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
1.1.2 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 ouтeapplications
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 withLong 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
3.3.2 Indirect interpretation signs
3.4 Methods of Urban Remote Sensing Imagery Interpretation
3.4.1 Visual interpretation
3.4.2 Semi-automatic interpretation
3.4.3 Automatic interpretation based on machine learning
3.4.4 Automatic interpretation based on deep learning
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 SensingImagery
Question
