編號 : 2018106
項目名稱 : Spatiotemporal Fusion of Multisource Remote Sensing Data
講員 : 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
時間 : 7:00 pm - 8:30 pm
進修時間 : 1.5
地點 : Lecture Hall N003, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
主辦單位 : 土地測量組
截止日期 : 09/05/2018
費用 : HK$120 for members; HK$150 for non-members (HK$30 walk-in surcharge on all prices listed)
名額 : LSD members; First-come-first-served
語言 : English
詳情 : 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.