Abstract
Abstract. Quantitative measurements of glacier flow over time are an important ingredient for glaciological research, for example to determine the mass balances and the evolution of glaciers. Measuring glacier flow in multi-temporal images involves the estimation of a dense set of corresponding points, which in turn define the flow vectors. Furthermore glaciers exhibit rather difficult radiometry, since their surface usually contains homogeneous areas as well as weak texture and contrast. To date glacier flow is usually observed by manually measuring a sparse set of correspondences, which is labor-intensive and often yields rather irregular point distributions, with the associated problems of interpolating over large areas. In the present work we propose to densely compute motion vectors at every pixel, by using recent robust methods for optic flow computation. Determining the optic flow, i.e. the dense deformation field between two images of a dynamic scene, has been a classic, long-standing research problem in computer vision and image processing. Sophisticated methods exist to optimally balance data fidelity with smoothness of the motion field. Depending on the strength of the local image gradients these methods yield a smooth trade-off between matching and interpolation, thereby avoiding the somewhat arbitrary decision which discrete anchor points to measure, while at the same time mitigating the problem of gross matching errors. We evaluate our method by comparing with manually measured point wise ground truth.
Highlights
It has become common practice to use photogrammetric tools to acquire and analyze the motion of glaciers
We evaluate the different optical flow methods on ortho-photos of the Unteraargletscher located in the Bernese Alps, Switzerland
This large valley glacier has two main tributary forming the common tongue with an area of about 23 km2 and a length of 13 km
Summary
It has become common practice to use photogrammetric tools to acquire and analyze the motion of glaciers. The corresponding points in the images, which define the glacier motion, are still often measured manually. In this work we show how to automate the process by applying optical flow techniques to the problem. Optical flow algorithms have advanced to a state at which they can densely compute motion vectors at every pixel despite the difficult radiometry of glaciers. In this work we compare several methods on a data set with available manual measurements and perform a thorough evaluation using different error metrics. The evaluation shows that in areas where the glacier surface is properly visible modern optical flow methods are competitive with human observers
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