Advances in hyperspectral image processing techniques / / edited by Chein-I Chang

• Pubblicazione: Hoboken, New Jersey : , : Wiley : , : IEEE Press, , [2023]; ©2023
• Descrizione fisica: 1 online resource (611 pages)
• Disciplina: 771
• Soggetto topico: Hyperspectral imaging; Image processing
• Persona (resp. second.): ChangChein-I
• Nota di bibliografia: Includes bibliographical references and index.
• 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.
• Sommario/riassunto: "This book is derived from recent development of hyperspectral imaging (HSI) techniques in the field. Many new ideas have been explored and have led in various new directions in the past a few years. The book's content is based on the expertise of invited scholars and is categorized into six parts. Part I describes hyperspectral data unmixing (4 chapters). Part II spans topics from hyperspectral target detection to hyperspectral image classification (8 chapters). Part III explains band selection for HSI (4 chapters). Part IV covers compressive sensing for HSI (2 chapters). Part V pertains to fusion for HSI (5 chapters)."--
• Titolo autorizzato: Advances in hyperspectral image processing techniques
• ISBN: 1-119-68778-0; 1-119-68775-6; 1-119-68777-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830508903321
• Lo trovi qui: Univ. Federico II
• Serie: IEEE Press