Ni-Bin Chang, Kaixu Bai

Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book rests upon a smooth integration between image fusion and data mining for information retrieval and content-based mapping in the context of different environmental applications, and it focuses on environmental application issues at global and regional scale, while using local scale ground-truth data for calibration and validation.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 528
• Erscheinungstermin: 21. Februar 2018
• Englisch
• ISBN-13: 9781498774345
• Artikelnr.: 53071685

Autorenporträt

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

Inhaltsangabe

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.