Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 online resource (434 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
air temperature
anomaly detection atmospheric transmittance attention mechanism band selection band selection (BS) boundary-aware constraint carbon dioxide absorption change detection channel augmented attention classification coffee beans color formation models constrained energy minimization (CEM) constrained sparse representation constrained-target optimal index factor band selection (CTOIFBS) constraint representation convolutional neural network data augmentation data fusion deep convolutional neural networks deep learning denoising emissivity evolutionary computation FTIR fused features generative adversarial network heuristic algorithms hyperspectral hyperspectral image hyperspectral image classification hyperspectral image few-shot classification hyperspectral image super-resolution hyperspectral imagery hyperspectral imagery classification hyperspectral images hyperspectral imaging hyperspectral imaging (HSI) hyperspectral imaging data hyperspectral reconstruction hyperspectral remote sensing hyperspectral target detection hyperspectral unmixing image classification image fusion insect damage joint tensor decomposition least square estimation lightweight convolutional neural networks machine learning meta-learning midwave infrared mine environment moving target detection multi-source image fusion multiscale decision fusion multispectral image MWIR nonlinear unmixing plug-and-play relation network residual augmented attentional u-shape network rice rice leaf blast rice leaf folder self-supervised learning self-supervised training separation SFIM spatial augmented attention spatial filter spatial measurement spatio-temporal processing spectral reconstruction spectral-spatial residual network superpixel segmentation target detection temperature transfer learning underwater hyperspectral target detection underwater spectral imaging system unmanned aerial vehicles (UAVs) upland swamps vegetation vegetation mapping visualization |
| 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 | 526.9/82 |
| Altri autori (Persone) | ChangChein-I |
| Soggetto topico |
Remote sensing
Multispectral imaging Image processing - Digital techniques |
| ISBN |
9786610822430
9781280822438 1280822430 9780470124628 0470124628 9780470124611 047012461X |
| 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-9911019937403321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hyperspectral data processing : algorithm design and analysis / / Chein-I Chang
| Hyperspectral data processing : algorithm design and analysis / / Chein-I Chang |
| Autore | Chang Chein-I |
| Edizione | [1st ed.] |
| 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 |
9781118269787
1118269780 9781118269770 1118269772 9781299241862 1299241867 9781118269756 1118269756 |
| 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
|
||
| Hoboken, N.J., : Wiley-Interscience, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 online resource (632 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
90° yaw imaging
adaptive window Africa agroforestry AHS airborne laser scanning algebraic multigrid methods anomaly detection AVIRIS band expansion process (BEP) band grouping band selection band selection (BS) band subset selection (BSS) biodiversity class imbalance classification composite kernel constrained energy minimization constrained energy minimization (CEM) correlation band expansion process (CBEP) data fusion data integration data unmixing data-guided constraints deep belief networks deep learning deep pipelined background statistics Dunhuang site endmember extraction ensemble learning evenness fire severity Gram-Schmidt orthogonalization graph hashing ensemble hierarchical feature high-level synthesis HyMap hyperspectral hyperspectral classification hyperspectral compression hyperspectral detection hyperspectral image hyperspectral image (HSI) hyperspectral image classification hyperspectral imagery hyperspectral images (HSIs) hyperspectral imaging hyperspectral pansharpening hyperspectral unmixing image enhancement image fusion imaging spectroscopy in situ measurements intrinsic image decomposition irradiance-based method iterative algorithm iterative CEM (ICEM) KSVD label propagation linearly constrained minimum variance (LCMV) local abundance local summation RX detector (LS-RXD) lossy compression machine learning mineral mapping minimum noise fraction multiscale spatial information multiscale union regions adaptive sparse representation (MURASR) nonlinear band expansion (NBE) nonnegative matrix factorization nuclear norm on-board compression optical spectral region orthogonal projections Otsu's method panchromatic panchromatic image parallel processing peatland progressive sample processing (PSP) prototype space raw material real-time processing recursive anomaly detection reflectance-based method remote sensing rolling guidance filtering (RGF) rotation forest semi-supervised learning semi-supervised local discriminant analysis sequential LCMV-BSS (SQ LCMV-BSS) sliding window sparse coding sparse unmixing sparseness spectral mixture analysis spectral variability spectral-spatial classification sprout detection structure tensor successive LCMV-BSS (SC LCMV-BSS) superpixel SVM target detection terrestrial hyperspectral imaging texture feature enhancement thermal infrared spectral region tree species tree-based ensemble vegetation type vicarious calibration vineyard water stress weighted fusion weighted least squares filter |
| 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 | ||
| ||