Automatically Structuralize the Curvilinear Glacier Using Phase-Coded Convolution

The morphological structure of glaciers is essential to understand and model their dynamics. In this letter, a novel method based on phase-coded disk (PCD) and phase-coded convolution is presented to automatically delineate the morphological structure of curvilinear glaciers from ice surface velocities. First, a region-growing algorithm is used to identify the glacier image object from an ice surface velocity remote sensing product. Second, the phase-coded convolution is applied to derive the image object’s magnitude surface. A Markov chain Monte Carlo (MCMC) approach is then developed to extract the centerlines of glacier tributaries. Finally, the morphological structure and attributes of the glacier image object are numerically derived based on the centerlines. A glacier in Alaska was employed to test the proposed method and conventional hydrologic method. The results proved the effectiveness of the proposed PCD-based method. This study is a trailblazing pilot study that novelly employs computer vision technology to automatically structuralize curvilinear glaciers. The outcomes enable the inspection and monitoring of complex glacier dynamics on a tributary level.