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In this paper, remote sensing data of Amery ice shelf was used to study Antarctica ice motion and flux problem by a hierarchical image matching method. It combines feature points and grid points to provide a dense, precise and reliable matching result. First, seed points are extracted at the top level of image pyramid using the SIFT algorithm with RANSAC approach to remove mismatches and enhance robustness. These points are used to construct an initial triangulation. Then, feature point and grid point matching are conducted based on the triangle constraint. In the process of hierarchical image matching, the parallaxes from upper levels are transferred to levels beneath with triangle constraint. At last, outliers are detected and removed based on local smooth constraint of parallax. Also, bidirectional image matching method is adopted to verify the matching results and increase the number of matched points. Experiments with Landsat7 images show that the proposed method has the capacity to generate reliable and dense matching results for surface velocity estimation from stereo satellite imagery. Global warming will lead to Amery shelf and glaciers melt and flow rate increase, which can be confirmed by on-site GPS and remote sensing data. Through research the ice shelf flow velocity field, the bottom can calculate the ice flux of this area, and result confirm that the impact of climate for glacier and ice shelf.

Shelf Surface Motion Estimation from Repeat Satellite Imagery