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Advances in Hyperspectral Data Exploitation
| Advances in Hyperspectral Data Exploitation |
| Autore | Chang Chein-I |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (434 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
hyperspectral image few-shot classification
deep learning meta-learning relation network convolutional neural network constrained-target optimal index factor band selection (CTOIFBS) hyperspectral image underwater spectral imaging system underwater hyperspectral target detection band selection (BS) constrained energy minimization (CEM) lightweight convolutional neural networks hyperspectral imagery classification transfer learning air temperature spatial measurement FTIR MWIR carbon dioxide absorption target detection coffee beans insect damage hyperspectral imaging band selection visualization color formation models multispectral image image fusion joint tensor decomposition anomaly detection constrained sparse representation hyperspectral imagery moving target detection spatio-temporal processing hyperspectral remote sensing image classification constraint representation superpixel segmentation multiscale decision fusion plug-and-play denoising nonlinear unmixing spectral reconstruction residual augmented attentional u-shape network spatial augmented attention channel augmented attention boundary-aware constraint atmospheric transmittance temperature emissivity separation midwave infrared hyperspectral images hyperspectral image super-resolution data fusion spectral-spatial residual network self-supervised training hyperspectral vegetation generative adversarial network data augmentation classification rice leaf blast hyperspectral imaging data deep convolutional neural networks fused features evolutionary computation heuristic algorithms machine learning unmanned aerial vehicles (UAVs) vegetation mapping upland swamps mine environment rice rice leaf folder hyperspectral image classification change detection self-supervised learning attention mechanism multi-source image fusion SFIM least square estimation spatial filter hyperspectral imaging (HSI) hyperspectral target detection hyperspectral reconstruction hyperspectral unmixing |
| ISBN | 3-0365-5796-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637782203321 |
Chang Chein-I
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in hyperspectral image processing techniques / / edited by Chein-I Chang
| Advances in hyperspectral image processing techniques / / edited by Chein-I Chang |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley : , : IEEE Press, , [2023] |
| Descrizione fisica | 1 online resource (611 pages) |
| Disciplina | 771 |
| Collana | IEEE Press |
| Soggetto topico |
Hyperspectral imaging
Image processing |
| ISBN |
1-119-68778-0
1-119-68775-6 1-119-68777-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910830508903321 |
| Hoboken, New Jersey : , : Wiley : , : IEEE Press, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hyperspectral data exploitation [[electronic resource] ] : theory and applications / / edited by Chein-I Chang
| Hyperspectral data exploitation [[electronic resource] ] : theory and applications / / edited by Chein-I Chang |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (442 p.) |
| Disciplina |
526.982
621.3678 |
| Altri autori (Persone) | ChangChein-I |
| Soggetto topico |
Remote sensing
Multispectral photography Image processing - Digital techniques |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-82243-0
9786610822430 0-470-12462-8 0-470-12461-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
HYPERSPECTRAL DATA EXPLOITATION; CONTENTS; PREFACE; CONTRIBUTORS; 1. OVERVIEW; I TUTORIALS; 2. HYPERSPECTRAL IMAGING SYSTEMS; 3. INFORMATION-PROCESSED MATCHED FILTERS FOR HYPERSPECTRAL TARGET DETECTION AND CLASSIFICATION; II THEORY; 4. AN OPTICAL REAL-TIME ADAPTIVE SPECTRAL IDENTIFICATION SYSTEM (ORASIS); 5. STOCHASTIC MIXTURE MODELING; 6. UNMIXING HYPERSPECTRAL DATA: INDEPENDENT AND DEPENDENT COMPONENT ANALYSIS; 7. MAXIMUM VOLUME TRANSFORM FOR ENDMEMBER SPECTRA DETERMINATION; 8. HYPERSPECTRAL DATA REPRESENTATION; 9. OPTIMAL BAND SELECTION AND UTILITY EVALUATION FOR SPECTRAL SYSTEMS
10. FEATURE REDUCTION FOR CLASSIFICATION PURPOSE11. SEMISUPERVISED SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES; III APPLICATIONS; 12. DECISION FUSION FOR HYPERSPECTRAL CLASSIFICATION; 13. MORPHOLOGICAL HYPERSPECTRAL IMAGE CLASSIFICATION: A PARALLEL PROCESSING PERSPECTIVE; 14. THREE-DIMENSIONAL WAVELET-BASED COMPRESSION OF HYPERSPECTRAL IMAGERY; INDEX |
| Record Nr. | UNINA-9910143423103321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hyperspectral data exploitation [[electronic resource] ] : theory and applications / / edited by Chein-I Chang
| Hyperspectral data exploitation [[electronic resource] ] : theory and applications / / edited by Chein-I Chang |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (442 p.) |
| Disciplina |
526.982
621.3678 |
| Altri autori (Persone) | ChangChein-I |
| Soggetto topico |
Remote sensing
Multispectral photography Image processing - Digital techniques |
| ISBN |
1-280-82243-0
9786610822430 0-470-12462-8 0-470-12461-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
HYPERSPECTRAL DATA EXPLOITATION; CONTENTS; PREFACE; CONTRIBUTORS; 1. OVERVIEW; I TUTORIALS; 2. HYPERSPECTRAL IMAGING SYSTEMS; 3. INFORMATION-PROCESSED MATCHED FILTERS FOR HYPERSPECTRAL TARGET DETECTION AND CLASSIFICATION; II THEORY; 4. AN OPTICAL REAL-TIME ADAPTIVE SPECTRAL IDENTIFICATION SYSTEM (ORASIS); 5. STOCHASTIC MIXTURE MODELING; 6. UNMIXING HYPERSPECTRAL DATA: INDEPENDENT AND DEPENDENT COMPONENT ANALYSIS; 7. MAXIMUM VOLUME TRANSFORM FOR ENDMEMBER SPECTRA DETERMINATION; 8. HYPERSPECTRAL DATA REPRESENTATION; 9. OPTIMAL BAND SELECTION AND UTILITY EVALUATION FOR SPECTRAL SYSTEMS
10. FEATURE REDUCTION FOR CLASSIFICATION PURPOSE11. SEMISUPERVISED SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES; III APPLICATIONS; 12. DECISION FUSION FOR HYPERSPECTRAL CLASSIFICATION; 13. MORPHOLOGICAL HYPERSPECTRAL IMAGE CLASSIFICATION: A PARALLEL PROCESSING PERSPECTIVE; 14. THREE-DIMENSIONAL WAVELET-BASED COMPRESSION OF HYPERSPECTRAL IMAGERY; INDEX |
| Record Nr. | UNINA-9910830729603321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hyperspectral data exploitation : theory and applications / / edited by Chein-I Chang
| Hyperspectral data exploitation : theory and applications / / edited by Chein-I Chang |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (442 p.) |
| Disciplina |
526.982
621.3678 |
| Altri autori (Persone) | ChangChein-I |
| Soggetto topico |
Remote sensing
Multispectral photography Image processing - Digital techniques |
| ISBN |
1-280-82243-0
9786610822430 0-470-12462-8 0-470-12461-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
HYPERSPECTRAL DATA EXPLOITATION; CONTENTS; PREFACE; CONTRIBUTORS; 1. OVERVIEW; I TUTORIALS; 2. HYPERSPECTRAL IMAGING SYSTEMS; 3. INFORMATION-PROCESSED MATCHED FILTERS FOR HYPERSPECTRAL TARGET DETECTION AND CLASSIFICATION; II THEORY; 4. AN OPTICAL REAL-TIME ADAPTIVE SPECTRAL IDENTIFICATION SYSTEM (ORASIS); 5. STOCHASTIC MIXTURE MODELING; 6. UNMIXING HYPERSPECTRAL DATA: INDEPENDENT AND DEPENDENT COMPONENT ANALYSIS; 7. MAXIMUM VOLUME TRANSFORM FOR ENDMEMBER SPECTRA DETERMINATION; 8. HYPERSPECTRAL DATA REPRESENTATION; 9. OPTIMAL BAND SELECTION AND UTILITY EVALUATION FOR SPECTRAL SYSTEMS
10. FEATURE REDUCTION FOR CLASSIFICATION PURPOSE11. SEMISUPERVISED SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES; III APPLICATIONS; 12. DECISION FUSION FOR HYPERSPECTRAL CLASSIFICATION; 13. MORPHOLOGICAL HYPERSPECTRAL IMAGE CLASSIFICATION: A PARALLEL PROCESSING PERSPECTIVE; 14. THREE-DIMENSIONAL WAVELET-BASED COMPRESSION OF HYPERSPECTRAL IMAGERY; INDEX |
| Record Nr. | UNINA-9910841298003321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hyperspectral data processing [[electronic resource] ] : algorithm design and analysis / / Chein-I Chang
| Hyperspectral data processing [[electronic resource] ] : algorithm design and analysis / / Chein-I Chang |
| Autore | Chang Chein-I |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, 2013 |
| Descrizione fisica | 1 online resource (1165 p.) |
| Disciplina | 621.39/94 |
| Altri autori (Persone) | ChangChein-I |
| Soggetto topico |
Image processing - Digital techniques
Spectroscopic imaging Signal processing |
| ISBN |
1-118-26978-0
1-118-26977-2 1-299-24186-7 1-118-26975-6 |
| Classificazione | TEC015000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
HYPERSPECTRAL DATA PROCESSING: Algorithm Design and Analysis; CONTENTS; PREFACE; 1 OVERVIEW AND INTRODUCTION; 1.1 Overview; 1.2 Issues of Multispectral and Hyperspectral Imageries; 1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery; 1.3.1 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 Analysis 1.5.5 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 Book 1.7.1 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 Decisions 2.3.1.1 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 Formulation 3.3 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 3.6.3 Chemical/Biological Agent Detection |
| Record Nr. | UNINA-9910141611003321 |
|
Chang Chein-I
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| Hoboken, N.J., : Wiley-Interscience, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Hyperspectral Imaging and Applications
| Hyperspectral Imaging and Applications |
| Autore | Chang Chein-I |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (632 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
biodiversity
peatland vegetation type classification hyperspectral in situ measurements hyperspectral image (HSI) multiscale union regions adaptive sparse representation (MURASR) multiscale spatial information imaging spectroscopy airborne laser scanning minimum noise fraction class imbalance Africa agroforestry tree species hyperspectral unmixing endmember extraction band selection spectral variability prototype space ensemble learning rotation forest semi-supervised local discriminant analysis optical spectral region thermal infrared spectral region mineral mapping data integration HyMap AHS raw material remote sensing nonnegative matrix factorization data-guided constraints sparseness evenness hashing ensemble hierarchical feature hyperspectral classification band expansion process (BEP) constrained energy minimization (CEM) correlation band expansion process (CBEP) iterative CEM (ICEM) nonlinear band expansion (NBE) Otsu’s method sparse unmixing local abundance nuclear norm hyperspectral detection target detection sprout detection constrained energy minimization iterative algorithm adaptive window hyperspectral imagery recursive anomaly detection local summation RX detector (LS-RXD) sliding window band selection (BS) band subset selection (BSS) hyperspectral image classification linearly constrained minimum variance (LCMV) successive LCMV-BSS (SC LCMV-BSS) sequential LCMV-BSS (SQ LCMV-BSS) vicarious calibration reflectance-based method irradiance-based method Dunhuang site 90° yaw imaging terrestrial hyperspectral imaging vineyard water stress machine learning tree-based ensemble progressive sample processing (PSP) real-time processing image fusion hyperspectral image panchromatic image structure tensor image enhancement weighted fusion spectral mixture analysis fire severity AVIRIS deep belief networks deep learning texture feature enhancement band grouping hyperspectral compression lossy compression on-board compression orthogonal projections Gram–Schmidt orthogonalization parallel processing anomaly detection sparse coding KSVD hyperspectral images (HSIs) SVM composite kernel algebraic multigrid methods hyperspectral pansharpening panchromatic intrinsic image decomposition weighted least squares filter spectral-spatial classification label propagation superpixel semi-supervised learning rolling guidance filtering (RGF) graph deep pipelined background statistics high-level synthesis data fusion data unmixing hyperspectral imaging |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910585941603321 |
|
Chang Chein-I
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||