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 Usersβ Need from Image-Based Product to ImageβBased 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 β the First Test Field
5.4.2 The Test Field 1185 β 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 CBERSβ02B 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