Multimodal Remote Sensing Fusion and Classification
Algorithms and Applications
- 1st Edition - October 1, 2025
- Imprint: Elsevier
- Editors: Man-On Pun, Xiaokang Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 5 2 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 5 3 - 1
Multimodal Remote Sensing Data Fusion for Classification: Algorithms and Applications offers a comprehensive overview of Earth observation data fusion, focusing on multim… Read more
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Request a sales quoteMultimodal Remote Sensing Data Fusion for Classification: Algorithms and Applications offers a comprehensive overview of Earth observation data fusion, focusing on multimodal remote sensing. It presents state-of-the-art algorithms and practical applications that enhance understanding of Earth's dynamic processes. Through detailed analysis, case studies, and practical examples, this book equips readers with the necessary tools to effectively utilize multimodal data fusion for land cover and land use classification, as well as environmental monitoring, making it an invaluable resource for those in remote sensing and Earth sciences.
Furthermore, the book is tailored for Masters and Doctorate students, scientists, and professionals in remote sensing, geography, and Earth sciences. It delves into the integration and analysis of multimodal remote sensing data, offering insights into sustainable solutions for environmental challenges. This comprehensive coverage ensures readers are well-versed in the cutting-edge techniques and methodologies required for advanced Earth observation and classification tasks.
Furthermore, the book is tailored for Masters and Doctorate students, scientists, and professionals in remote sensing, geography, and Earth sciences. It delves into the integration and analysis of multimodal remote sensing data, offering insights into sustainable solutions for environmental challenges. This comprehensive coverage ensures readers are well-versed in the cutting-edge techniques and methodologies required for advanced Earth observation and classification tasks.
- Provides a holistic overview of Multimodal Remote Sensing, from data acquisition, preprocessing, fusion techniques, analysis methodologies, and diverse applications
- Includes real-world case studies and examples, showcasing the application of multimodal remote sensing in various fields
- Emphasizes future perspectives and emerging technologies, providing readers with forward-thinking applications and their potential impact on the field
Masters or Doctorate level students, scientists and professionals in remote sensing, geography and Earth sciences.
1. Understanding Multimodal Remote Sensing
1.1 What is Remote Sensing?
1.2 Multimodal Remote Sensing: An Overview
2. Multimodal Data Processing
2.1 Data Enhancement
2.2 Multimodal Data Registration and Alignment
3. Fusion Techniques for Multimodal Remote Sensing
3.1 Pixel-Level Fusion Methods
3.2 Feature-Level Fusion Methods
3.3 Decision-Level Fusion Methods
4. Multisensor Fusion
4.1 Fusion of Optical and SAR Data
4.2 Fusion of LiDAR and Hyperspectral Data
4.3 Fusion of Thermal Infrared and Multispectral Data
5. Classification Algorithms for Multimodal Remote Sensing
5.1 Deep Learning-based Remote Sensing Classification
5.2 Weakly Supervised Learning Approaches
5.3 Self-Supervised Learning Approaches
5.4 Semi-Supervised and Transfer Learning Approaches
6. Change Detection and Monitoring
6.1 Deep Learning-based Change Detection Methodologies
6.2 Heterogeneous Change Detection
7. Applications in Carbon Neutrality
7.1 Forest Monitoring
7.2 Coastal wetlands monitoring
8. Applications in Disaster Monitoring
8.1 Flood Monitoring
8.2 Landslide Mapping
8.3 Forest Fire Mapping
9. Applications in Urban Sensing for Smart Cities
9.1 Urban Growth Monitoring and Expansion Analysis
9.2 Assessment and mapping of urban green spaces
9.3 Urban Functional Zone Recognition
10. Future Perspectives and Emerging Technologies
10.1 New potentials of machine learning for earth observation
10.2 High-performance cloud computing for large scale monitoring.
10.3 Digital Twin Technologies
1.1 What is Remote Sensing?
1.2 Multimodal Remote Sensing: An Overview
2. Multimodal Data Processing
2.1 Data Enhancement
2.2 Multimodal Data Registration and Alignment
3. Fusion Techniques for Multimodal Remote Sensing
3.1 Pixel-Level Fusion Methods
3.2 Feature-Level Fusion Methods
3.3 Decision-Level Fusion Methods
4. Multisensor Fusion
4.1 Fusion of Optical and SAR Data
4.2 Fusion of LiDAR and Hyperspectral Data
4.3 Fusion of Thermal Infrared and Multispectral Data
5. Classification Algorithms for Multimodal Remote Sensing
5.1 Deep Learning-based Remote Sensing Classification
5.2 Weakly Supervised Learning Approaches
5.3 Self-Supervised Learning Approaches
5.4 Semi-Supervised and Transfer Learning Approaches
6. Change Detection and Monitoring
6.1 Deep Learning-based Change Detection Methodologies
6.2 Heterogeneous Change Detection
7. Applications in Carbon Neutrality
7.1 Forest Monitoring
7.2 Coastal wetlands monitoring
8. Applications in Disaster Monitoring
8.1 Flood Monitoring
8.2 Landslide Mapping
8.3 Forest Fire Mapping
9. Applications in Urban Sensing for Smart Cities
9.1 Urban Growth Monitoring and Expansion Analysis
9.2 Assessment and mapping of urban green spaces
9.3 Urban Functional Zone Recognition
10. Future Perspectives and Emerging Technologies
10.1 New potentials of machine learning for earth observation
10.2 High-performance cloud computing for large scale monitoring.
10.3 Digital Twin Technologies
- Edition: 1
- Published: October 1, 2025
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443291524
- eBook ISBN: 9780443291531
MP
Man-On Pun
Man-On Pun is presently an Associate Professor at the School of Science and Engineering, CUHKSZ. Previously, he served as a Post-Doctoral Research Associate at Princeton University in Princeton, NJ, USA, from 2006 to 2008. He also held research positions at Huawei in Milford, NJ, USA, the Mitsubishi Electric Research Labs (MERL) in Boston, MA, USA, and Sony in Tokyo, Japan.
His research encompasses artificial intelligence (AI), Internet of Things (IoT), and the application of machine learning in communications and satellite remote sensing. Prof. Pun has been recognized with best paper awards from the IEEE Vehicular Technology Conference 2006 Fall, the IEEE International Conference on Communication 2008, and the IEEE Infocom'09. Additionally, he has taken on the role of Founding Chair for the IEEE Joint Signal Processing Society-Communications Society Chapter in Shenzhen and served as an Associate Editor for the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS from 2010 to 2014.
Affiliations and expertise
Associate Professor, School of Science and Engineering, CUHKSZ, Hong KongXZ
Xiaokang Zhang
Prof. Xiaokang Zhang is currently a specially-appointed Professor with the School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan. He has authored or coauthored more than 20 scientific publications in international journals and conferences. His research interests include remote sensing image analysis, computer vision, and deep learning. Dr. Zhang serves as a Reviewer for more than 10 renowned international journals, such as the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Information Fusion, and the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING.
Affiliations and expertise
Professor, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China