003e \u003cp\u003e8.8 Conclusions 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Initialization-driven Endmember Extraction Algorithms (ID-EEAs) 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 265\u003c\/p\u003e \u003cp\u003e9.2 Initialization Issues 266\u003c\/p\u003e \u003cp\u003e9.3 Initialization-Driven EEAs 271\u003c\/p\u003e \u003cp\u003e9.4 Experiments 278\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Random Endmember Extraction Algorithms (REEAs) 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 287\u003c\/p\u003e \u003cp\u003e10.2 Random PPI (RPPI) 288\u003c\/p\u003e \u003cp\u003e10.3 Random VCA (RVCA) 290\u003c\/p\u003e \u003cp\u003e10.4 Random N-FINDR (RN-FINDR) 290\u003c\/p\u003e \u003cp\u003e10.5 Random SGA (RSGA) 292\u003c\/p\u003e \u003cp\u003e10.6 Random ICA-Based EEA (RICA-EEA) 292\u003c\/p\u003e \u003cp\u003e10.7 Synthetic Image Experiments 293\u003c\/p\u003e \u003cp\u003e10.8 Real Image Experiments 305\u003c\/p\u003e \u003cp\u003e10.9 Conclusions 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Exploration on Relationships Among Endmember Extraction Algorithms 316\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 316\u003c\/p\u003e \u003cp\u003e11.2 Orthogonal Projection-Based EEAs 318\u003c\/p\u003e \u003cp\u003e11.3 Comparative Study and Analysis Between SGA and VCA 330\u003c\/p\u003e \u003cp\u003e11.4 Does an Endmember Set Really Yield Maximum Simplex Volume? 339\u003c\/p\u003e \u003cp\u003e11.5 Impact of Dimensionality Reduction on EEAs 344\u003c\/p\u003e \u003cp\u003e11.6 Conclusions 348\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII: Supervised Linear Hyperspectral Mixture Analysis 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Orthogonal Subspace Projection Revisited 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 355\u003c\/p\u003e \u003cp\u003e12.2 Three Perspectives to Derive OSP 358\u003c\/p\u003e \u003cp\u003e12.3 Gaussian Noise in OSP 364\u003c\/p\u003e \u003cp\u003e12.4 OSP Implemented with Partial Knowledge 372\u003c\/p\u003e \u003cp\u003e12.5 OSP Implemented Without Knowledge 383\u003c\/p\u003e \u003cp\u003e12.6 Conclusions 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Fisher’s Linear Spectral Mixture Analysis 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 391\u003c\/p\u003e \u003cp\u003e13.2 Feature Vector-Constrained FLSMA (FVC-FLSMA) 392\u003c\/p\u003e \u003cp\u003e13.3 Relationship Between FVC-FLSMA and LCMV, TCIMF, and CEM 395\u003c\/p\u003e \u003cp\u003e13.4 Relationship Between FVC-FLSMA and OSP 396\u003c\/p\u003e \u003cp\u003e13.5 Relationship Between FVC-FLSMA and LCDA 396\u003c\/p\u003e \u003cp\u003e13.6 Abundance-Constrained Least Squares FLDA (ACLS-FLDA) 397\u003c\/p\u003e \u003cp\u003e13.7 Synthetic Image Experiments 398\u003c\/p\u003e \u003cp\u003e13.8 Real Image Experiments 402\u003c\/p\u003e \u003cp\u003e13.9 Conclusions 409\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Weighted Abundance-constrained Linear Spectral Mixture Analysis 411\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 411\u003c\/p\u003e \u003cp\u003e14.2 Abundance-Constrained LSMA (AC-LSMA) 413\u003c\/p\u003e \u003cp\u003e14.3 Weighted Least-Squares Abundance-Constrained LSMA 413\u003c\/p\u003e \u003cp\u003e14.4 Synthetic Image-Based Computer Simulations 419\u003c\/p\u003e \u003cp\u003e14.5 Real Image Experiments 426\u003c\/p\u003e \u003cp\u003e14.6 Conclusions 432\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Kernel-based Linear Spectral Mixture Analysis 434\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 434\u003c\/p\u003e \u003cp\u003e15.2 Kernel-Based LSMA (KLSMA) 436\u003c\/p\u003e \u003cp\u003e15.3 Synthetic Image Experiments 441\u003c\/p\u003e \u003cp\u003e15.4 AVIRIS Data Experiments 444\u003c\/p\u003e \u003cp\u003e15.5 HYDICE Data Experiments 460\u003c\/p\u003e \u003cp\u003e15.6 Conclusions 462\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV: Unsupervised Hyperspectral Image Analysis 465\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Hyperspectral Measures 469\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 469\u003c\/p\u003e \u003cp\u003e16.2 Signature Vector-Based Hyperspectral Measures for Target Discrimanition and Identification 470\u003c\/p\u003e \u003cp\u003e16.3 Correlation-Weighted Hyperspectral Measures for Target Discrimanition and Identification 472\u003c\/p\u003e \u003cp\u003e16.4 Experiments 477\u003c\/p\u003e \u003cp\u003e16.5 Conclusions 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Unsupervised Linear Hyperspectral Mixture Analysis 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 483\u003c\/p\u003e \u003cp\u003e17.2 Least Squares-Based ULSMA 486\u003c\/p\u003e \u003cp\u003e17.3 Component Analysis-Based ULSMA 488\u003c\/p\u003e \u003cp\u003e17.4 Synthetic Image Experiments 490\u003c\/p\u003e \u003cp\u003e17.5 Real-Image Experiments 503\u003c\/p\u003e \u003cp\u003e17.6 ULSMA Versus Endmember Extraction 517\u003c\/p\u003e \u003cp\u003e17.7 Conclusions 524\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Pixel Extraction and Information 526\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 526\u003c\/p\u003e \u003cp\u003e18.2 Four Types of Pixels 527\u003c\/p\u003e \u003cp\u003e18.3 Algorithms Selected to Extract Pixel Information 528\u003c\/p\u003e \u003cp\u003e18.4 Pixel Information Analysis via Synthetic Images 528\u003c\/p\u003e \u003cp\u003e18.5 Real Image Experiments 534\u003c\/p\u003e \u003cp\u003e18.6 Conclusions 539\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV: Hyperspectral Information Compression 541\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Exploitation-based Hyperspectral Data Compression 545\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 545\u003c\/p\u003e \u003cp\u003e19.2 Hyperspectral Information Compression Systems 547\u003c\/p\u003e \u003cp\u003e19.3 Spectral\/Spatial Compression 549\u003c\/p\u003e \u003cp\u003e19.4 Progressive Spectral\/Spatial Compression 557\u003c\/p\u003e \u003cp\u003e19.5 3D Compression 557\u003c\/p\u003e \u003cp\u003e19.6 Exploration-Based Applications 559\u003c\/p\u003e \u003cp\u003e19.7 Experiments 561\u003c\/p\u003e \u003cp\u003e19.8 Conclusions 580\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Progressive Spectral Dimensionality Process 581\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 582\u003c\/p\u003e \u003cp\u003e20.2 Dimensionality Prioritization 584\u003c\/p\u003e \u003cp\u003e20.3 Representation of Transformed Components for DP 585\u003c\/p\u003e \u003cp\u003e20.4 Progressive Spectral Dimensionality Process 589\u003c\/p\u003e \u003cp\u003e20.5 Hyperspectral Compression by PSDP 597\u003c\/p\u003e \u003cp\u003e20.6 Experiments for PSDP 598\u003c\/p\u003e \u003cp\u003e20.7 Conclusions 608\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Progressive Band Dimensionality Process 613\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 614\u003c\/p\u003e \u003cp\u003e21.2 Band Prioritization 615\u003c\/p\u003e \u003cp\u003e21.3 Criteria for Band Prioritization 617\u003c\/p\u003e \u003cp\u003e21.4 Experiments for BP 624\u003c\/p\u003e \u003cp\u003e21.5 Progressive Band Dimensionality Process 651\u003c\/p\u003e \u003cp\u003e21.6 Hyperspectral Compresssion by PBDP 653\u003c\/p\u003e \u003cp\u003e21.7 Experiments for PBDP 656\u003c\/p\u003e \u003cp\u003e21.8 Conclusions 662\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Dynamic Dimensionality Allocation 664\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 664\u003c\/p\u003e \u003cp\u003e22.2 Dynamic Dimensionality Allocaction 665\u003c\/p\u003e \u003cp\u003e22.3 Signature Discriminatory Probabilties 667\u003c\/p\u003e \u003cp\u003e22.4 Coding Techniques for Determining DDA 667\u003c\/p\u003e \u003cp\u003e22.5 Experiments for Dynamic Dimensionality Allocation 669\u003c\/p\u003e \u003cp\u003e22.6 Conclusions 682\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Progressive Band Selection 683\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 683\u003c\/p\u003e \u003cp\u003e23.2 Band De-Corrleation 684\u003c\/p\u003e \u003cp\u003e23.3 Progressive Band Selection 686\u003c\/p\u003e \u003cp\u003e23.4 Experiments for Progressive Band Selection 688\u003c\/p\u003e \u003cp\u003e23.5 Endmember Extraction 688\u003c\/p\u003e \u003cp\u003e23.6 Land Cover\/Use Classification 690\u003c\/p\u003e \u003cp\u003e23.7 Linear Spectral Mixture Analysis 694\u003c\/p\u003e \u003cp\u003e23.8 Conclusions 715\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI: Hyperspectral Signal Coding 717\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Binary Coding for Spectral Signatures 719\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 719\u003c\/p\u003e \u003cp\u003e24.2 Binary Coding 720\u003c\/p\u003e \u003cp\u003e24.3 Spectral Feature-Based Coding 723\u003c\/p\u003e \u003cp\u003e24.4 Experiments 725\u003c\/p\u003e \u003cp\u003e24.5 Conclusions 740\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Vector Coding for Hyperspectral Signatures 741\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 741\u003c\/p\u003e \u003cp\u003e25.2 Spectral Derivative Feature Coding 743\u003c\/p\u003e \u003cp\u003e25.3 Spectral Feature Probabilistic Coding 755\u003c\/p\u003e \u003cp\u003e25.4 Real Image Experiments 764\u003c\/p\u003e \u003cp\u003e25.5 Conclusions 771\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Progressive Coding for Spectral Signatures 772\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 772\u003c\/p\u003e \u003cp\u003e26.2 Multistage Pulse Code Modulation 774\u003c\/p\u003e \u003cp\u003e26.3 MPCM-Based Progressive Spectral Signature Coding 783\u003c\/p\u003e \u003cp\u003e26.4 NIST-GAS Data Experiments 786\u003c\/p\u003e \u003cp\u003e26.5 Real Image Hyperspectral Experiments 790\u003c\/p\u003e \u003cp\u003e26.6 Conclusions 796\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVII: Hyperspectral Signal Characterization 797\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Variable-number Variable-band Selection for Hyperspectral Signals 799\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 799\u003c\/p\u003e \u003cp\u003e27.2 Orthogonal Subspace Projection-Based Band Prioritization Criterion 801\u003c\/p\u003e \u003cp\u003e27.3 Variable-Number Variable-Band Selection 803\u003c\/p\u003e \u003cp\u003e27.4 Experiments 806\u003c\/p\u003e \u003cp\u003e27.5 Selection of Reference Signatures 819\u003c\/p\u003e \u003cp\u003e27.6 Conclusions 819\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Kalman Filter-based Estimation for Hyperspectral Signals 820\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 820\u003c\/p\u003e \u003cp\u003e28.2 Kalman Filter-Based Linear Unmixing 822\u003c\/p\u003e \u003cp\u003e28.3 Kalman Filter-Based Spectral Characterization Signal-Processing Techniques 824\u003c\/p\u003e \u003cp\u003e28.4 Computer Simulations Using AVIRIS Data 831\u003c\/p\u003e \u003cp\u003e28.5 Computer Simulations Using NIST-Gas Data 843\u003c\/p\u003e \u003cp\u003e28.6 Real Data Experiments 852\u003c\/p\u003e \u003cp\u003e28.7 Conclusions 857\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Wavelet Representation for Hyperspectral Signals 859\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 859\u003c\/p\u003e \u003cp\u003e29.2 Wavelet Analysis 860\u003c\/p\u003e \u003cp\u003e29.3 Wavelet-Based Signature Characterization Algorithm 863\u003c\/p\u003e \u003cp\u003e29.4 Synthetic Image-Based Computer Simulations 868\u003c\/p\u003e \u003cp\u003e29.5 Real Image Experiments 871\u003c\/p\u003e \u003cp\u003e29.6 Conclusions 875\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVIII: Applications 877\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Applications of Target Detection 879\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 879\u003c\/p\u003e \u003cp\u003e30.2 Size Estimation of Subpixel Targets 880\u003c\/p\u003e \u003cp\u003e30.3 Experiments 881\u003c\/p\u003e \u003cp\u003e30.4 Concealed Target Detection 891\u003c\/p\u003e \u003cp\u003e30.5 Computer-Aided Detection and Classification Algorithm for Concealed Targets 892\u003c\/p\u003e \u003cp\u003e30.6 Experiments for Concealed Target Detection 893\u003c\/p\u003e \u003cp\u003e30.7 Conclusions 895\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Nonlinear Dimensionality Expansion to Multispectral Imagery 897\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 897\u003c\/p\u003e \u003cp\u003e31.2 Band Dimensionality Expansion 899\u003c\/p\u003e \u003cp\u003e31.3 Hyperspectral Imaging Techniques Expanded by BDE 902\u003c\/p\u003e \u003cp\u003e31.4 Feature Dimensionality Expansion by Nonlinear Kernels 904\u003c\/p\u003e \u003cp\u003e31.5 BDE in Conjunction with FDE 909\u003c\/p\u003e \u003cp\u003e31.6 Multispectral Image Experiments 909\u003c\/p\u003e \u003cp\u003e31.7 Conclusion 918\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 Multispectral Magnetic Resonance Imaging 920\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e32.1 Introduction 920\u003c\/p\u003e \u003cp\u003e32.2 Linear Spectral Mixture Analysis for MRI 923\u003c\/p\u003e \u003cp\u003e32.3 Linear Spectral Random Mixture Analysis for MRI 928\u003c\/p\u003e \u003cp\u003e32.4 Kernel-Based Linear Spectral Mixture Analysis 933\u003c\/p\u003e \u003cp\u003e32.5 Synthetic MR Brain Image Experiments 933\u003c\/p\u003e \u003cp\u003e32.6 Real MR Brain Image Experiments 951\u003c\/p\u003e \u003cp\u003e32.7 Conclusions 955\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 Conclusions 956\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e33.1 Design Principles for Nonliteral Hyperspectral Imaging Techniques 956\u003c\/p\u003e \u003cp\u003e33.2 Endmember Extraction 964\u003c\/p\u003e \u003cp\u003e33.3 Linear Spectral Mixture Analysis 970\u003c\/p\u003e \u003cp\u003e33.4 Anomaly Detection 974\u003c\/p\u003e \u003cp\u003e33.5 Support Vector Machines and Kernel-Based Approaches 977\u003c\/p\u003e \u003cp\u003e33.6 Hyperspectral Compression 981\u003c\/p\u003e \u003cp\u003e33.7 Hyperspectral Signal Processing 984\u003c\/p\u003e \u003cp\u003e33.8 Applications 987\u003c\/p\u003e \u003cp\u003e33.9 Further Topics 987\u003c\/p\u003e \u003cp\u003eGlossary 993\u003c\/p\u003e \u003cp\u003eAppendix: Algorithm Compendium 997\u003c\/p\u003e \u003cp\u003eReferences 1052\u003c\/p\u003e \u003cp\u003eIndex 1071\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49525406990679,"sku":"9780471690566","price":166.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471690566.jpg?v=1731860397","url":"https:\/\/bookcurl.com\/products\/hyperspectral-imaging-algorithm-design-and-analysis-9780471690566","provider":"Book Curl","version":"1.0","type":"link"}