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2020年7月3日 2:28:44

简介
专业评核试/技术评核试
学会持续专业发展项目
其他持续专业发展项目
 
编号 : 2018106
项目名 : Spatiotemporal Fusion of Multisource Remote Sensing Data
学会持续专业发展编号 : Formal Events
讲师 : Dr. Zhu Xiaolin, Assistant Professor, Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University.

Dr. Zhu Xiaolin received his B.S. in 2007 and M.S. in 2010, both from Beijing Normal University. He received his Ph.D. in geography at Ohio State University in 2014. Before coming to PolyU, he was a postdoctoral scholar in the Center of Spatial Technologies and Remote Sensing at the University of California, Davis. His research interests include remote sensing methods and applications. Dr Zhu published more than 30 peer-reviewed journal articles. He was awarded prestigious awards, including the Presidential Fellowship from Ohio State University, and the Robert N. Colwell Memorial Fellowship Award from the American Society of Photogrammetry and Remote Sensing.
日期 : 16/05/2018
时间 : 7:00 pm - 8:30 pm
持续进修小时 : 1.5
有关组别之资格预审前研习小时 : 1.5
地点 : Lecture Hall N003, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
部门 : 土地测量组
截止日期 : 09/05/2018
费用 : HK$120 for members; HK$150 for non-members (HK$30 walk-in surcharge on all prices listed)
名额 : LSD members; First-come-first-served
语言 : English
详情 : Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies experience rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular since the last decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this presentation will categorize existing methods, discuss the principal laws underlying these methods, summarize their potential applications, and propose possible directions for future studies in this field.