Dense Multi-Stereo Matching for High Quality Digital Elevation Models

Haala, Norbert; Rothermel, Mathias

doi:10.1127/1432-8364/2012/0121

Cited by 74 publications

(54 citation statements)

References 7 publications

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“…This, in turn, can destabilise the bundle adjustment solution and errors can propagate into the DEMs (Harwin et al, 2015). The higher the image overlap the greater the number of optical rays that intersect an object point, thereby attaining increased redundancy in point determination (Haala and Rothermel, 2012). Blurred images are caused by wind, sudden turbulence and forward motion of the UAV (Sieberth et al, 2014).…”

Section: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

“…detected corresponding points), thereby allowing the selfcalibrating bundle adjustment to converge to a stable solution that minimises the reprojection errors . These quantify the pixel differences between the initially detected corresponding points and those estimated and backprojected into the images through the SfM pipeline (Haala and Rothermel, 2012). Corresponding points with reprojection errors greater than 1.5 pixels were automatically removed to optimise the solution.…”

Section: 22mentioning

confidence: 99%

“…The disparity corresponds to the object's height perception from overlapping stereo pairs . In a stereo pair, neighbouring pixels are expected to have similar disparities (Haala and Rothermel, 2012). According to Remondino et al (2014), PhotoScan implements a method which is similar to the stereo semi-global matching algorithm (Hirschmüller, 2008).…”

Section: 22mentioning

confidence: 99%

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Accuracy Assessment of a Uav-Based Landslide Monitoring System

Peppa¹,

Mills²,

Moore³

et al. 2016

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

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ABSTRACT:Landslides are hazardous events with often disastrous consequences. Monitoring landslides with observations of high spatio-temporal resolution can help mitigate such hazards. Mini unmanned aerial vehicles (UAVs) complemented by structure-from-motion (SfM) photogrammetry and modern per-pixel image matching algorithms can deliver a time-series of landslide elevation models in an automated and inexpensive way. This research investigates the potential of a mini UAV, equipped with a Panasonic Lumix DMC-LX5 compact camera, to provide surface deformations at acceptable levels of accuracy for landslide assessment. The study adopts a selfcalibrating bundle adjustment-SfM pipeline using ground control points (GCPs). It evaluates misalignment biases and unresolved systematic errors that are transferred through the SfM process into the derived elevation models. To cross-validate the research outputs, results are compared to benchmark observations obtained by standard surveying techniques. The data is collected with 6 cm ground sample distance (GSD) and is shown to achieve planimetric and vertical accuracy of a few centimetres at independent check points (ICPs). The co-registration error of the generated elevation models is also examined in areas of stable terrain. Through this error assessment, the study estimates that the vertical sensitivity to real terrain change of the tested landslide is equal to 9 cm.

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Section: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

Section: 22mentioning

confidence: 99%

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Accuracy Assessment of a Uav-Based Landslide Monitoring System

Peppa¹,

Mills²,

Moore³

et al. 2016

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

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“…Semi global matching is the most widely used dense image matching algorithm for 3D reconstruction from aerial (Haala and Rothermel, 2012) and satellite stereo (d 'Angelo and Reinartz, 2011) imagery and is also used in applications like driver assistance system (Hermann and Klette, 2013). SGM is ranked high on KITTI (Geiger et al, 2012) and Middlebury (Scharstein and Szeliski, 2002) stereo benchmarks.…”

Section: Literature Reviewmentioning

confidence: 99%

Satellite Stereo Based Digital Surface Model Generation Using Semi Global Matching in Object and Image Space

Ghuffar¹

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents methodology and evaluation of Digital Surface Models (DSM) generated from satellite stereo imagery using Semi Global Matching (SGM) applied in image space and georeferenced voxel space. SGM is a well known algorithm, used widely for DSM generation from airborne and satellite imagery. SGM is typically applied in the image space to compute disparity map corresponding to a stereo image pair. As a different approach, SGM can be applied directly to the georeferenced voxel space similar to the approach of volumetric multi-view reconstruction techniques. The matching in voxel space simplifies the DSM generation pipeline because the stereo rectification and triangulation steps are not required. For a comparison, the complete pipeline for generation of DSM from satellite pushbroom sensors is also presented. The results on the ISPRS satellite stereo benchmark using Worldview stereo imagery of 0.5m resolution shows that the SGM applied in image space produce slightly better results than its object space counterpart. Furthermore, a qualitative analysis of the results on Worldview-3 stereo and Pleiades tri-stereo images are presented.

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“…Especially the introduction of sophisticated Dense Image Matching (DIM) techniques (Remondino et al, 2014;Haala and Rothermel, 2012;Hirschmuller, 2008) providing elevations for each image pixel has increased the achievable point density and led to surface models with a point spacing equal to the ground sampling distance (GSD) of the images (typically 5-20 cm).…”

Section: Introductionmentioning

confidence: 99%

Improved Topographic Models via Concurrent Airborne Lidar And Dense Image Matching

Mandlburger

Wenzel²,

Spitzer

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Modern airborne sensors integrate laser scanners and digital cameras for capturing topographic data at high spatial resolution. The capability of penetrating vegetation through small openings in the foliage and the high ranging precision in the cm range have made airborne LiDAR the prime terrain acquisition technique. In the recent years dense image matching evolved rapidly and outperforms laser scanning meanwhile in terms of the achievable spatial resolution of the derived surface models. In our contribution we analyze the inherent properties and review the typical processing chains of both acquisition techniques. In addition, we present potential synergies of jointly processing image and laser data with emphasis on sensor orientation and point cloud fusion for digital surface model derivation. Test data were concurrently acquired with the RIEGL LMS-Q1560 sensor over the city of Melk, Austria, in January 2016 and served as basis for testing innovative processing strategies. We demonstrate that (i) systematic effects in the resulting scanned and matched 3D point clouds can be minimized based on a hybrid orientation procedure, (ii) systematic differences of the individual point clouds are observable at penetrable, vegetated surfaces due to the different measurement principles, and (iii) improved digital surface models can be derived combining the higher density of the matching point cloud and the higher reliability of LiDAR point clouds, especially in the narrow alleys and courtyards of the study site, a medieval city.

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Dense Multi-Stereo Matching for High Quality Digital Elevation Models

Cited by 74 publications

References 7 publications

Accuracy Assessment of a Uav-Based Landslide Monitoring System

Accuracy Assessment of a Uav-Based Landslide Monitoring System

Satellite Stereo Based Digital Surface Model Generation Using Semi Global Matching in Object and Image Space

Improved Topographic Models via Concurrent Airborne Lidar And Dense Image Matching

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