Feature tracking is an efficient way to derive glacier velocity. It is based on a cross-correlation algorithm that seeks offsets of the maximal correlation windows on repeated satellite images. In this paper we demonstrate that different window sizes lead to different velocities. The
averaged velocity gradient (AVG) method is proposed to improve window sizes in feature tracking and to obtain the most suitable flow field. The AVG method measures velocity variation between adjacent windows on the whole glacier in the image. Different window sizes lead to different AVG values,
and the best-size window corresponds to the value where the AVG changes from abrupt to gradual. Using improved feature tracking, two flow fields of the same glacier are acquired with Advanced Land Observing Satellite (ALOS) optical and synthetic aperture radar (SAR) data, respectively. The
advantages, application conditions, accuracy and disadvantages of the two kinds of data using the feature tracking method are discussed.
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Document Type: Research Article
Centre for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China,Graduate University of the Chinese Academy of Sciences, Beijing, China
Centre for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China
Publication date: 2011-05-01
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