Multiple Feature Object-Based Change Detection Technology for Very High-Resolution Remote Sensing Image
Description
This book is devoted to the theory and method of the change detection on very high-resolution remote sensing image. Since the very high-resolution remote sensing images have been used in the field of land cover or land use change detection, problems such as salt and pepper effect, single spectral feature and the difficulty of discrimination of change types always affect the accuracy of information extraction by using the object-based image analysis.
This book comprehensively introduces the filed from basic theory of remote sensing change detection to various key technologies such as multi-scale image segmentation, consistency difference analysis, genetic particle swarm optimization multi feature selection, adaptive weight change vector analysis and so on. Using numerous data examples, it explains the relevant concepts and theoretical background of the object-based change detection and proposes a theoretical method and process of multi-feature change detection of very high-resolution remote sensing images. The targeted audience is advance graduate students and researchers who wish to learn about the physical process underlying the image segmentation, feature selection and change detection by using the very high-resolution remote sensing in the perspective of the object-based image analysis.
The basis of English translation of this book, originally in Chinese, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.
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Dr. Qiang Chen is an associate professor in urban remote sensing at the Beijing University of Civil Engineering and Architecture (BUCEA), China. He received his Ph.D. degree in geography and GIS from Beijing Normal University (BNU) in 2017. He has ever worked at Western University (UWO), Canada, as a visiting researcher. His main research interests include urban remote sensing, remote sensing change detection and urban construction waste monitoring. He is currently responsible for managing a project from National Key Research and Development Program of China and a project from National Natural Science Foundation of China. He has published more than 40 papers in many peer-reviewed journals as the main author or co-author, covering urban remote sensing field, as well as very high-resolution remote sensing image analysis field. He as also authored a number of books and book chapters.
Dr. Yunhao Chen is a professor in the Faculty of Geographical Sciences, Beijing Normal University, China. He received his Ph.D. degree from China University of Mining and Technology (Beijing) in 1999 and postdoctoral degree from Beijing Normal University in 2001. He has ever worked at the Department of Geography and Resource Management, The Chinese University of Hong Kong, as a visiting researcher in 2004 and worked at the national remote sensing center of Malaysia as a foreign expert in 2005. His main research interests include resource environment and urban remote sensing, hyperspectral and thermal infrared remote sensing. He is responsible for managing two projects of the National Science and Technology Support Program, five projects of the Natural Science Foundation of China and more than 20 other projects. In the past five years, he has published 2 monographs (co-authors) and obtained 9 national invention patents.
Dr. Mingyi Du is a professor of the School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture (BUCEA), China. He received his Ph.D. degree in Geodesy and Survey Engineering of China University of Mining and Technology in 2001. He has ever worked at L.N Technical University as a lecturer and an associate professor from 1984 to 2005. He is mainly engaged in the field of urban spatial information research, and his research directions are refined management of urban operations, smart cities, urban remote sensing and digitization of architectural heritage. He is currently responsible for managing a number of scientific research projects, including key projects of the National Natural Science Foundation of China, key research and development projects of the 13th Five-Year Plan, special projects of high-tech innovation centers for future urban design, etc.
Content
Introduction.- Study area data.- Generation of image object and segmentation parameter preference.- Multiple feature selection for image objects.- Multi-feature change detection of image objects.- Conclusion and Outlook.