Urban High-Resolution Remote Sensing. Algorithms and Modeling

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Half Title Title Page Copyright Page Table of Contents Foreword Preface Acknowledgments A Note on the Author List of Abbreviations Section I: Introduction Chapter 1: Introduction 1.1 What Is Urban Remote Sensing? 1.2 Simple Overview of the History of Urban Remote Sensing 1.3 Journals Relevant to Urban Remote Sensing 1.4 About This Book References Chapter 2: Urban Remote Sensing and Urban Studies 2.1 Characteristics of Urban Remote Sensing 2.2 Studies on Urban Remote Sensing 2.2.1 Architecture of Urban Remote Sensing 2.2.2 Topics of Studies on Urban Remote Sensing References Chapter 3: Advances in Urban Remote Sensing 3.1 Urban Remote Sensing Big Data 3.2 Characteristics of Urban Remote Sensing Big Data 3.3 Challenges in Urban Remotely Sensed Big Data Processing 3.3.1 Storage and Management of Urban Remote Sensing Big Data 3.3.2 Processing of Urban Remote Sensing Big Data 3.4 Challenges of Urban Remote Sensing Big Data 3.5 Intelligent Earth Observing Satellite System for Urban Remote Sensing 3.5.1 Change in Users’ Need from Image-Based Product to Image‑Based Information/Knowledge 3.5.2 Intelligent Earth Observing Satellite System for Urban Remote Sensing References Section II: Information Extroduction Chapter 4: Urban 3D Surface Information Extraction from Aerial Image Sequences 4.1 Introduction 4.2 Principle of Temporal and Spatial Analysis 4.3 Geometric Rectification of Image Sequences 4.4 Experiments in Spatio-Temporal Analysis 4.4.1 Image Data Processing 4.4.2 Sensitivity to Occlusion 4.4.3 Rectification of Distorted Image Sequence Data 4.4.4 DEM Generation 4.5 Error Analysis 4.6 System Development 4.7 Conclusions Appendix A Appendix B References Chapter 5: Urban 3D Surface Information Extraction from Linear Pushbroom Stereo Imagery 5.1 Introduction 5.2 Linear Array Stereo Imaging Principle with Pushbroom Scanning Technique 5.2.1 Basic Principle of Linear Array Stereo Imaging System 5.2.2 Three Typical Linear Array Stereo Imaging Systems 5.3 Mathematical Model of 3D Ground Coordinates from Linear Array Stereo Imaging System 5.3.1 Coordinate Systems 5.3.2 Interior Orientation 5.3.3 Transformation from Image to Reference Coordinate System 5.3.4 Collinearity Equations 5.3.5 Navigation Data as Exterior Orientation Parameters 5.3.6 Observation Equations 5.3.7 Adjustment Computation 5.4 Test Field Establishment 5.4.1 The High-Altitude Test Range – the First Test Field 5.4.2 The Test Field 1185 – The Second Test Field 5.5 Image Simulation of Satellite IKONOS 5.5.1 Simulation of IKONOS Imaging 5.5.2 The Brightness on Satellite Imagery 5.5.3 Error Analysis 5.6 Potential Accuracy Attainable for Ground Points of IKONOS 5.6.1 Accuracy Assessment Based on the First Test Field 5.6.2 Accuracy Assessment Based on the Second Test Field 5.7 Airborne HRSC and Space Shuttle MOMS-2P Three-Line Sensor 5.7.1 Data Processing and Accuracy Evaluation for HRSC Imaging Data 5.7.2 Data Processing and Accuracy Evaluation for MOMS-2P 5.8 Conclusions References Chapter 6: Urban 3D Building Extraction Through LiDAR and Aerial Imagery 6.1 Introduction 6.2 Principle of Aspect Code and Creation of Aspect Code Database 6.2.1 Selection of the 3D Primitives (Houses) 6.2.2 Creation of 2D Aspects 6.2.3 Coding Regulation for Aspect 6.2.4 Coding Regulation for Aspect Merging 6.2.5 Discussion of the Proposed Coding Regulation 6.2.6 Creation of Aspect Graphs 6.2.7 Creation of Aspect Codes Database 6.3 Experiments and Analyses 6.3.1 Data Set 6.3.2 Preprocessing of Aerial Imagery 6.3.3 Creation of Aspect Graphs and Extraction of Houses 6.3.4 Occlusion Analysis 6.3.5 Comparison Analysis 6.4 Conclusions References Chapter 7: Urban 3D Building Extraction from LiDAR and Orthoimages 7.1 Introduction 7.2 Building Detection and Extraction 7.2.1 Edge Detection from Orthoimage 7.2.2 Image Interpretation and Building Extraction 7.3 Digitally Modeling Buildings 7.4 Creation of Digital Surface Model (DSM) 7.4.1 Establish the Relationship Between Images and LiDAR Point Cloud Data 7.4.2 Interpolation Algorithm via Planar Equation 7.5 Creation of Digital Terrain Model (DTM) 7.6 Experiments 7.6.1 Data Set 7.6.1.1 LiDAR Data 7.6.1.2 Aerial Image Data 7.6.2 System Development 7.7 Conclusions References Chapter 8: Vehicle Extraction from High-Resolution Aerial Images 8.1 Introduction 8.2 Vehicle Detection from Aerial Imagery 8.2.1 Structure Element Identification 8.2.2 Gray-Scale Morphological Method 8.2.3 Background Estimation 8.2.4 Vehicle Detection 8.3 Experiments and Discussion 8.3.1 Data Set 8.3.2 Vehicle Extraction Results 8.3.3 Discussion 8.4 Conclusions References Chapter 9: Single Tree Canopy Extraction from LiDAR Point Cloud Data 9.1 Introduction 9.2 K-Means Clustering Watershed Algorithm 9.2.1 K-Means Clustering 9.2.2 Watershed Segmentation Combining with K-means Clustering 9.3 Validation and Analysis 9.3.1 Study Area 9.3.2 Data Preprocessing 9.3.2.1 Point Cloud Filtering 9.3.2.2 Generation of CHM 9.3.3 Treetop Detection 9.3.4 Accuracy Assessment and Comparison Analysis 9.4 Conclusions References Chapter 10: Power Lines Extraction from Aerial Images 10.1 Introduction 10.2 Power Line Extraction Method 10.2.1 Imaged Properties of Power Lines 10.2.1.1 Line Detector Mask 10.2.1.2 Ratio Line Detector 10.2.2 Power Line Pixel Detection 10.2.3 Line Segments Detecting and Grouping 10.2.3.1 Radon Transform 10.2.3.2 Line Segments Grouping 10.2.4 Kalman Filter to Track Line 10.2.5 Other Cases 10.3 Experimental Results and Analysis 10.3.1 Experimental Results 10.3.2 Discussion 10.4 Conclusions References Section III: Urban Orthophotomap Generation Chapter 11: The Basic Principle of Urban True Orthophotomap Generation 11.1 Introduction 11.2 Principle of Urban True Orthoimage Map Generation 11.2.1 Basic Steps of True Orthoimage Map Generation 11.2.2 DBM-Based Orthoimage Map Generation and Occlusion Detection 11.2.3 DTM-Based Orthoimage Map Generation 11.2.4 Near True Orthoimage Map Generation 11.2.5 Occlusion Compensation 11.3 Experiments and Analyses 11.3.1 Data Set 11.3.2 Shadow Detection and Restoration 11.3.3 Occlusion Detection and Compensation 11.3.4 Radiometric Balancing 11.3.5 True Orthoimage Map (TOM) Generation 11.4 Conclusions References Chapter 12: Orthophotomap Creation with Extremely High Buildings 12.1 Introduction 12.2 Relative Constraint for Orthorectification 12.2.1 Traditional Orthorectification Model 12.2.2 Perpendicular Control Condition 12.2.3 Collinear Constraint Condition 12.3 Experiments and Analyses 12.3.1 Experimental Data 12.3.2 Control Information 12.3.3 Constraint Line Extraction 12.3.4 Accurate Comparison 12.4 Conclusion References Chapter 13: Near Real-Time Orthophotomap Generation from UAV Video 13.1 Introduction 13.2 Mathematical Model of Georeferencing 13.2.1 Calibration of Video Camera 13.2.2 Determination of the Offset Between GPS Antenna and Camera 13.2.3 Solution of Kinematic GPS Errors 13.2.4 Estimation of Boresight Matrix 13.3 Georectification of Video Stream 13.3.1 Determination of Orthorectified Image Size 13.3.2 Orthorectification 13.3.3 Mosaicking 13.4 Experiments and Analysis 13.4.1 Experimental Field Establishment 13.4.2 UAV System 13.4.3 Data Collection 13.4.4 Bundle Adjustment of Video 13.4.5 Orthorectification and Accuracy Analysis 13.5 Conclusions References Chapter 14: Orthophotomap Generation from Satellite Imagery Without Camera Parameters 14.1 Introduction 14.2 The Second Order Polynomial Equation-Based Rectification Model Method 14.2.1 Polynomial Equation-Based Block Adjustment Model 14.2.2 Orthorectification of DISP Images 14.2.3 Data Set 14.3 Results and Accuracy Analysis 14.3.1 Image Preprocessing 14.3.2 DISP Image Orthorectification and Accuracy Analysis 14.3.2.1 DISP Image Orthorectification 14.3.2.2 Accuracy Comparison Analysis 14.3.3 Image Mosaicking 14.3.4 Radiance Balance 14.3.5 Mosaicking Result and Accuracy Evaluation 14.4 Discussions 14.5 Conclusions References Chapter 15: Building Occlusion Detection in an Urban True Orthophotomap 15.1 Introduction 15.2 The Principle of Building Occlusion Detection from Ghost Images 15.2.1 The Relationship Between Building Occlusion and Ghost Images 15.2.2 The Methodologies for Two Typical Cases 15.2.2.1 Buildings Occluding the Ground 15.2.2.2 Building Occluding Other Buildings 15.3 Experimental Results and Analysis 15.3.1 Experimental Data Set 15.3.1.1 Experimental Results and Analysis 15.3.1.2 Accuracy Comparison 15.3.1.3 Occlusion Compensation for True Orthophotomap Generation 15.4 Conclusions References Section IV: Advanced Algorithms Urban Remote Sensing Application Chapter 16: Hierarchical Spatial Features Learning for Image Classification 16.1 Introduction 16.2 The Basic Principle of HCNNs 16.2.1 Image Pyramid Product 16.2.2 Pixel-Level HSFS Extraction 16.3 Experiments and Analysis 16.3.1 Environmental Variables 16.3.2 Tract Morphological Pattern 16.3.3 Parameters 16.3.4 Pyramid Images Production for Training the HCNNs 16.3.5 Structure Details of HCNNs 16.3.6 HSFS Extraction 16.3.7 Results of Classification 16.4 Discovery of Relationship Between UHI and Various Variables 16.4.1 The Relationship Between LST and Environmental and Social Variables 16.4.2 The Relationship Between LST and Urban Landscape Metrics 16.5 Conclusions References Chapter 17: Surface Soil Moisture Retrieval from CBERS‑02B Satellite Imagery 17.1 Introduction 17.2 Model for SSM Retrieval from CBERS-02B Imagery 17.2.1 SSM Retrieval Model of Landsat TM Image 17.2.2 Spectral Radiance Relationships Between Landsat TM and CBERS-02B Images 17.2.3 Average Spectral Reflectance 17.2.4 Average Atmospheric Transmittance 17.2.5 Average Solar Radiance 17.2.6 SSM Model for Retrieval from CBERS-02B Image 17.2.7 Accuracy Evaluation Method 17.3 Experiment and Analysis 17.3.1 Test Area and Data 17.3.2 Image Preprocessing 17.3.3 SSM Retrieval and Analysis 17.4 Conclusions References Chapter 18: Measuring Control Delay at Signalized Intersections Using GPS and Video Flow 18.1 Introduction 18.2 Model for Calculating the Control Delay 18.3 Travel Time Data Collection 18.3.1 RTK GPS Technology 18.3.2 GPS Test Car Configuration 18.3.3 Field Data Collection 18.3.4 Data Preprocessing 18.4 Delay Measures with GPS at Signalized Intersections 18.4.1 Video Data Resampling 18.4.2 Interpolation for Video Recorded GPS Test Car 18.4.3 Interpolation for Other Video Recorded Vehicles 18.4.3.1 Multi-Vehicle Tracking 18.4.3.2 Interpolation of Vehicle Position 18.4.3.3 Extraction of Control Time Delay 18.4.4 Comparison of GPS-Measured and Manually Measured Stopped-Time Delays 18.5 Conclusions References Chapter 19: Measurement of Dry Asphalt Road Surface Friction Using Hyperspectral Images 19.1 Introduction 19.2 Study Area and Data Set 19.2.1 Study Area 19.2.2 Data Set 19.2.2.1 Road Condition Data 19.2.2.2 Spectral Library 19.2.2.3 Remote Sensing Data 19.3 Road Texture and Spectral Image Properties 19.3.1 Road Texture and Friction 19.3.2 Hyperspectral Images and Road Friction 19.3.2.1 Spectral Properties of Asphalt Aging 19.3.2.2 Spectral Properties of Typical Asphalt Road Distresses 19.3.2.3 Spectral Properties of Asphalt Paint 19.4 Measurement and Mapping of Skid Resistance 19.4.1 Relationship Between PCI and Friction Coefficient 19.4.2 Modeling the Relationship Between Spectral Reflectance and Skid Resistance 19.4.3 Validation of Established Model 19.5 Conclusions References Index