Analysis of Different Methods for 3D Reconstruction of Natural Surfaces from Parallel‐Axes <scp>UAV</scp> Images

Eltner, Anette; Schneider, D.

doi:10.1111/phor.12115

Cited by 133 publications

(149 citation statements)

References 43 publications

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“…However, recent studies have revealed the presence of systematic errors in the automatic SfM workflow (Eltner and Schneider, 2015;Harwin et al, 2015;James and Robson, 2014;Sieberth et al, 2014). These errors have been found to originate from various sources such as low overlap, blurry images, flight configuration, number and distribution of GCPs, as well as the various geometric camera models used in the different SfM software.…”

Section: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

“…This was found to be particularly relevant in the case of low-cost cameras and in the absence of GCPs. The unresolved distortion might form bowlshape systematic patterns that were recognisable either in the undistorted images (Eltner and Schneider, 2015) or in the vertical error distributions at ICPs (James and Robson, 2014).…”

Section: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

“…The same study also highlighted that evenly distributed GCPs should be included into the bundle adjustment in order to reduce the aforementioned systematic patterns. Furthermore, Eltner and Schneider (2015) investigated Agisoft PhotoScan, a popular commercial SfM software, and demonstrated that the geometric camera model was unable to entirely resolve the lens distortion of a Panasonic Lumix DMC-LX3 camera. This was found to be particularly relevant in the case of low-cost cameras and in the absence of GCPs.…”

Section: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

“…Bundle adjustment solved for (a) the interior orientation camera parameters (IOP), i.e. focal length, sensor size, radial and tangential distortion coefficients for the entire photogrammetric block, and (b) the relative orientation and translation of each image (Eltner and Schneider, 2015;Remondino et al, 2014). The image alignment step was undertaken in Agisoft PhotoScan (version 1.2.3) (PhotoScan, 2016).…”

Section: Dem Generation and Elevation Difference Determinationmentioning

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: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

Section: Suitability Of Uavs For Monitoring Purposesmentioning

confidence: 99%

Section: Dem Generation and Elevation Difference Determinationmentioning

confidence: 99%

See 2 more Smart Citations

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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“…This task is often referred to as registration, and is conventionally dependent on establishing ground control points (GCPs) which are fixed by a higher-order control method (usually Global Navigation Satellite System-GNSS or Global Positioning System). A number of studies have examined the effect of GCP networks (number and distribution) in UAS surveys, showing that significant errors are expected in SfM-based products where GCPs are not adopted [39,40]. Nevertheless, systematic DEM error can Remote Sens.…”

Section: Preflight Planningmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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