Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

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

Introduction. Part I Fundamental Principles of Remote Sensing. Electromagnetic Radiation and Remote Sensing. Remote Sensing Sensors and Platforms. Image Processing Techniques in Remote Sensing. Part II Feature Extraction for Remote Sensing. Feature Extraction and Classification for Environmental Remote Sensing.Feature Extraction with Statistics and Decision Science Algorithms. Feature Extraction with Machine Learning and Data Mining Algorithms. Part III Image and Data Fusion for Remote Sensing. Principles and Practices of Data Fusion in Multisensor Remote Sensing for Environmental Monitoring. Major Techniques and Algorithms for Multisensor Data Fusion. System Design of Data Fusion and the Relevant Performance Evaluation Metrics. Part IV Integrated Data Merging, Data Reconstruction. Data Fusion, and Machine Learning. Cross-Mission Data Merging Methods and Algorithm. Cloudy Pixel Removal and Image Reconstruction. Integrated Data Fusion and Machine Learning for Intelligent Feature Extraction. Integrated Cross-Mission Data Merging, Fusion, and Machine Learning Algorithms Toward Better Environmental Surveillance. Part V Remote Sensing for Environmental Decision Analysis. Data Merging for Creating Long-Term Coherent Multisensor. Water Quality Monitoring in a Lake for Improving a Drinking Water Treatment Process. Monitoring Ecosystem Toxins in a Water Body for Sustainable Development of a Lake Watershed. Environmental Reconstruction of Watershed Vegetation Cover to Reflect the Impact of a Hurricane Event. Multisensor Data Merging and Reconstruction for Estimating PM Concentrations in a Metropolitan Region.Conclusions.