This compendium is based on more than ten years of urban remote sensing teaching experience, scientific research achievements, and the latest developments of remote sensing technology.The volume is divided into ten chapters, which describes the principles of urban remote sensing and multi-source remote sensing big data acquisition, urban remote sensing image processing methods, urban remote sensing image specific applications in related industries, and the prospect of urban remote sensing development. It summarizes the achievements on urban remote sensing projects, uses a large number of algorithm studies as intuitive materials, combines the achievements of urban remote sensing technology, and provides typical industry solutions or case studies in specific applied urban remote sensing areas.This essential reference textbook benefits undergraduate and graduate students, and anyone keen in urban remote sensing.
Author(s): Zhenfeng Shao
Series: Topics in Advanced Geoinformatics, 1
Publisher: World Scientific
Year: 2023
Language: English
Pages: 255
City: Singapore
Contents
Preface
About the Author
Chapter 1 Urban Spatio-Temporal-Spectral-Angular Observation Model
1.1 Urban Remote Sensing Observation Demand
1.1.1 The observation objects of urban remote sensing
1.1.1.1 Observation of urban land cover and land use
1.1.1.2 Observe city information at different heights from multi-angles in three-dimensional space
1.1.1.3 Monitoring the change information of urban land use and land cover on time scale
1.1.1.4 Remote sensing detection and tracking observation of time-sensitive targets in cities
1.1.1.5 Observation of social and economic activities
1.1.2 The demand for urban application services for remote sensing platform
1.1.3 The demand for sensor diversity in urban remote sensing
1.1.4 Remote sensing platform for various urban applications
1.1.4.1 Urban space remote sensing platform
1.1.4.2 Urban aerial remote sensing platform
1.1.4.3 Urban ground remote sensing platform
1.2 Multi-platform Multi-sensor Collaborative Network Observation Mode of Space-Air-Ground for Urban Service Demand
1.3 Theoretical Model of Urban Spatio-Temporal-Spectral-Angular Observation
1.4 Urban Remote Sensing Observation Services
Questions
References
Chapter 2 Big Data Characteristics of Urban Remote Sensing
2.1 Multi-source Heterogeneous Remote Sensing Big Data with Long Time Series
2.1.1 Urban satellite imagery
2.1.2 Urban aerial imagery
2.1.3 Urban UAV imagery
2.1.4 Urban mobile mapping system
2.1.5 Urban crowdsourcing images
2.2 Data Characteristics of Urban Visible Panchromatic Remote Sensing Imagery
2.2.1 Image characteristics of urban houses
2.2.2 Image characteristics of urban roads
2.2.3 Image characteristics of urban green space
2.2.4 Image characteristics of urban water bodies
2.3 Data Characteristics of Urban Multi-spectral Remote Sensing Images
2.3.1 Data characteristics of urban green space
2.3.2 Data characteristics of urban water bodies
2.4 Data Characteristics of Urban Hyperspectral Imagery
2.5 Data Characteristics of Urban Thermal Infrared Images
2.6 Data Characteristics of Urban Microwave Remote Sensing Images
2.7 Data Characteristics of Urban LiDAR Data
2.8 Data Characteristics of Urban Nighttime Light Remote Sensing Imagery
2.9 Data Characteristics of Urban Crowdsourcing Images
Questions
References
Chapter 3 Principles and Methods of Urban Remote Sensing Image Interpretation
3.1 The Task of Urban Remote Sensing Imagery Interpretation
3.2 The Objects of Urban Remote Sensing Imagery Interpretation
3.2.1 Image spatial interpretation
3.2.2 Interpretation of spectrum space
3.2.3 Interpretation of feature space
3.3 The Mechanism of Urban Remote Sensing Imagery Interpretation
3.3.1 Direct interpretation signs
3.3.1.1 Hue and color of urban features
3.3.1.2 The shape of urban features
3.3.1.3 The shadow of urban features
3.3.1.4 The texture of urban features
3.3.2 Indirect interpretation signs
3.3.2.1 Locations of urban objects
3.3.2.2 Interrelationships between urban objects
3.4 Methods of Urban Remote Sensing Imagery Interpretation
3.4.1 Visual interpretation
3.4.2 Semi-automatic interpretation
3.4.2.1 Large-scale scene interpretation of urban remote sensing images
3.4.2.2 Manually assisted interpretation of urban street scene images
3.4.2.3 City interactive interpretation guided by vector features
3.4.3 Automatic interpretation based on machine learning
3.4.4 Automatic interpretation based on deep learning
3.4.4.1 Applications in image automatic retrieval
3.4.4.2 The applications of semantic segmentation
3.4.4.3 The applications in urban object automatic detection
3.4.4.4 The applications of automatic segmentation of urban building entities
3.4.5 Interpretation of remote sensing big data
Questions
References
Chapter 4 Preprocessing Methods of Urban Remote Sensing Imagery
4.1 Cloud Detection Methods for Urban Remote Sensing Imagery
4.2 Shadow Detection Methods for Urban Remote Sensing Imagery
4.3 Image Enhancement Methods for Urban Remote Sensing Imagery
4.4 Super-Resolution Reconstruction Methods for Urban Remote Sensing Imagery
4.5 Fusion Demands of Urban Remote Sensing Imagery
4.5.1 Spatial-spectral fusion methods
4.5.2 Spatio-temporal fusion methods
Questions
References
Chapter 5 Classification and Information Extraction Methods for Urban Remote Sensing Imagery
5.1 Classification and Information Extraction Demands for Urban Remote Sensing Imagery
5.2 Unsupervised Classification Methods for Urban Remote Sensing Imagery
5.3 Supervised Classification Methods for Urban Remote Sensing Imagery
5.4 New Classification Methods for Urban Remote Sensing Imagery
5.5 Urban Road Extraction Methods Based on Remote Sensing Imagery
5.5.1 Automatic extraction method of urban roads
5.5.2 Road extraction method based on deep learning models
5.6 Urban Building Extraction Methods Based on Remote Sensing Imagery
5.7 Urban Lake Extraction Methods Based on Remote Sensing Imagery
Questions
References
Chapter 6 Urban 3D Reconstruction Methods Based on Multi-source High-Resolution Remote Sensing Imagery
6.1 Urban 3D Reconstruction Demands
6.2 Urban 3D Reconstruction Based on Stereo-Pair Image
6.3 3D Reconstruction Based on Onboard LiDAR
6.4 3D Reconstruction Based on Mobile Mapping Systems
6.5 3D Reconstruction of Urban Ancient Buildings Based on Ground LiDAR
6.6 Texture Reconstruction of Urban 3D Model
6.7 Urban 3D Reconstruction Based on Street View Data
6.8 3D Modeling Technology Based on Integrated Aerial-Ground Panoramic Imagery from Indoor and Outdoor
6.9 Quality Control Strategy for 3D Reconstruction of the Urban Scene
6.10 3D Reconstruction Management Platform of the Urban Scene
6.11 The Planning and Construction Practice of Real-Scene 3D in China
Questions
References
Chapter 7 Change Detection Methods and Applications of Urban Remote Sensing
7.1 Change Detection Demands of Urban Remote Sensing
7.1.1 Urban construction monitoring demands
7.1.2 Urban management monitoring needs
7.2 Change Detection Process of Urban Remote Sensing
7.3 Change Detection Methods of Urban Remote Sensing
7.3.1 2D change detection methods
7.3.1.1 Pixel-level change detection method
7.3.1.2 Object-level change detection method
7.3.1.3 Post-classification change detection method
7.3.1.4 Urban change detection method based on deep learning mo
7.3.2 3D change detection methods
7.4 Change Detection Practice of Urban Remote Sensing
7.4.1 Urban land cover/use change detect
7.4.2 Urban impervious surface change detection methods
7.4.3 Urban cultivated land change detection methods
Questions
References
Chapter 8 Dynamic Monitoring Methods and Applications through Urban Remote Sensing
8.1 Demands of Dynamic Urban Monitoring
8.2 Urban Dynamic Monitoring Process
8.3 Urban Dynamic Monitoring Methods
8.3.1 Urban dynamic monitoring based on time series remote sensing imagery
8.3.2 Urban dynamic monitoring based on the integration of earth observation sensor network
8.4 Remote Sensing Monitoring Applications of Urban Natural Resources
8.4.1 Remote sensing monitoring of urban lake changes
8.4.2 Remote sensing monitoring of urban impervious surface
8.4.3 Remote sensing monitoring of urban cultivated land
8.5 Remote Sensing Monitoring of Urban Planning
8.6 Remote Sensing Monitoring of Urban Conditions
8.6.1 Difficulties of remote sensing monitoring of urban situations
8.6.2 Urban area extraction method based on progressive learning models
8.7 Remote Sensing Monitoring of Urban Geological Hazards
Questions
References
Chapter 9 Remote Sensing Monitoring Technology and Practice of Urban Ecological Environment
9.1 Remote Sensing Monitoring Demands of Urban Ecological Environment
9.1.1 The concept of urban heat island and the remote sensing monitoring demands of the urban thermal environment
9.1.2 Application demands for remote sensing inversion of urban aboveground biomass
9.2 Remote Sensing Monitoring Methods and Practice of Urban Heat Island Effect
9.3 Remote Sensing Inversion and Application of Urban Aboveground Biomass
9.4 Innovative Models and Methods in Remote Sensing Quantitative Monitoring of Cyanobacteria from Urban Lakes with Eutrophication
Questions
References
Chapter 10 Prospects of Urban Remote Sensing Technologies and Applications
10.1 The Developing Directions of Urban Remote Sensing Sensors
10.2 New Application Fields of Urban Remote Sensing
10.3 The Future Directions of Urban Remote Sensing
Questions
References
Index
