Multimodal Remote Sensing Fusion and Classification: Algorithms and Applications

by Man-On Pun (Editor), Xiaokang Zhang (Editor)

Overview

Multimodal 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.

Product Details

ISBN-13:	9780443291524
Publisher:	Elsevier Science
Publication date:	03/01/2026
Series:	Earth Observation
Pages:	320
Product dimensions:	7.50(w) x 9.25(h) x 0.00(d)

About the Author

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.

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.

Table of Contents

1. Understanding Multimodal Remote Sensing
2. Multimodal Data Processing
3. Fusion Techniques for Multimodal Remote Sensing
4. Multisensor Fusion
5. Classification Algorithms for Multimodal Remote Sensing
6. Change Detection and Monitoring
7. Applications in Carbon Neutrality
8. Applications in Disaster Monitoring
9. Applications in Urban Sensing for Smart Cities
10. Future Perspectives and Emerging Technologies

What People are Saying About This

From the Publisher

Provides the fundamental principles of multimodal remote sensing and applications in Earth and Environmental Sciences