Improved Multiple Matching Method for Observing Glacier Motion With Repeat Image Feature Tracking

Repeat image feature tracking is commonly used to measure glacier surface motion from pairs of images, most often utilizing normalized cross correlation (NCC). The multiple-image multiple-chip (MIMC) algorithm successfully employed redundant matching (i.e., repeating the matching process over each area using varying combinations of settings) to increase the matching success rate. Due to the large number of repeat calculations, however, the original MIMC algorithm was slow and still prone to failure in areas of high shearing flow. Here, we present several major updates to the MIMC algorithm that increase both the speed and the matching success rate. First, we include additional redundant measurements by swapping the image order and matching direction; a process we term quadramatching. Second, we utilize a priori ice velocity information to confine the NCC search space through a system we term dynamic linear constraint, which substantially reduces the computation time and increases the rate of successful matches. Additionally, we develop a novel postprocessing algorithm, pseudosmoothing, to determine the most probable displacement. Our tests reveal the complementary and multiplicative nature of these upgrades in their improvement in the overall MIMC performance.