What Is Hyperspectral Analysis?

InSAR photogrammetry involves multiple specialized knowledge such as radar, signal processing, image processing, interference processing, navigation, and photogrammetry, and is a highly comprehensive emerging discipline. This book is a summary of the research results of the topographic mapping processing system technology project of the high-efficiency aerial SAR remote sensing application system project in the "Eleventh Five-Year 863" earth observation and navigation technology field. The main contents of this book include an introduction to airborne InSAR photogrammetry, basic principles of SAR, basic principles of InSAR, SAR and InSAR imaging processing, airborne InSAR photogrammetric coordinate system, InSAR conformation equation, airborne InSAR motion compensation, and airborne InSAR system interference processing. 2. Calibration of airborne InSAR photogrammetry system, analysis of airborne InSAR photogrammetry error, airborne InSAR mapping software system, etc.

Hyperspectral Image Analysis and Application

"Hyperspectral Image Analysis and Application" is a summary of the research results on the topographic mapping processing system technology topic of the "Eleventh Five-Year 863" high-efficiency aerial SAR remote sensing application system project in the field of earth observation and navigation technology. The main content of "Analysis and Application of Hyperspectral Imagery" includes the introduction of airborne InSAR photogrammetry, the basic principles of SAR, the basic principles of InSAR, SAR and InSAR imaging processing, the airborne InSAR photogrammetric coordinate system, the InSAR conformation equation, the airborne InSAR motion compensation, Interference processing of airborne InSAR system, calibration of airborne InSAR photogrammetry system, analysis of airborne InSAR photogrammetry, software system of airborne InSAR mapping, etc.

Introduction to Earth Observation and Navigation Technology Series Preface Chapter 1 Introduction 1.1 Hyperspectral Remote Sensing Technology in Earth Observation System 1.2 Hyperspectral Remote Sensing and Geospatial Information Acquisition 1.3 Hyperspectral Image Processing and Analysis Chapter 2 Spectral Characteristics And detection requirements 2.1 Spectral characteristics of vegetation 2.1.1 Basic characteristics of vegetation spectrum 2.1.2 Characteristic parameters of vegetation spectrum 2.1.3 Factors affecting spectral characteristics of vegetation 2.1.4 Differences between green paint and vegetation spectrum 2.2 Spectral characteristics of soil and rock 2.2 .1 Spectral characteristics of soil 2.2.2 Spectral characteristics of rocks 2.3 Spectral characteristics of artificial ground features 2.3.1 Spectral characteristics of materials on the top of buildings 2.3.2 Spectral characteristics of road paving materials 2.4 Spectral characteristics of terrestrial water bodies 2.4.1 Clean water bodies 2.4.2 The effect of sand content on the reflection characteristics of water bodies 2.4.3 The effect of chlorophyll concentration on the reflection characteristics of water bodies 2.4.4 The spectral reflection characteristics of water bodies at different depths 2.4.5 The spectral reflection characteristics of snow 2.5 The elements of the sea Spectral Characteristics 2.5.1 Spectral Characteristics of Seawater 2.5.2 Spectral Characteristics of Coastal Vegetation 2.5.3 Spectral Characteristics of Coastal Bedrock and Tidal Flat 2.6 Requirement for detection of spectral attributes of ground features 2.6.1 Requirements for detection of vegetation 2.6.2 Requirements for detection of soil and rocks 2.6.3 Requirements for detection of artificial features 2.6.4 Requirements for detection of terrestrial water bodies and glaciers 2.6.5 Requirements for detection of sea elements Chapter Hyperspectral Imaging System 3.1 Hyperspectral Remote Sensing Imaging Mechanism 3.1.1 Optical Detection 3.1.2 Spatial Scanning 3.1.3 Spectral Spectroscopy 3.2 Development Status of Imaging Spectrometer 3.2.1 Foreign Imaging Spectrometer System 3.2.2 Domestic Imaging Spectrometer System 3.3 Imaging Spectrometer Calibration 3.3.1 Spectral calibration 3.3.2 Radiation calibration 3.3.3 Geometric calibration 3.4 Characteristics of hyperspectral remote sensing data 3.4.1 Cube structure 3.4.2 Data description model Chapter 4 Hyperspectral image correction technology 4.1 Solar radiation and atmosphere Transmission characteristics 4.1.1 Solar radiation 4.1.2 The influence of the atmosphere on the electromagnetic wave transmission process 4.1.3 Radiation transmission equation 4.2 Radiation errors of hyperspectral images 4.2.1 Radiation errors caused by the sensitivity characteristics of sensors 4.2.2 Radiation caused by differences in lighting conditions Error 4.2.3 Radiation error caused by different atmospheric conditions 4.3 Radiation correction based on calibration parameters 4.3.1 Radiation correction parameter acquisition 4.3.2 Image radiation correction Method 4.4 Correction of Atmospheric Radiation of Hyperspectral Imagery 4.4.1 Correction of Atmospheric Radiation Based on Radiation Transfer Theory 4.4.2 Reflectivity Inversion Using Image Data 4.4.3 Method of Spectral Reflectance Using Special Ground Objects 4.5 Geometric Characteristics of Hyperspectral Imagery 4.5.1 Geometric imaging model 4.5.2 Geometric distortion of the image 4.6 Geometric correction of hyperspectral image 4.6.1 General method of geometric correction 4.6.2 POS-based geometric correction Chapter 5 Feature database technology 5.1 Overview 5.1.1 Feature spectrum The concept of the database 5.1.2 The status and role of the ground feature spectrum database 5.1.3 The construction process of the ground feature spectrum database 5.2 The status quo of the research on the spectral database 5.2.1 The status quo of the research on foreign spectral databases 5.2.2 The research progress of the domestic ground feature spectral database 5.3 The ground feature spectrum Database system design 5.3.1 Analysis of system application requirements 5.3.2 System design principles 5.3.3 System content design 5.3.4 System structure design 5.3.5 System function design 5.4 Obtaining spectral data 5.4.1 Laboratory spectral measurement 5.4.2 Ground Spectral Measurement 5.4.3 Remote Sensing Image Extraction Chapter 6 Spectral Feature Analysis and Matching 6.1 Spectral Feature Enhancement and Quantitative Analysis 6.1.1 Light Feature enhancement method 6.1.2 Spectral feature parameter 6.2 Spectral similarity measure 6.2.1 Geometric space measure 6.2.2 Probability space measure 6.2.3 Transformation space measure 6.2.4 Comprehensive similarity measure 6.2.5 Classification test 6.3 Spectrum matching technology 6.3 .1 Code matching 6.3.2 Spectral angle matching 6.3.3 Cross-correlation spectral matching 6.3.4 Matched filtering technology 6.4 Scale space matching technology 6.4.1 Scale space theory 6.4.2 Peak feature extraction 6.4.3 Matching algorithm 6.5 Decision tree matching classification 6.5.1 Decision tree classification method 6.5.2 Hierarchical analysis model of spectral matching 6.5.3 Application examples Chapter 7 Statistical pattern classification of hyperspectral images 7.1 Principle of pattern classification of hyperspectral images 7.1.1 Concepts and methods of pattern recognition 7.1.2 Statistical pattern recognition general process 7.2 Bayes statistical decision classification 7.2.1 Basic decision rules 7.2.2 Maximum likelihood classification under normal distribution 7.3 Bayes non-parametric decision classification 7.3.1 Fisher linear discriminant method 7.3.2 Fisher discriminant function Training 7.3.3 Fisher piecewise linear discriminant function 7.4 Cluster analysis and unsupervised classification 7.4.1 Clustering criteria 7.4.2 K-means clustering 7.4.3 ISODATA clustering 7.4.4 Kernel-based Constructed dynamic clustering method 7.5 Artificial neural network classification 7.5.1 Multilayer perceptron 7.5.2 BP algorithm 7.5.3 Radial basis function network 7.5.4 Kohonen network Chapter 8 Spectral feature selection and extraction 8.1 High-dimensional spectral feature analysis Basic 8.1.1 Sample distribution of high-dimensional feature space 8.1.2 "Dimensional disaster" phenomenon 8.1.3 Correlation analysis between bands 8.2 Criteria for class separability 8.2.1 Basic characteristics 8.2.2 Criteria for distance between classes within a class 8.2.3 Probabilistic distance criterion 8.2.4 Information entropy criterion 8.3 Feature extraction based on class separability 8.3.1 Feature extraction by intra-class distance criterion 8.3.2 Feature extraction by probabilistic distance criterion 8.3.3 Features by information entropy criterion Extraction 8.4 Feature extraction based on information compression 8.4.1 Principal component analysis 8.4.2 Noise separation transform 8.5 Independent component analysis feature extraction 8.5.1 Model estimation method 8.5.2 Fast ICA algorithm 8.6 Projection pursuit feature extraction 8.6.1 Projection index 8.6 .2 Hyperspectral Image Feature Extraction Based on PP 8.7 Nonlinear Feature Extraction Methods Chapter 9 Hyperspectral Image Kernel Method Analysis 9.1 Kernel Function and Kernel Method Principle 9.1.1 Kernel Function 9.1.2 Kernel Method 9.2 Statistical Learning Theory and Support Machine 9.2.1 Statistical Learning Theory 9.2.2 Support Vector Machine 9.3 Support Vector Machine Classification 9.3.1 Fast Training Algorithm 9.3.2 Multi-Class Classifier Construction 9.3.3 Kernel Function and Parameter Selection 9.4 Kernel Fisher Discriminant Classification 9.4.1 Fisher Discriminant Analysis 9.4.2 Kernel Fisher Discriminant Analysis 9.4.3 Kernel Fisher Discriminant Classification 9.5 Correlation Vector Machine Classification 9.5.1 Sparse Bayes Model 9.5.2 Model Parameter Inference 9.5.3 Correlation Vector Machine Classification 9.6 Nonlinear Feature Extraction 9.6.1 Kernel Principal Components Analysis 9.6.2 Pursuit of Kernel Pitch Distance Projection 9.6.3 Generalized Discriminant Analysis Chapter 10 Mixed Pixel Decomposition 10.1 Overview 10.1.1 Significance of Mixed Pixel Decomposition 10.1.2 Mixed Pixel Decomposition Process 10.2 Spectral Mixing Model 10.2.1 Causes of mixed spectrum 10.2.2 Linear mixed model 10.2.3 Nonlinear mixed model 10.2.4 Random mixed model 10.3 End element number estimation 10.3.1 NPD algorithm 10.3.2 Orthogonal subspace projection method 10.4 End element extraction technology 10.4. 1 Typical end element extraction algorithm 10.4.2 End element extraction technology assisted by spatial information 10.4.3 End element extraction algorithm based on particle swarm optimization 10.5 Spectral demixing technology 10.5.1 Supervised decomposition algorithm 10.5.2 Unsupervised decomposition algorithm 11 chapter Hyperspectral and high spatial resolution image fusion 11.1 Overview 11.1.1 Pixel level fusion 11.1.2 Feature level fusion 11.1.3 Decision level fusion 11.2 Fusion preprocessing 11.2.1 Radiation correction 11.2.2 Geometric correction 11.2.3 Image registration 11.3 Hyperspectral and high spatial resolution image fusion algorithm 11.3.1 General pixel level fusion algorithm 11.3.2 General pixel level fusion algorithm characteristic analysis 11.3.3 Fusion algorithm based on non-negative matrix factorization 11.3.4 Fusion method based on genetic algorithm 11.3. 5 Spatial domain fusion method based on image spectral restoration 11.3.6 Fusion algorithm based on mixed pixel decomposition 11.3.7 Edge information-based spectral information preservation fusion algorithm 11.4 Fusion effect evaluation 11.4.1 Subjective evaluation method 11.4.2 Objective evaluation method 11.4.3 Comprehensive Evaluation Methods Chapter 12 Design of Hyperspectral Data Processing System 12.1 Current Status Analysis of Hyperspectral Data Processing System 12.1.1 Introduction of Foreign Hyperspectral Data Processing System 12.1.2 Introduction of Domestic Hyperspectral Data Processing System 12.2 Structure of Hyperspectral Data Processing System Design 12.2.1 Hyperspectral image data structure 12.2.2 Data processing flow design 12.2.3 System architecture design 12.3 High Functional design of spectral data processing system 12.3.1 Image data preprocessing module 12.3.2 Attribute information classification and extraction module 12.3.3 Data fusion module 12.4 Key technologies of hyperspectral data processing and their implementation 12.4.1 Hyperspectral image geometric correction technology 12.4.2 High-dimensional spectral feature compression and extraction technology 12.4.3 Hyperspectral and high spatial resolution data fusion processing technology 12.4.4 High-precision classification and extraction technology 12.5 Hyperspectral remote sensing image analysis software system 12.5.1 Hyperspectral image memory display 12.5. 2 Hyperspectral image pre-processing 12.5.3 Hyperspectral image feature analysis 12.5.4 Hyperspectral image classification and identification 12.5.5 Reference map of ground feature database