OPTICAL FLOW FOR GLACIER MOTION ESTIMATION
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, whic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2012-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/359/2012/isprsannals-I-3-359-2012.pdf |
| _version_ | 1818543732760248320 |
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| author | C. Vogel A. Bauder K. Schindler |
| author_facet | C. Vogel A. Bauder K. Schindler |
| author_sort | C. Vogel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T22:39:14Z |
| format | Article |
| id | doaj.art-34440d5df7014715a6d79f953a94a4cd |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-11T22:39:14Z |
| publishDate | 2012-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-34440d5df7014715a6d79f953a94a4cd2022-12-22T00:47:50ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502012-07-01I-335936410.5194/isprsannals-I-3-359-2012OPTICAL FLOW FOR GLACIER MOTION ESTIMATIONC. Vogel0A. Bauder1K. Schindler2Photogrammetry and Remote Sensing, ETH ZürichLaboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH ZürichPhotogrammetry and Remote Sensing, ETH ZürichQuantitative 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.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/359/2012/isprsannals-I-3-359-2012.pdf |
| spellingShingle | C. Vogel A. Bauder K. Schindler OPTICAL FLOW FOR GLACIER MOTION ESTIMATION ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | OPTICAL FLOW FOR GLACIER MOTION ESTIMATION |
| title_full | OPTICAL FLOW FOR GLACIER MOTION ESTIMATION |
| title_fullStr | OPTICAL FLOW FOR GLACIER MOTION ESTIMATION |
| title_full_unstemmed | OPTICAL FLOW FOR GLACIER MOTION ESTIMATION |
| title_short | OPTICAL FLOW FOR GLACIER MOTION ESTIMATION |
| title_sort | optical flow for glacier motion estimation |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/359/2012/isprsannals-I-3-359-2012.pdf |
| work_keys_str_mv | AT cvogel opticalflowforglaciermotionestimation AT abauder opticalflowforglaciermotionestimation AT kschindler opticalflowforglaciermotionestimation |