Advances in Hyperspectral Image Processing Techniques (IEEE Press)

Description

Advances in Hyperspectral Image Processing Techniques Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications Advances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years. The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields. The book’s content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification. Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include: Two fundamental principles of hyperspectral imagingConstrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classificationRestricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domainHyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional informationAdvances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysisSparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification With many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field. Cover Title Page Copyright Page Contents Editor Biography List of Contributors Preface Part I General Theory Chapter 1 Introduction: Two Fundamental Principles Behind Hyperspectral Imaging 1.1 Introduction 1.2 Why Is Hyperspectral Imaging? 1.3 Two Principles for Hyperspectral Imaging 1.3.1 Pigeon-Hole Principle 1.3.2 Orthogonality Principle 1.4 What Are the Issues of Hyperspectral Imaging? 1.5 Determination of p by Virtual Dimensionality via Pigeon-Hole Principle 1.6 Order Determination of Low Rank and Sparse Matrices by Virtual Dimensionality via Pigeon-Hole Principle 1.7 Band Selection by Pigeon-Hole Principle 1.8 Band Selection by a Hyperspectral Band Channel via Pigeon-Hole Principle 1.9 Band Sampling via Pigeon-Hole Principle 1.10 Spectral Unmixing via Orthogonality Principle 1.11 Target Detection by Orthogonality Principle 1.11.1 ATGP 1.11.1.1 Automatic Target Generation Process (ATGP) 1.11.2 Constrained Energy Minimization (CEM) 1.12 Anomaly Detection by Orthogonality Principle 1.13 Endmember Finding by Orthogonality Principle 1.13.1 Pixel Purity Index (PPI) 1.13.2 Vertex Component Analysis (VCA) 1.13.3 Simplex Growing Algorithm (SGA) 1.14 Low Rank and Sparse Representation by OSP via Orthogonality Principle 1.15 Hyperspectral Classification 1.15.1 Hyperspectral Mixed Pixel Classification (HMPC) 1.15.2 Number of Sampled Bands Lower Than Number of Classes 1.15.3 Potential and Promise of Band Sampling in HMPC 1.16 Conclusion References Chapter 2 Overview of Hyperspectral Imaging Remote Sensing from Satellites 2.1 Hyperspectral Imaging Remote Sensing from Airplanes to Satellites 2.1.1 History of Development of Airborne Hyperspectral Imagers 2.1.2 Early Development of Spaceborne Hyperspectral Imagers 2.2 Development of Spaceborne Hyperspectral Imagers in the Last Two Decades 2.2.1 Survey of Spaceborne Hyperspectral Imagers Acronyms List 2.2.2 Brief Description of Spaceborne Hyperspectral Imagers 2.2.2.1 Visible Imagers and Spectrographic Imagers (UVISI) Onboard the MSX Satellite 2.2.2.2 HyperSpectral Imager (HSI) for the LEWIS Mission 2.2.2.3 MODIS Onboard Terra and Aqua Satellites 2.2.2.4 Hyperion Onboard NASA's EO-1 Satellite 2.2.2.5 CHRIS Onboard ESA's PROBA Satellite 2.2.2.6 MERIS Onboard ESA's ENVISAT Satellite 2.2.2.7 VIRTIS for ESA's Rosetta, Venus-Express, and NASA-Dawn Planetary Missions 2.2.2.8 CRISM Aboard Mars Reconnaissance Orbiter 2.2.2.9 Moon Mineralogy Mapper for Mapping Lunar Surface 2.2.2.10 Fourier Transform Hyperspectral Imager Onboard Chinese Environment Satellite 2.2.2.11 HySI Onboard Indian Mini Satellite-1 2.2.2.12 ARTEMIS Onboard TacSat-3 2.2.2.13 HICO Onboard the International Space Station 2.2.2.14 Visible and Near-infrared Imaging Spectrometer Aboard Chang'E 3 Spacecraft 2.2.2.15 Ocean and Land Color Imager (OLCI) on Sentinel-3A 2.2.2.16 Miniature High-Resolution Imaging Spectrometer on GHGSat-D 2.2.2.17 Aalto-1 Spectral Imager .(AaSI) on a 3U Nanosatellite 2.2.2.18 DLR Earth Sensing Imaging Spectrometer on the International Space Station 2.2.2.19 HyperScout Hyperspectral Camera on ESA's Nanosatellite GomX-4B 2.2.2.20 Advanced Hyperspectral Imager (AHSI) on Chinese Gaofen-5 Satellite 2.2.2.21 Italian Hyperspectral Satellite PRISMA 2.2.2.22 Hyperspectral Imager Suite Onboard the International Space Station 2.2.2.23 German Spaceborne Hyperspectral Imager EnMAP 2.2.2.24 ESA's Moons and Jupiter Imaging Spectrometer (MAJIS) 2.3 Conclusion References Chapter 3 Efficient Hardware Implementation for Hyperspectral Anomaly and Target Detection 3.1 Introduction 3.2 Hyperspectral Anomaly and Target Detection 3.2.1 DPBS-CEM 3.2.2 DBN-RXD 3.2.3 Fast-ATGP 3.2.4 Fast-MGD 3.3 Model-Based Design 3.3.1 What is Model-Based Design? 3.3.2 FPGA Development Based on MBD 3.3.3 Examples of IP Design Based on HLS 3.3.3.1 Efficient Off-Chip Storage Access IP 3.3.3.2 Parallel Matrix Multiplication IP 3.3.3.3 Matrix Dot-Product-Plus IP 3.3.3.4 Erosion/Dilation IP 3.4 System Integration Framework Design 3.4.1 Efficient FPGA Implementation 3.4.1.1 FPGA Implementation of DPBS-CEM 3.4.1.2 FPGA Implementation of DBN-RXD 3.4.1.3 FPGA Implementation of Fast-ATGP 3.4.1.4 FPGA Implementation of Fast-MGD 3.5 Experiments and Discussions 3.5.1 Hyperspectral Image Data Set 3.5.1.1 TE1 Data Set 3.5.1.2 HyMap Data Set 3.5.1.3 Airport-Beach-Urban. .(ABU) Data Set 3.5.1.4 Cuprite Data Set 3.5.1.5 San Diego Data Set 3.5.1.6 HYDICE Data Set 3.5.2 Experiments of DPBS-CEM 3.5.2.1 Detection Accuracy 3.5.2.2 Acceleration Performance 3.5.3 Experiments of DBN-RXD 3.5.3.1 Detection Accuracy 3.5.3.2 Acceleration Performance 3.5.4 Experiments of Fast-ATGP 3.5.4.1 Detection Accuracy 3.5.4.2 Results for the AVIRIS Cuprite Scene 3.5.5 Experiments of Fast-MGD 3.5.5.1 Detection Accuracy 3.5.5.2 Performance Evaluation 3.6 Conclusion References Part II Band Selection for Hyperspectral Imaging Chapter 4 Constrained Band Selection for Hyperspectral Imaging 4.1 Introduction 4.2 Constrained BS 4.2.1 Band Vector-Constrained BS 4.2.1.1 Band Correlation Minimization (BCM) 4.2.1.2 Band Dependence Minimization (BDM) 4.2.1.3 Band Correlation Constraint (BCC) 4.2.1.4 Band Dependence Constraint (BDC) 4.2.2 Band Image-Constrained BS 4.3 BCBS Experiments 4.3.1 HYDICE Data 4.3.1.1 Target Detection 4.3.1.2 Unsupervised Mixed Pixel Classification 4.3.2 AVIRIS Cuprite Data 4.4 Target-Constrained BS 4.4.1 Target-Constrained Band Prioritization 4.4.1.1 Single Band Minimum Variance Band Prioritization by TCBS 4.4.1.2 Leave-One-Out Maximum Variance Band Prioritization by TCBS 4.4.2 Constrained-Target Band Selection 4.4.2.1 Sequential Feed-Forward TCBS 4.4.2.2 Sequential Backward TCBS 4.5 TCBS Experiments 4.6 Conclusion References Chapter 5 Band Subset Selection for Hyperspectral Imaging 5.1 Introduction 5.2 Simultaneous Multiple Band Selection 5.3 Search Strategies for BSS 5.3.1 Sequential Band Subset Selection 5.3.2 Successive Band Subset Selection 5.4 Channel Capacity BSS 5.5 Multiple Band-Constrained Band Subset Selection 5.5.1 Constrained BSS (CBSS) 5.5.2 Search Algorithms for CBSS 5.5.2.1 Sequential CBSS (SQ CBSS) 5.5.2.2 Successive CBSS (SC CBSS) 5.6 Application-Specified BSS (AS-BSS) 5.6.1 Application to Hyperspectral Classification 5.6.2 LCMV Criterion for BSS 5.6.3 LCMV-BSS Algorithms 5.6.3.1 SQ LCMV-CBSS 5.6.3.2 SC LCMV-CBSS 5.7 Experiments 5.7.1 MBC-BSS 5.7.2 MTC-BSS 5.7.2.1 Purdue Indiana Indian Pines Scene 5.7.2.2 Salinas 5.7.2.3 ROSIS Data 5.8 Conclusion References Chapter 6 Progressive Band Selection Processing for Hyperspectral Image Classification 6.1 Introduction 6.2 Measures of Class Classification Priority 6.3 p-Ary Huffman Coding Tree Construction 6.4 Iterative LCMV 6.4.1 Linearly Constrained Minimum Variance (LCMV) 6.4.2 Iterative Linearly Constrained Minimum Variance (ILCMV) 6.5 Class Signature Constrained Band Prioritization-Based Band Selection 6.6 Progressive Band Selection 6.7 Classification Measures 6.8 Real Images to be Used for Experiments 6.8.1 Purdue Indiana Indian Pines 6.8.2 Salinas 6.8.3 ROSIS Data 6.9 Experiments 6.9.1 Purdue Indiana Indian Pines 6.9.2 Salinas 6.9.3 University of Pavia 6.10 Conclusion References Part III Compressive Sensing for Hyperspectral Imaging Chapter 7 Restricted Entropy and Spectrum Properties for Hyperspectral Imaging 7.1 Introduction 7.2 Compressive Sensing Review 7.3 Restricted Entropy Property 7.4 Restricted Spectrum Property 7.5 REP and RSP Hyperspectral Measures 7.6 Experiments 7.7 Conclusion References Chapter 8 Endmember Finding in Compressively Sensed Band Domain 8.1 Introduction 8.2 Compressive Hyperspectral Band Sensing 8.2.1 Compressive Sensing Framework 8.2.2 Compressive Sensing of Hyperspectral Bands 8.2.3 Universality Model 8.3 Simplex Volume Calculation 8.3.1 Simplex Volume via Singular Value Decomposition 8.3.2 Simplex Volume via Matrix Determinant 8.4 Restricted Simplex Volume Property 8.5 Two Sequential Algorithms for p-FINDR 8.5.1 SeQuential p-FINDR (SQ p-FINDR) 8.5.2 SuCcessive p-FINDR (SC p-FINDR) 8.5.3 SQ p-FINDR and SC p-FINDR in CSBD 8.6 Experiments 8.6.1 Experimental Setup 8.6.2 Algorithm Analysis on Experimental Data 8.7 Experimental Results and Discussions 8.7.1 SQ p-FINDR Experimental Result Analysis 8.7.2 SC p-FINDR Experimental Result Analysis 8.8 Conclusion References Chapter 9 Hyperspectral Image Classification in Compressively Sensed Band Domain 9.1 Introduction 9.2 Compressive Sensing Review 9.2.1 Compressive Sensing Framework 9.2.2 Compressive Sensing of Hyperspectral Bands 9.2.3 Universality (Universal Sensed Model) 9.3 Hyperspectral Image Classification 9.3.1 Linear Support Vector Machines 9.3.2 Kernel Support Vector Machines 9.3.3 Edge-Preserving Filters 9.4 Classification Measures 9.5 Experiments 9.5.1 Experimental Setup 9.5.2 Classification Accuracy Analysis 9.5.2.1 Purdue Indian Pines Scene 9.5.2.2 Salinas Scene 9.5.2.3 Pavia University 9.5.2.4 Pavia Centre 9.5.3 Classification Precision Analysis 9.5.3.1 Purdue Indian Pines Scene 9.5.3.2 Salinas and Pavia Scenes 9.5.4 Discussions on Individual Class Accuracies 9.5.5 Scene Complexity Analysis 9.5.6 Selecting an Appropriate Number of CSBs 9.6 Conclusion References Part IV Fusion for Hyperspectral Imaging Chapter 10 Hyperspectral and LiDAR Data Fusion 10.1 Introduction 10.2 Deep Learning-Based HSI and LiDAR Data Classification 10.2.1 Two-Branch CNN for Joint Classification 10.2.2 Hierarchical Random Walk Network (HRWN) 10.2.3 Residual Network-Based Probability Reconstruction Fusion (RNPRF) 10.3 Experiments 10.3.1 Experimental Data 10.3.2 Classification Performance 10.4 Conclusions References Chapter 11 Hyperspectral Data Fusion Using Multidimensional Information 11.1 Introduction of Remote Sensing Data Fusion 11.1.1 Basic Idea of Data Fusion 11.1.2 Why Do We Need Data Fusion? 11.2 Common Methods for Data Fusion 11.2.1 Fusion Methods Emphasizing on Spatial Resolution Enhancement 11.2.1.1 Component Constitution Method 11.2.1.2 Multiresolution Analysis Method 11.2.2 Fusion Methods Emphasizing on Spectral Resolution Enhancement 11.2.2.1 Linear Optimization Method 11.2.2.2 Nonlinear Optimization Method 11.2.3 Fusion Methods Emphasizing on Temporal Resolution Enhancement 11.2.3.1 Weight-Based Method 11.2.3.2 Linear Optimization Method 11.2.3.3 Nonlinear Optimization Method 11.2.4 Quality Assessment of Data Fusion 11.2.4.1 Quality Assessment with Reference Image 11.2.4.2 Quality Assessment Without Reference Image 11.3 Enhancing Spectral Resolution of Multispectral Data Using Deep Learning Method 11.3.1 Data Fusion Methods Using Deep Learning 11.3.2 Spectral Resolution Enhancement Method via Convolutional Neural Networks (SRECNN) 11.3.3 Application 1: Extending the Swath of Hyperspectral Data 11.3.4 Application 2: Cloud Removal of GF-5 Hyperspectral Data 11.4 Multidimensional Datasets (MDD) 11.4.1 Introduction of Multidimensional Datasets (MDD) 11.4.1.1 Temporal Sequential in Band (TSB) 11.4.1.2 Temporal Sequential in Pixel (TSP) 11.4.1.3 Temporal Interleaved by Band (TIB) 11.4.1.4 Temporal Interleaved by Pixel (TIP) 11.4.1.5 Temporal Interleaved by Spectrum (TIS) 11.4.2 Spatial-Temporal Data Fusion via Multidimensional Datasets 11.4.2.1 Basic Architecture 11.4.2.2 Dataset Experiment 11.5 A Fusion-Related Case Study: Comparison of Fusion Methods on GF-5 Hyperspectral Data 11.5.1 Backgrounds 11.5.2 Experiment 11.5.3 Visual Analysis 11.5.4 Index Evaluation 11.5.5 Classification Application 11.5.6 Discussion 11.6 Conclusion References Chapter 12 Fusion of Band Selection Methods for Hyperspectral Imaging 12.1 Introduction 12.2 Band Selection Fusion 12.2.1 Simultaneous Band Selection Fusion 12.2.2 Progressive Band Selection Fusion 12.3 Experiments 12.3.1 Linear Spectral Unmixing 12.3.2 Hyperspectral Image Classification 12.3.2.1 AVIRIS Data 12.3.2.2 ROSIS Data 12.4 Conclusion References Part V Hyperspectral Data Unmixing Chapter 13 Model-Inspired Deep Neural Networks for Hyperspectral Unmixing 13.1 Model-Based and Learning-Based Spectral Unmixing 13.1.1 Mode-Based Spectral Unmixing 13.1.2 Learning-Based Spectral Unmixing 13.2 Model-Inspired Learning for Spectral Unmixing 13.2.1 Why Should Model-Driven and Data-Driven Techniques Be Combined? 13.2.2 How Are Model-Inspired Unmixing Network Architectures Designed? 13.3 Unfolded Iterative Shrinkage-Thresholding Model for Supervised Abundance Estimation 13.3.1 Linear Mixture Model 13.3.2 Model Optimization 13.3.3 Unfolded ISTA for Abundance Estimation 13.3.4 Experimental Results on Synthetic Data 13.3.4.1 Data Generation 13.3.4.2 Evaluation Index 13.3.4.3 Experiment Setting 13.3.4.4 Impact of Sampling Strategy 13.3.4.5 Impact of Number of Layers 13.3.4.6 Impact of Number of Training Samples 13.3.4.7 Robustness to Noise 13.3.4.8 Running Time Comparison 13.3.5 Experimental Results on Real-World Data 13.4 Model-Inspired Network Architectures for Blind Unmixing 13.4.1 Unsupervised Model-Inspired NN for Blind Unmixing 13.4.2 Experimental Results on Synthetic Data 13.4.2.1 Evaluation Index 13.4.2.2 Impact of Number of Layers 13.4.2.3 Impact of Number of Training Samples 13.4.2.4 Robustness to Noise 13.4.2.5 Running Time Comparison 13.4.3 Experimental Results on Real-World Data 13.5 NMF-Inspired Sparse Autoencoder for Hyperspectral Unmixing 13.5.1 Model Optimization 13.5.2 Network Architecture 13.5.3 Experimental Results on Synthetic Data 13.5.3.1 Influence of the Number of Training Samples 13.5.3.2 Comparison with the State-of-the-Arts 13.5.4 Experimental Results on Real-World Data 13.6 Learning a Deep Alternating Neural Network for Hyperspectral Unmixing 13.6.1 Model Formulation 13.6.2 Model Optimization 13.6.3 SNMF-NET 13.6.3.1 Connection Between Proximal Gradient Method and DNN 13.6.3.2 Lp-NMF Inspired Deep Alternating Neural Network 13.6.4 Experimental Results on Synthetic Data 13.6.4.1 The Influence of Layers 13.6.4.2 Influence of the Number of Training Samples 13.6.4.3 Influence of Initialization 13.6.4.4 Robustness to Noise 13.6.4.5 Experiments on Real-world Data 13.7 Conclusion References Chapter 14 Analytical Fully Constrained Least Squares Linear Spectral Mixture Analysis 14.1 Introduction 14.2 Linear Spectral Mixture Analysis 14.3 Fully Constrained Least Squares Method 14.3.1 Abundance Sum-to-one-Constrained LSMA 14.3.2 Abundance Non-negativity-Constrained LSMA 14.3.3 Abundance Fully Constrained LSMA 14.4 Modified Fully Constrained Linear Squares Method 14.5 Analytical Non-negativity-Constrained Linear Squares Method 14.6 Analytical Fully Constrained Least Squares Method 14.7 Experiments 14.8 Conclusion References Chapter 15 Swarm Intelligence Optimization-Based Spectral Unmixing 15.1 Introduction 15.2 Pixel Mixing Models 15.3 Swarm Intelligence Optimization-Based Approaches 15.3.1 Approach Based on LMM 15.3.2 Approach Based on NCM 15.3.2.1 "Winner-Take-All" Version of the EM 15.3.2.2 PSO for Abundance Optimization 15.3.2.3 Unmixing Based on PSO-EM Algorithm 15.3.3 Approach Based on NLMM 15.4 Experiments 15.4.1 Experimental Data 15.4.1.1 Moffett Field Data 15.4.1.2 Cuprite Data 15.4.2 Results of Approaches Based on LMM 15.4.2.1 Pure Pixel-Based Algorithm 15.4.2.2 Minimum Volume-Based Algorithm 15.4.3 Results of Approaches Based on NCM 15.4.3.1 Experiment Using Moffett Field Data 15.4.3.2 Experiment Using Cuprite Data 15.4.4 Results of Approaches Based on NLMM 15.4.4.1 Experiment Using Moffett Field Data 15.4.4.2 Experiment Using Cuprite Data 15.5 Conclusion Acknowledgments References Chapter 16 Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing 16.1 Introduction 16.2 Robust NMF 16.2.1 RNMF Using ℓ2,1 Norm 16.2.2 RNMF Using ℓ1, 2 Norm 16.3 Spectral-Spatial Robust Nonnegative Matrix Factorization (SSRNMF) 16.3.1 SSRNMF Model 16.3.2 Update Rules for SSRNMF 16.3.3 Implementation Issues 16.3.4 Computation Complexity Analysis 16.4 Experiments on Synthetic Data 16.4.1 Robustness Analysis to Noise Composition 16.4.2 Robustness Analysis to Noise Intensity 16.4.3 Performance Comparison When the Number of Endmembers Varies 16.5 Experiments on First Real Data 16.5.1 Results Without the Low-SNR Bands 16.5.2 Results with the Low-SNR Bands 16.6 Experiments on Second Real Data 16.7 Conclusion References Part VI Hyperspectral Image Classification Chapter 17 Sparse Representation-Based Hyperspectral Image Classification 17.1 Introduction 17.2 Classic Representation-Based Models 17.2.1 Sparse Representation-Based Framework 17.2.2 Joint Representation-Based Framework 17.3 Sparse Representation-Based Hyperspectral Image Classification 17.3.1 Approach in the Spectral Domain 17.3.2 Approach in the Spectral-Spatial Domain 17.4 Experimental Results and Analysis 17.4.1 Experimental Data 17.4.1.1 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Indian Pines Scene 17.4.1.2 Reflective Optics Spectrographic Imaging System (ROSIS) University of Pavia Scene 17.4.2 Parameter Tuning 17.4.3 Results and Analysis 17.5 Conclusion Acknowledgments References Chapter 18 Collaborative Classification Based on Hyperspectral Images 18.1 Introduction 18.1.1 Hyperspectral and Panchromatic Images 18.1.2 Hyperspectral and Multispectral Images 18.1.3 LWIR Hyperspectral and Visible Images 18.2 Problems and Challenges in Multisource Image Collaborative Processing 18.3 Collaborative Classification of Hyperspectral and High-Resolution Panchromatic Images 18.3.1 Methodology 18.3.2 Experiments 18.4 Collaborative Classification of Infrared Hyperspectral and Visible Images 18.4.1 Methodology 18.4.2 Experiments 18.5 Conclusions References Chapter 19 Class Feature-Weighted Hyperspectral Image Classification 19.1 Introduction 19.2 Class Feature Descriptors 19.2.1 Intra-Class Feature Descriptors for Class Variability 19.2.2 Inter-Class Feature Descriptors for Class Separability 19.2.3 Total Class Features 19.2.4 Calculation of CF Probabilities 19.3 Allocation of Class Training Sample Size 19.4 CFW-HSIC 19.5 Experiments 19.5.1 Purdue University's Indiana Indian Pines 19.5.2 University of Pavia 19.6 Novelties 19.7 Conclusion References Chapter 20 Target Detection Approaches to Hyperspectral Image Classification 20.1 Introduction 20.2 Signal Detection Theory 20.3 Binary Classification Theory 20.4 Multiple Hypotheses Testing Theory 20.4.1 Multi-Target Detection Problems 20.4.1.1 One-Against-All (Winner-Take-All) Approach 20.4.1.2 One-Against-One Approach 20.4.1.3 Multi-Class Classification Problems 20.5 Iterative Constrained Energy Minimization 20.6 Iterative Linearly Constrained Minimum Variance 20.7 How to Convert Detection to Classification 20.8 Real Image Experiments 20.8.1 Purdue University´s Indiana Indian Pines 20.8.2 University of Pavia 20.9 Conclusion References Index EULA