Optical Flow for Glacier Motion Estimation

Vogel, Christoph; Bauder, Andreas; Schindler, Konrad

doi:10.5194/isprsannals-i-3-359-2012

Cited by 24 publications

(16 citation statements)

References 17 publications

(21 reference statements)

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“…In our case, an interval of 1 week is a good balance between expected motion (approximately 5-7 m total), the resolution of the camera and limited change in the appearance of surface texture between the image acquisitions. ImGRAFT uses the NCC algorithm as a measure of template similarity, which generally performs well (Heid and Kääb, 2012) but other measures such as phase correlation and optical flow analysis have been suggested in the literature (Ahn and Box, 2010;Ahn and Howat, 2011;Heid and Kääb, 2012;Vogel et al, 2012). We hope to include more templatematch methods in future versions of ImGRAFT.…”

Section: Template Matchingmentioning

confidence: 99%

Image georectification and feature tracking toolbox: ImGRAFT

Messerli

Grinsted

2015

Geosci. Instrum. Method. Data Syst.

127

117

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Abstract. The use of time-lapse camera systems is becoming an increasingly popular method for data acquisition. The camera setup is often cost-effective and simple, allowing for a large amount of data to be accumulated over a variety of environments for relatively minimal effort. The acquired data can, with the correct post-processing, result in a wide range of useful quantitative and qualitative information in remote and dangerous areas. The post-processing requires a significant amount of steps to transform images into meaningful data for quantitative analysis, such as velocity fields. To the best of our knowledge at present a complete, openly available package that encompasses georeferencing, georectification and feature tracking of terrestrial, oblique images is still absent. This study presents a complete, yet adaptable, opensource package developed in MATLAB, that addresses and combines each of these post-processing steps into one complete suite in the form of an "Image GeoRectification and Feature Tracking" (ImGRAFT: http://imgraft.glaciology.net) toolbox. The toolbox can also independently produce other useful outputs, such as viewsheds, georectified and orthorectified images. ImGRAFT is primarily focused on terrestrial oblique images, for which there are currently limited post-processing options available. In this study, we illustrate ImGRAFT for glaciological applications on a small outlet glacier Engabreen, Norway.

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Section: Template Matchingmentioning

confidence: 99%

Image georectification and feature tracking toolbox: ImGRAFT

Messerli

Grinsted

2015

Geosci. Instrum. Method. Data Syst.

127

117

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“…Optical flow is a method which can estimate the displacement up to individual pixel level [22,23], attempts of exploiting this novel approach have been made over glaciers, but yet without satisfying results [24]. The lack of successful attempts stems from the condition within the optical flow formulation that the illumination and albedo should not change over time, otherwise artificial movement is introduced.…”

Section: Image Matchingmentioning

confidence: 99%

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Altena

Kääb

2017

Remote Sensing

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Abstract:Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources with variable qualities, from dispersed time instances, or with different spatial resolutions. Consequently, terrain representation used for orthorectifying satellite images is often in disagreement with reality at image acquisition. Normally, the lateral orthoprojection offsets resulting from vertical DEM errors are taken into account in the geolocation error budget of the corrected images, or may even be neglected. The largest offsets of this type are often found over glaciers, as these may show strong elevation changes over time and thus large elevation errors in the reference DEM with respect to image acquisition. The detection and correction of such orthorectification offsets is further complicated by ice flow which adds a second offset component to the displacement vectors between orthorectified data. Vice versa, measurement of glacier flow is complicated by the inherent superposition of ice movement vectors and orthorectification offset vectors. In this study, we try to estimate these orthorectification offsets in the presence of terrain movement and translate them to elevation biases in the reference surface. We demonstrate our method using three different sites which include very dynamic glaciers. For the Oriental Glacier, an outlet of the Southern Patagonian icefield, Landsat 7 and 8 data from different orbits enabled the identification of trends related to elevation change. For the Aletsch Glacier, Swiss Alps, we assess the terrain offsets of both Landsat 8 and Sentinel-2A: a superior DEM appears to be used for Landsat in comparison to Sentinel-2, however a systematic bias is observed in the snow covered areas. Lastly, we demonstrate our methodology in a pipeline structure; displacement estimates for the Helheim-glacier, in Greenland, are mapped and corrected for orthorectification offsets between data from different orbits, which enables a twice as dense a temporal resolution of velocity data, as compared to the standard method of measuring velocities from repeat-orbit data only. In addition, we introduce and implement a novel matching method which uses image triplets. By formulating the three image displacements as a convolution, a geometric constraint can be exploited. Such a constraint enhances the reliability of the displacement estimations. Furthermore the implementation is simple and computationally swift.

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“…The biggest errors correspond to periods of large lighting changes, such as shown in the 10, 21 and 24 pairs. These changes are related to the presence of snow cover and no uniform melting on the glacier surface as mentioned Vogel et al (2012). Figure 8A shows an example of the error in the case of changing lighting conditions, resulting in significant errors in the computation of LDOF.…”

Section: Results: Motion Detection Processesmentioning

confidence: 99%

“…Few studies of ice motion have been carried out using optical flow algorithm (Vogel et al, 2012;Bown, 2015). Thus, in this work, we propose the LDOF algorithm to estimate the motion The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016 XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic of a glacier by terrestrial monoscopic time-lapse image series acquired by non-metric professional DSLR camera systems.…”

Section: Introductionmentioning

confidence: 99%

Optical Flow Applied to Time-Lapse Image Series to Estimate Glacier Motion in the Southern Patagonia Ice Field

Lannutti

Lenzano

Toth

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this work, we assessed the feasibility of using optical flow to obtain the motion estimation of a glacier. In general, former investigations used to detect glacier changes involve solutions that require repeated observations which are many times based on extensive field work. Taking into account glaciers are usually located in geographically complex and hard to access areas, deploying time-lapse imaging sensors, optical flow may provide an efficient solution at good spatial and temporal resolution to describe mass motion. Several studies in computer vision and image processing community have used this method to detect large displacements. Therefore, we carried out a test of the proposed Large Displacement Optical Flow method at the Viedma Glacier, located at South Patagonia Icefield, Argentina. We collected monoscopic terrestrial time-lapse imagery, acquired by a calibrated camera at every 24 hour from April 2014 until April 2015. A filter based on temporal correlation and RGB color discretization between the images was applied to minimize errors related to changes in lighting, shadows, clouds and snow. This selection allowed discarding images that do not follow a sequence of similarity. Our results show a flow field in the direction of the glacier movement with acceleration in the terminus. We analyzed the errors between image pairs, and the matching generally appears to be adequate, although some areas show random gross errors related to the presence of changes in lighting. The proposed technique allowed the determination of glacier motion during one year, providing accurate and reliable motion data for subsequent analysis.

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Optical Flow for Glacier Motion Estimation

Cited by 24 publications

References 17 publications

Image georectification and feature tracking toolbox: ImGRAFT

Image georectification and feature tracking toolbox: ImGRAFT

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Optical Flow Applied to Time-Lapse Image Series to Estimate Glacier Motion in the Southern Patagonia Ice Field

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