Using Multiple Photographs and USGS LiDAR to Improve Photogrammetric Accuracy

Terpstra, Toby; Dickinson, Jordan; Hashemian, Alireza

doi:10.4271/2018-01-0516

Cited by 9 publications

(2 citation statements)

References 40 publications

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“…The 2017 images required very little adjustment, but the corrections were essential for the 2015 GoPro images, which had strong barrel (or "fisheye" distortion). More complex and sophisticated lens correction techniques exist [41,54,[115][116][117][118] but Adobe's lens profiles have been tested and recommended in the photogrammetric literature [64,[119][120][121][122]. The quality of Lightroom's corrections are evident in before-and-after images ( Figure 6), and in the quality of the final model.…”

Section: Image Optimisation and Distortion Correctionmentioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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Repeat photogrammetry is increasingly the go-too tool for long-term geomorphic monitoring, but quantifying the differences between structure-from-motion (SfM) models is a developing field. Volumetric differencing software (such as the open-source package CloudCompare) provides an efficient mechanism for quantifying change in landscapes. In this case study, we apply this methodology to coastal boulder deposits on Inishmore, Ireland. Storm waves are known to move these rocks, but boulder transportation and evolution of the deposits are not well documented. We used two disparate SfM data sets for this analysis. The first model was built from imagery captured in 2015 using a GoPro Hero 3+ camera (fisheye lens) and the second used 2017 imagery from a DJI FC300X camera (standard digital single-lens reflex (DSLR) camera); and we used CloudCompare to measure the differences between them. This study produced two noteworthy findings: First, volumetric differencing reveals that short-term changes in boulder deposits can be larger than expected, and that frequent monitoring can reveal not only the scale but the complexities of boulder transport in this setting. This is a valuable addition to our growing understanding of coastal boulder deposits. Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47]. CloudCompare is a cross-platform 3D point cloud and mesh processing package, which provides a suite of features for direct comparison of dense point clouds (>10 million points) and meshes on standard consumer computer hardware [48,49]. Accessibility and ease of use make CloudCompare an attractive tool, and it is applied increasingly in a variety of fields, including mapping [50,51], archaeology [52,53], and geomorphology [54][55][56][57].Another issue relevant to long term monitoring studies is how to deal with repeat imagery captured using different kinds of hardware, with different resolutions and/or distortion levels. The ultra-wide-angle (fisheye) lenses mounted on the popular consumer camera brand GoPro capture a~120 • field of view, but have barrel distortion due to their short focal length. Despite their limitations, GoPro cameras are popular tools for acquiring UAV imagery [24,31,[58][59][60], they are relatively inexpensive, are light enou...

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Section: Image Optimisation and Distortion Correctionmentioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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“…Most researchers have achieved superior results in the combination of measurement techniques (Liu et al., 2023; Markiewicz et al., 2022; Stothard & Shirani Faradonbeh, 2023). Additionally, some have enhanced the accuracy of low‐precision lidar through the utilisation of photogrammetric techniques (Li et al., 2019; Rzonca & Twardowski, 2022; Yuan et al., 2022), or conversely, improved photogrammetric accuracy through different lidar applications (Terpstra et al., 2018, 2019). The challenge persists in merging data from both techniques to enhance monitoring accuracy, particularly when the monitoring precision of both techniques is within the centimetre range (within 10 cm).…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of surface deformation monitoring in a mining area based on UAV‐lidar and UAV photogrammetry

Zhan,

Zhang,

Wang

et al. 2024

The Photogrammetric Record

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Unmanned aerial vehicle light detection and ranging (UAV‐lidar) and unmanned aerial vehicle (UAV) photogrammetry are currently commonly used surface monitoring technologies. Previous studies have used the two technologies interchangeably and ignored their correlation, or only compared them on a single product. However, there are few quantitative assessments of the differences between these two techniques in monitoring surface deformation and prediction of their application prospects. Therefore, the paper compared the differences between the digital elevation model (DEM) and subsidence basins obtained by the two techniques using Gaussian analysis. The results indicate that the surface DEMs obtained by both the techniques exhibit a high degree of similarity. The statistical analysis of the difference values in the z direction between the two DEMs follows a Gaussian distribution with a standard deviation of less than 0.36 m. When comparing the surface subsidence values monitored by the two techniques, it was found that UAV‐lidar was more sensitive to small‐scale deformation, with a difference range of 0.23–0.44 m compared to photogrammetry. The conclusion provides valuable information regarding the utilisation of multisource monitoring data.

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Reconstruction of 3D Accident Sites Using USGS LiDAR, Aerial Images, and Photogrammetry

Terpstra¹,

Dickinson²,

Hashemian³

et al. 2019

SAE Technical Paper Series

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Using Multiple Photographs and USGS LiDAR to Improve Photogrammetric Accuracy

Cited by 9 publications

References 40 publications

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Comparative analysis of surface deformation monitoring in a mining area based on UAV‐lidar and UAV photogrammetry

Reconstruction of 3D Accident Sites Using USGS LiDAR, Aerial Images, and Photogrammetry

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