| Pubbl/distr/stampa |
Hoboken, New Jersey : , : Wiley : , : IEEE Press, , [2023]
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| Descrizione fisica |
1 online resource (611 pages)
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| Disciplina |
771
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| Collana |
IEEE Press
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| Soggetto topico |
Hyperspectral imaging
Image processing
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| ISBN |
1-119-68778-0
1-119-68775-6
1-119-68777-2
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| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
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| 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.
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| Record Nr. | UNINA-9910830508903321 |
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50 wege zur kreativen Fotografie / / Michael Freeman
