Hyperspectral image processing

Hyperspectral image processing

Based on the authors' research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.

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Main Authors: Wang, Liguo autor/a, Zhao, Chunhui autor/a
Format: Texto biblioteca
Language:eng
Published: New York, New York, United States Springer Berlin Heidelberg 2015
Subjects:Sensores remotos, Imaginería hiperespectral, Procesamiento de imágenes,
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id KOHA-OAI-ECOSUR:42044
record_format koha
institution ECOSUR
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
Fisico
databasecode cat-ecosur
tag biblioteca
region America del Norte
libraryname Sistema de Información Bibliotecario de ECOSUR (SIBE)
language eng
topic Sensores remotos
Imaginería hiperespectral
Procesamiento de imágenes
Sensores remotos
Imaginería hiperespectral
Procesamiento de imágenes
spellingShingle Sensores remotos
Imaginería hiperespectral
Procesamiento de imágenes
Sensores remotos
Imaginería hiperespectral
Procesamiento de imágenes
Wang, Liguo autor/a
Zhao, Chunhui autor/a
Hyperspectral image processing
description Based on the authors' research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.
format Texto
topic_facet Sensores remotos
Imaginería hiperespectral
Procesamiento de imágenes
author Wang, Liguo autor/a
Zhao, Chunhui autor/a
author_facet Wang, Liguo autor/a
Zhao, Chunhui autor/a
author_sort Wang, Liguo autor/a
title Hyperspectral image processing
title_short Hyperspectral image processing
title_full Hyperspectral image processing
title_fullStr Hyperspectral image processing
title_full_unstemmed Hyperspectral image processing
title_sort hyperspectral image processing
publisher New York, New York, United States Springer Berlin Heidelberg
publishDate 2015
work_keys_str_mv AT wangliguoautora hyperspectralimageprocessing
AT zhaochunhuiautora hyperspectralimageprocessing
_version_ 1756226135037837313
spelling KOHA-OAI-ECOSUR:420442020-11-25T14:44:13ZHyperspectral image processing Wang, Liguo autor/a Zhao, Chunhui autor/a textNew York, New York, United States Springer Berlin Heidelberg2015engBased on the authors' research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.Incluye bibliografía1 Basic Theory and Main Processing Techniques of Hyperspectral Remote Sensing.. 1.1 Basic Theory of Hyperspectral Remote Sensing.. 1.1.1 Theory of Remote Electromagnetic Wave.. 1.1.2 Interaction of Solar Radiation and Materials.. 1.1.3 Imaging Spectrometer and Spectral Imaging Modes.. 1.1.4 Imaging Characteristics of HSI.. 1.2 Classification Technique of HSI.. 1.2.1 Supervised Classifications and Unsupervised Classifications.. 1.2.2 Parameter Classifications and Nonparameter Classifications.. 1.2.3 Crisp Classifications and Fuzzy Classifications. 1.2.4 Other Classification Methods.. 1.3 Endmember Extraction Technique of HSI.. 1.4 Spectral Unmixing Technique of HSI.. 1.4.1 Nonlinear Model.. 1.4.2 Linear Model.. 1.4.3 Multi-endmember Mode of Linear Model.. 1.5 Sub-pixel Mapping Technique of HSI.. 1.5.1 Spatial Correlation-Based Sub-pixel Mapping.. 1.5.2 Spatial Geostatistics-Based Sub-pixel Mapping. 1.5.3 Neural Network-Based Sub-pixel Mapping.. 1.5.4 Pixel-Swapping Strategy-Based Sub-pixel Mapping.. 1.6 Super Resolution Technique of HSI.. 1.7 Anomaly Detection Technique of HSI.. 1.8 Dimensionality Reduction and Compression Technique for HSI.. 1.8.1 Dimensionality Reduction: Band Selection and Feature Extraction.. 1.8.2 Compression: Lossy Compression and Lossless Compression.. References.. 2 Classification Technique for HSI.. 2.1 Typical Classification Methods.. 2.2 Typical Assessment Criterions.. 2.3 SVM-Based Classification Method.. 2.3.1 Theory Foundation.. 2.3.2 Classification Principle.. 2.3.3 Construction of Multi-class Classifier with the Simplest Structure.. 2.3.4 Least Squares SVM and Its SMO Optimization Algorithm.. 2.3.5 Triply Weighted Classification Method.. 2.4 Performance Assessment for SVM-Based Classification.. 2.4.1 Performance Assessment for Original SVM-Based Classification.. 2.4.2 Performance Assessment for Multi-class Classifier with the Simplest Structure2.4.3 Performance Assessment for Triply Weighted Classification.. 2.5 Chapter Conclusions.. References.. 3 Endmember Extraction Technique of HSI.. 3.1 Endmember Extraction Method: N-FINDR.. 3.1.1 Introduction of Related Theory.. 3.1.2 N-FINDR Algorithm.. 3.2 Distance Measure-Based Fast N-FINDR Algorithm.. 3.2.1 Substituting Distance Measure for Volume One.. 3.2.2 PPI Concept-Based Pixel Indexing.. 3.2.3 Complexity Analysis and Efficiency Assessment.. 3.3 Linear LSSVM-Based Distance Calculation.. 3.4 Robust Method in Endmember Extraction.. 3.4.1 In the Pre-processing Stage: Obtaining of Robust Covariance Matrix.. 3.4.2 In Endmember Extraction Stage: Deletion of Outliers.. 3.5 Performance Assessment.. 3.5.1 Distance Measure-Based N-FINDR Fast Algorithm.. 3.5.2 Robustness Assessment.. 3.6 Two Applications of Fast N-FINDR Algorithm.. 3.6.1 Construction of New Solving Algorithm for LSMM.. 3.6.2 Construction of Fast and Unsupervised Band Selection Algorithm.. 3.7 Chapter Conclusions.. References.. 4 Spectral Unmixing Technique of HSI.. 4.1 LSMM-Based LSMA Method.. 4.2 Two New Solving Methods for Full Constrained LSMA.. 4.2.1 Parameter Substitution Method in Iteration Solving Method.. 4.2.2 Geometric Solving Method.. 4.3 The Principle of LSVM-Based Spectral Unmixing.. 4.3.1 Equality Proof of LSVM and LSMM for Spectral Unmixing.. 4.3.2 The Unique Superiority of LSVM-Based Unmixing.. 4.4 Spatial-Spectral Information-Based Unmixing Method.. 4.5 SVM-Based Spectral Unmixing Model with Unmixing Residue Constraints.. 4.5.1 Original LSSVM-Based Spectral Unmixing.. 4.5.2 Construction of Spectral Unmixing Model Based on Unmixing Residue Constrained LSSVM and Derivation of Its Closed form Solution.. 4.5.3 Substituting Multiple Endmembers for Single One in the New Model.. 4.6 Performance Assessment.. 4.6.1 Performance Assessment for Original SVM-Based Spectral Unmixing.. 4.6.2 Assessment on Robust Weighted SVM-Based Unmixing4.6.3 Assessment on Spatial-Spectral Unmixing Method.. 4.6.4 Performance Assessment on New SVM Unmixing Model with Unmixing Residue Constraints.. 4.7 Fuzzy Method of Accuracy Assessment of Spectral Unmixing.. 4.7.1 Fuzzy Method of Accuracy Assessment.. 4.7.2 Application of Fuzzy Method of Accuracy Assessment in Experiments.. 4.8 Chapter Conclusions.. References.. 5 Subpixel Mapping Technique of HSI.. 5.1 Subpixel Mapping for a Land Class with Linear Features Using a Least Square Support Vector Machine (LSSVM.. 5.1.1 Subpixel Mapping Based on the Least Square Support Vector Machine (LSSVM.. 5.1.2 Artificially Synthesized Training Samples.. 5.2 Spatial Attraction-Based Subpixel Mapping (SPSAM.. 5.2.1 Subpixel Mapping Based on the Modified Subpixel/Pixel Spatial Attraction Model (MSPSAM.. 5.2.2 Subpixel Mapping Based on the Mixed Spatial Attraction Model (MSAM.. 5.3 Subpixel Mapping Using Markov Random Field with Subpixel Shifted Remote Sensing Images.. 5.3.1 Markov Random Field-Based Subpixel Mapping.. 5.3.2 Markov Random Field-Based Subpixel Mapping with Subpixel Shifted Remote-Sensing Images.. 5.4 Accuracy Assessment.. 5.4.1 Subpixel Mapping for Land Class with Linear Features Using the Least Squares Support Vector Machine (LSSVM.. 5.4.2 MSPSAM and MSAM.. 5.4.3 MRF-Based Subpixel Mapping with Subpixel Shifted Remote-Sensing Images.. 5.5 Chapter Conclusions.. References.. 6 Super-Resolution Technique of HIS.. 6.1 POCS Algorithm-Based Super-Resolution Recovery.. 6.1.1 Basic Theory of POCS.. 6.1.2 POCS Algorithm-Based Super-Resolution Recovery.. 6.2 MAP Algorithm-Based Super-Resolution Recovery.. 6.2.1 Basic Theory of MAP.. 6.2.2 MAP Algorithm-Based Super-Resolution Recovery.. 6.3 Resolution Enhancement Method for Single Band.. 6.3.1 Construction of Geometric Dual Model and Interpolation Method.. 6.3.2 Mixed Interpolation Method.. 6.4 Performance Assessment.. 6.4.1 POCS and MAP-Based Super-Resolution Methods6.4.2 Dual Interpolation Method.. 6.5 Chapter Conclusions.. References.. 7 Anomaly Detection Technique of HSI.. 7.1 Kernel Detection Algorithm Based on the Theory of the Morphology.. 7.1.1 Band Selection Based on Morphology.. 7.1.2 Kernel RX Algorithm Based on Morphology.. 7.2 Adaptive Kernel Anomaly Detection Algorithm.. 7.2.1 The Method of Support Vector Data Description.. 7.2.2 Adaptive Kernel Anomaly Detection Algorithm.. 7.3 Construction of Spectral Similarity Measurement Kernel in Kernel Anomaly Detection.. 7.3.1 The Limitations of Gaussian Radial Basis Kernel.. 7.3.2 Spectral Similarity Measurement Kernel Function.. 7.4 Performance Assessment.. 7.4.1 Effect Testing of Morphology-Based Kernel Detection Algorithm.. 7.4.2 Effect Testing of Adaptive Kernel Anomaly Detection Algorithm.. 7.4.3 Effect Testing of Spectral Similarity Measurement Kernel-Based Anomaly Detection Algorithm.. 7.5 Introduction of Other Anomaly Detection Algorithms.. 7.5.1 Spatial Filtering-Based Kernel RX Anomaly Detection Algorithm.. 7.5.2 Multiple Window Analysis-Based Kernel Detection Algorithm.. 7.6 Summary.. References.. 8 Dimensionality Reduction and Compression Technique of HSI.. 8.1 Dimensionality Reduction Technique.. 8.1.1 SVM-Based Band Selection.. 8.1.2 Application of Typical Endmember Methods-based Band Selection.. 8.1.3 Simulation Experiments.. 8.2 Compression Technique.. 8.2.1 Vector Quantization-based Compression Algorithm.. 8.2.2 Lifting Scheme-based Compression Algorithm.. 8.3 Chapter Conclusions.. References.. 9 Introduction of Hyperspectral Remote Sensing Applications.. 9.1 Agriculture.. 9.1.1 Wheat.. 9.1.2 Paddy.. 9.1.3 Soybean.. 9.1.4 Maize.. 9.2 Forest.. 9.2.1 Forest Investigation.. 9.2.2 Forest Biochemical Composition and Forest Health Status.. 9.2.3 Forest Disaster.. 9.2.4 Exotic Species Monitoring.. 9.3 Meadow.. 9.3.1 Biomass Estimation in Meadow.. 9.3.2 Grassland Species Identification9.3.3 Chemical Constituent Estimation.. 9.4 Ocean.. 9.4.1 Basic Research on Ocean Remote Sensing.. 9.4.2 Application Research on Resource and Environment Monitoring of Ocean and Coastal Zone.. 9.4.3 International Development Trend.. 9.5 Geology.. 9.5.1 Mineral Identification.. 9.5.2 Resource Exploration.. 9.6 Environment.. 9.6.1 Atmospheric Pollution Monitoring.. 9.6.2 Soil Erosion Monitoring.. 9.6.3 Water Environment Monitoring.. 9.7 Military Affairs.. References.. AppendixBased on the authors' research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.Sensores remotosImaginería hiperespectralProcesamiento de imágenesURN:ISBN:3662474557URN:ISBN:9783662474556

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