Accuracy Evaluation of 3D Geometry from Low-Attitude UAV collections A case at Zijin Mine

Wang, Q.; Wu, Lei; Chen, S.; Shu, Defu; Xu, Zhihua; Li, Fashuai; Wang, Ruoyan

doi:10.5194/isprsarchives-xl-4-297-2014

Cited by 23 publications

(19 citation statements)

References 5 publications

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“…However, most studies to date ignore and do not acknowledge reproducibility limitations of an SfM + MVS workflow. As such, the implications of findings of many studies (Hugenholtz et al, ; Mancini et al, ; Obanawa & Hayakawa, ; Ouédraogo et al, ; Tonkin, Midgley, Graham, & Labadz, ; Wang et al, ) are limited as the conclusions are based on a single SfM + MVS model. Further work needs to be carried out to find the optimal number of replicate image datasets to describe potential variance and to find a compromise between reproducibility and computational cost.…”

Section: Discussionmentioning

confidence: 99%

Section: H1 (1) Replicated Independent Image Datasets Can Produce mentioning

confidence: 99%

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Structure from motion photogrammetry in ecology: Does the choice of software matter?

Forsmoo

Anderson

Macleod

et al. 2019

Ecology and Evolution

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Image‐based modeling, and more precisely, Structure from Motion (SfM) and Multi‐View Stereo (MVS), is emerging as a flexible, self‐service, remote sensing tool for generating fine‐grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone‐based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer‐grade camera was used to capture replicate, centimeter‐resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM‐MVS software—Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset (n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets (n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for “High,” “Medium,” and “Low” quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using “High” and “Medium” quality settings are of small overall magnitude.

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Section: Discussionmentioning

confidence: 99%

Section: H1 (1) Replicated Independent Image Datasets Can Produce mentioning

confidence: 99%

Structure from motion photogrammetry in ecology: Does the choice of software matter?

Forsmoo

Anderson

Macleod

et al. 2019

Ecology and Evolution

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“…Martin et al [9] used UAVs for mapping uranium mines. The accuracy of 3D surfaces reconstructed from UAV images was evaluated by Douterloigne et al [10] and James and Robson [11], while Wang et al [2] determined the accuracy of 3D geometry from low-attitude UAV images at the Zijin Mine in China. Fonstad et al [12] tested the utility of the structure from motion (SfM) approach using UAV imagery in a bedrock fluvial setting.…”

Section: Introductionmentioning

confidence: 99%

“…This method is low cost and has a flexible deployment [1][2][3]. UAV applications can be used to create an orthomosaic, a digital elevation model (DEM), as well as 3D point cloud and 3D terrain models.…”

Section: Introductionmentioning

confidence: 99%

Areal Change Detection and 3D Modeling of Mine Lakes Using High-Resolution Unmanned Aerial Vehicle Images

Yücel

Turan

2016

Arab J Sci Eng

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This study focuses on the Etili and Comakli openpit coal mines, where mine lakes are most densely located within the Can Coal Basin (northwestern Turkey). The aim of our study was to create 3D terrain models of these mine lakes using high-resolution images from an unmanned aerial vehicle (UAV) and to quantify areal changes linked to anthropogenic and meteorological effects over the study period. In November 2014 and October 2015, a DJI F550 hexacopter was flown at elevations of 30-100 m, and images were acquired using a 12-megapixel camera. Nineteen flights were completed, with each flight acquiring between 109 and 554 images. 3D modeling of UAV images was carried out with Agisoft software, using the most common structure from motion algorithm. Its workflow, involving image matching, georeferencing, digital elevation modeling, orthomosaics, 3D point cloud, and 3D textured model creation, was used to generate our 3D terrain model for the mine lakes. Orthomosaics with 1-6 cm/pixel resolution were used to assess areal change in the mine lakes. Area was calculated using two different methods (digitization and classification) within the ArcGIS package; results of these two methods were compared. Our UAV study of open-pit mines rapidly and precisely determined changes in topography linked to anthropogenic and meteorological effects. It also proved to be an effective method of visualizing such effects over the short term.

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“…Founded on DEM differencing and manual feature tracking, they derived the mass loss and the surface velocity of the glacier with a high spatial accuracy. Wang et al, (2014) reconstructed the 3D surface of a detritus area located at the Zijin Mine in the Fujian Province, China. They implemented different algorithms, such as the SfM system and the Patch-Based Multiview Stereo (PMVS) system, to generate a dense 3D point cloud from the UAV images.…”

Section: Point Cloud Matching Techniquesmentioning

confidence: 99%

A Robust Registration Algorithm for Point Clouds From Uav Images for Change Detection

Al‐Rawabdeh¹,

Al-Gurrani²,

Al-Durgham³

et al. 2016

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

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Commission I/Vb, ICWG I/Vb KEY WORDS: landslide dynamics, normal distance, bundle block adjustment with self-calibration, structure from motion, 3D dense surface reconstruction, Unmanned Aerial Vehicle (UAV) ABSTRACT:Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with selfcalibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs over the period of one year (i.e., May 2014 and May 2015). Note that due to the coarse accuracy of the on-board GPS receiver (e.g., +/-5-10 m) the geo-tagged positions of the images were only used as initial values in the bundle block adjustment. Normal distances, signifying detected changes, varying from 20 cm to 4 m were identified between the two epochs. The accuracy of the co-registered surfaces was estimated by comparing non-active patches within the monitored area of interest. Since these non-active sub-areas are stationary, the computed normal distances should theoretically be close to zero. The quality control of the registration results showed that the average normal distance was approximately 4 cm, which is within the noise level of the reconstructed surfaces.

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Accuracy Evaluation of 3D Geometry from Low-Attitude UAV collections A case at Zijin Mine

Cited by 23 publications

References 5 publications

Structure from motion photogrammetry in ecology: Does the choice of software matter?

Structure from motion photogrammetry in ecology: Does the choice of software matter?

Areal Change Detection and 3D Modeling of Mine Lakes Using High-Resolution Unmanned Aerial Vehicle Images

A Robust Registration Algorithm for Point Clouds From Uav Images for Change Detection

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