Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

Combining versatile data sets from multiple satellite sensors with advanced thematic information retrieval is a powerful way for studying complex earth systems. The book Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing offers complete understanding of the basic scientific principles needed to perform image processing, gap filling, data merging, data fusion, machine learning, and feature extraction. Written by two experts in remote sensing, the book presents the required basic concepts, tools, algorithms, platforms, and technology hubs toward advanced integration. By merging and fusing data sets collected from different satellite sensors with common features, we are enabled to utilize the strength of each satellite sensor to the maximum extent. The inclusion of machine learning or data mining techniques to aid in feature extraction after gap filling, data merging and/or data fusion further empowers earth observation, leading to confirm the whole is greater than the sum of its parts. Contemporary applications discussed in this book make all essential knowledge seamlessly integrated by an interdisciplinary manner. These case-based engineering practices uniquely illustrate how to improve such an emerging field of importance to cope with the most challenging real-world environmental monitoring issues.

Author Biography: Ni-Bin Chang is currently a professor with the Civil, Environmental, and Construction Engineering Department at the University of Central Florida. He has authored and coauthored over 230 peer-reviewed journal articles, seven books and 220 conference papers. He is a Fellow of the Royal Society of Chemistry (F.RSC) in the United Kingdom (July, 2015), the International Society of Optics and Photonics (F.SPIE) (Dec., 2014), the American Association for the Advancement of Science (F.AAAS) (Feb., 2012), the American Society of Civil Engineers (F.ASCE) (April, 2009), and a Foreign Member of the European Academy of Sciences (M.EAS) (Nov., 2008). He is also a senior member of Institute of Electrical and Electronics Engineers (IEEE) (since 2012). During Aug. 2012 and Aug. 2014, Prof. Chang has served on a number of professional and government positions including the program director of the Hydrologic Sciences Program and Cyber-innovation Sustainability Science and Engineering Program at the National Science Foundation in the US. He is currently an editor-in-chief, associate editor, or editorial board member of over 30 professional

Preface Acknowledgments Authors Chapter 1 Introduction Part I Fundamental Principles of Remote Sensing Chapter 2 Electromagnetic Radiation and Remote Sensing Chapter 3 Remote Sensing Sensors and Platforms Chapter 4 Image Processing Techniques in Remote Sensing Part II Feature Extraction for Remote Sensing Chapter 5 Feature Extraction and Classification for Environmental Remote Sensing Chapter 6 Feature Extraction with Statistics and Decision Science Algorithms Chapter 7 Feature Extraction with Machine Learning and Data Mining Algorithms Part III Image and Data Fusion for Remote Sensing Chapter 8 Principles and Practices of Data Fusion in Multisensor Remote Sensing for Environmental Monitoring Chapter 9 Major Techniques and Algorithms for Multisensor Data Fusion Chapter 10 System Design of Data Fusion and the Relevant Performance Evaluation Metrics Part IV Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning Chapter 11 Cross-Mission Data Merging Methods and Algorithms Chapter 12 Cloudy Pixel Removal and Image Reconstruction Chapter 13 Integrated Data Fusion and Machine Learning for Intelligent Feature Extraction Chapter 14 Integrated Cross-Mission Data Merging, Fusion, and Machine Learning Algorithms Toward Better Environmental Surveillance Part V Remote Sensing for Environmental Decision Analysis Chapter 15 Data Merging for Creating Long-Term Coherent Multisensor Chapter 16 Water Quality Monitoring in a Lake for Improving a Drinking Water Treatment Process Chapter 17 Monitoring Ecosystem Toxins in a Water Body for Sustainable Development of a Lake Watershed Chapter 18 Environmental Reconstruction of Watershed Vegetation Cover to Reflect the Impact of a Hurricane Event Chapter 19 Multisensor Data Merging and Reconstruction for Estimating PM Concentrations in a Metropolitan Region Chapter 20 Conclusions References Index