scholarly journals Mining Ground Surface Information Extraction and Topographic Analysis Using UAV Video Data

2020 ◽  
Vol 194 ◽  
pp. 05030
Author(s):  
Yin Yaqiu ◽  
Jiang Cunhao ◽  
Lv Jing ◽  
Wang Jie ◽  
Ju Xing ◽  
...  
Keyword(s):  
Land Surface ◽  
Image Data ◽  
Video Data ◽  
Shanxi Province ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Topographic Analysis ◽  
Ground Control Points ◽  
Data Source

Taking the Xiangwang bauxite mining of Xiaoyi City, Shanxi Province as the research object, the DJi “Wu”inspire2 model Unmanned aerial vehicle (UAV) was used to obtain the video data, image data and Ground control points (GCP) data of a typical pit in the study area. Based on the two kinds of data source (video data and image data), the Digital surface model (DSM) of the research area was acquired with or without ground control points through aerial triangulation and block adjustment. Using the DSM obtained by the two data source, the distribution of elevation, slope, slope direction, surface fluctuation and surface roughness was extracted and compared. Research shows that the DSM, acquired by the ContextCapture software without GCP, using video data obtained by aerial shooting around one interest point, can qualitatively reflect the topographic distribution of the land surface. The DSM got by the video data with the GCP can achieve the similar accuracy with the result obtained by image data, and the topographic information acquired by the two kinds of data source has highly similar characteristics in spatial and numerical distribution. It can be concluded through comparison and analysis of the topographical factors that steep slopes with complex topography and large elevation difference distributes in the northwest-central of the pit, of which northwest and southwest slopes can be easily eroded by wind and rain, so attention should be paid to slop stability monitoring and disaster prevention in this area. As a whole, the results show that video data obtained by UAV can not only reflect the dynamic changes of the land surface qualitatively, but also can describe the distribution of surface topography quantitatively through processing to get the DSM. It has great application potential in the field of disaster emergency monitoring and geological hazard risk assessment in mining areas.

scholarly journals CORRECTION OF MOBILE MAPPING TRAJECTORIES IN GNSS-DENIED ENVIRONMENTS USING AERIAL NADIR AND AERIAL OBLIQUE IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1649-1654
Author(s):  
P. Fanta-Jende ◽  
F. Nex ◽  
M. Gerke ◽  
J. Lijnen ◽  
G. Vosselman
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Image Data ◽  
City Centre ◽  
Ground Control ◽  
Control Points ◽  
Mobile Mapping ◽  
Ground Control Points ◽  
Before And After ◽  
Oblique Images

<p><strong>Abstract.</strong> Mobile mapping enables highly accurate as well as high-resolution image data capture at low cost and high speed. As a terrestrial acquisition technique predominately employed in urban, and thus built-up areas, non-line-of-sight and multipath effects challenge its absolute positioning capabilities provided by GNSS. In conjunction with IMU drift, the platform’s trajectory has an unknown accuracy, which influences the quality of the data product. By employing a highly accurate co-registration technique for identifying tie correspondences between mobile mapping images and aerial nadir as well as aerial oblique images, reliable ground control can be introduced into an adjustment solution. We exemplify the performance of our registration results by showcasing adjusted mobile mapping trajectories in four different test areas, each with about 100 consecutive recording locations (approx. 500&amp;thinsp;m length) in the city centre of Rotterdam, The Netherlands. The mobile mapping data has been adjusted in different configurations, i.e. with nadir or oblique aerial correspondences only and if possible in conjunction. To compare the horizontal as well as the vertical accuracy before and after the respective adjustments, more than 30 ground control points were surveyed for these experiments. In general, the aim of our technique is not only to correct mobile mapping trajectories in an automated fashion but also to verify their accuracy without the need to acquire ground control points. In most of our test cases, the overall accuracy of the mobile mapping image positions in the trajectory could be improved. Depending on the test area, an RMSE in 3D between 15 and 21&amp;thinsp;cm and an RMSE in 2D between 11 and 18&amp;thinsp;cm is achievable.</p>

scholarly journals Geometric Accuracy Study of Orthorectification Based on Sensor Model Refinement in Imagery Subset Using ORFEO Toolbox (OTB)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andri Suprayogi ◽  
Nurhadi Bashit
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Geometric Accuracy ◽  
Model Refinement ◽  
Full Extent ◽  
Sensor Model ◽  
Ground Control Points

Large scale base map can be obtained by various methods, one of them is orthorectification process of remote sensing satellite imagery to eliminate the relief displacement caused by height variation of earth surface. To obtain a  map images with good quality,  it requires additional data such as sensor model in the form of rational polynomial coefficients (RPC), surface model data, and ground control points Satellite imageries with high resolution  file size are relatively large.  In order to process them,  high specification of hardwares were required. To overcome this by cutting only a portion of the images, based on certain study areas were suffer from of georeference lost so it would not be able to orthorectified. On the other hand,  in several remote sensing software such as ESA SNAP and Orfeo Toolbox (OTB)  subset or pixel extraction from satellite imagery,  preserve the imagery geometric sensor models. This research aimed at geometric accuracy of orthorectification carried out in a single scene of Pleiades Imagery within the Kepahiang Subdistrict, located at Kepahiang Regency, Bengkulu Province, by using DEMNAS and the imagery refined sensor mode, and ground control points taken using GPS Survey. Related with the raw imagery condition which consists of Panchromatic and multispectral bands, this study were separated to assembly, pan sharpening , and sensor model refinement stages prior to orthorectification carried out both in the original or full extent imagery and the result of subset extent imagery. After  these processses taken place, we measure the accuracy of each full and subset imagery.These procedures were carried out using Orfeo toolbox 6.6.0 in the Linux Mint 19 Operating system. From the process log, running time in total  were 7814.518  second for the full extent and 4321.95 seconds for the subset processess. And as a big data process, the total of full extent imageries was 83.15 GB  while the subset size  was  only 30.73 GB.  The relative accuracy of the full extent and its subset imagery were 0.431 meters. Accuracy of the  sensor model refinement process are  1.217 meters and 1.550 meters with GCP added, while the accuracu of  the orthorectifications results were  0.416 meters and 0.751 meters by using ICP.  Variation of execution time may caused by the data input size and complexity of the mathematical process carried out in each stages. Meanwhile,  the variation of accuracy may  caused by the check or control points placements above satellite Imagery which suffer from uncertainty when dealing with  the sub-pixel position or under 0.5 meters.

scholarly journals UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 413-418 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
H. Moayedi ◽  
A. A. Halin ◽  
...  
Keyword(s):  
Image Registration ◽  
Water Resource Management ◽  
Feature Matching ◽  
Image Data ◽  
Ground Control ◽  
Control Points ◽  
Speeded Up Robust Features ◽  
Ground Control Points ◽  
Edge Detectors ◽  
Edge Features

<p><strong>Abstract.</strong> Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.</p>

scholarly journals Accuracy assessment of open - pit mine’s digital surface models generated using photos captured by Unmanned Aerial Vehicles in the post - processing kinematic mode

2021 ◽  
Vol 62 (4) ◽  
pp. 38-47
Author(s):  
Long Quoc Nguyen ◽  
Keyword(s):  
Accuracy Assessment ◽  
Open Pit ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Post Processing ◽  
Ground Control Points ◽  
Satellite Positioning ◽  
Surface Models ◽  
Aerial Vehicle

To evaluate the accuracy of the digital surface model (DSM) of an open-pit mine produced using photos captured by the unmanned aerial vehicle equipped with the post-processing dynamic satellite positioning technology (UAV/PPK), a DSM model of the Deo Nai open-pit coal mine was built in two cases: (1) only using images taken from UAV/PPK and (2) using images taken from UAV/PPK and ground control points (GCPs). These DSMs are evaluated in two ways: using checkpoints (CPs) and comparing the entire generated DSM with the DSM established by the electronic total station. The obtained results show that if using CPs, in case 1, the errors in horizontal and vertical dimension were 6.8 and 34.3 cm, respectively. When using two or more GCPs (case 2), the horizontal and vertical errors are at the centimetre-level (4.5 cm and 4.7 cm); if using the DSM comparison, the same accuracy as case 2 was also obtained.

scholarly journals Accuracy of DSM By Using Unmanned Aerial Vehicles on the Downstream of Welang Riverbank, District of Pasuruan, Jawa Timur

UKaRsT ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 49
Author(s):  
Dian Wahyu Khaulan ◽  
Entin Hidayah ◽  
Gusfan Halik
Keyword(s):  
High Resolution ◽  
Vertical Direction ◽  
Sufficient Accuracy ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Digital Surface Model ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Topography Data

The Digital Surface Model (DSM) is commonly used in studies on flood map modeling. The lack of accurate, high-resolution topography data has hindered flood modeling. The use of the Unmanned Aerial Vehicle (UAV) can help data acquisition with sufficient accuracy. This research aims to provide high-resolution DSM-generated maps by Ground Control Points (GCPs) settings. Improvement of the model's accuracy was pursued by distributing 20 GCPs along the edges of the study area. Agrisoft software was used to generate the DSM. The generated DSM can be used for various planning purposes. The model's accuracy is measured in Root Mean Square Error (RMSE) based on the generated DSM. The RMSE values are 0.488 m for x-coordinates and y-coordinates (horizontal direction) and 0.161 m for z-coordinates (vertical direction).

scholarly journals The Evaluation of GPS techniques for UAV-based Photogrammetry in Urban Area

2016 ◽  
Vol XLI-B1 ◽  
pp. 1079-1084 ◽  
Author(s):  
M. L. Yeh ◽  
Y. T. Chou ◽  
L. S. Yang
Keyword(s):  
Land Use ◽  
Unmanned Aerial Vehicle ◽  
Three Dimensional ◽  
High Mobility ◽  
Urban Land Use ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Ground Control Points ◽  
Aerial Vehicle

The efficiency and high mobility of Unmanned Aerial Vehicle (UAV) made them essential to aerial photography assisted survey and mapping. Especially for urban land use and land cover, that they often changes, and need UAVs to obtain new terrain data and the new changes of land use. This study aims to collect image data and three dimensional ground control points in Taichung city area with Unmanned Aerial Vehicle (UAV), general camera and Real-Time Kinematic with positioning accuracy down to centimetre. The study area is an ecological park that has a low topography which support the city as a detention basin. A digital surface model was also built with Agisoft PhotoScan, and there will also be a high resolution orthophotos. There will be two conditions for this study, with or without ground control points and both were discussed and compared for the accuracy level of each of the digital surface models. According to check point deviation estimate, the model without ground control points has an average two-dimension error up to 40 centimeter, altitude error within one meter. The GCP-free RTK-airborne approach produces centimeter-level accuracy with excellent to low risk to the UAS operators. As in the case of the model with ground control points, the accuracy of x, y, z coordinates has gone up 54.62%, 49.07%, and 87.74%, and the accuracy of altitude has improved the most.

scholarly journals Smartphone: An alternative to ground control points for orienting virtual outcrop models and assessing their quality

Geosphere ◽  
2019 ◽  
Vol 15 (6) ◽  
pp. 2043-2052 ◽  
Author(s):  
Stefano Tavani ◽  
Amerigo Corradetti ◽  
Pablo Granado ◽  
Marco Snidero ◽  
Thomas D. Seers ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Image Data ◽  
Ground Control ◽  
Control Points ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Test Models ◽  
Surface Reconstructions ◽  
The Cost ◽  
Range Error

Abstract The application of structure from motion–multiview stereo (SfM-MVS) photogrammetry to map metric- to hectometric-scale exposures facilitates the production of three-dimensional (3-D) surface reconstructions with centimeter resolution and range error. In order to be useful for geospatial data interrogation, models must be correctly located, scaled, and oriented, which typically requires the geolocation of manually positioned ground control points with survey-grade accuracy. The cost and operational complexity of portable tools capable of achieving such positional accuracy and precision is a major obstacle in the routine deployment of SfM-MVS photogrammetry in many fields, including geological fieldwork. Here, we propose a procedure to overcome this limitation and to produce satisfactorily oriented models, which involves the use of photo orientation information recorded by smartphones. Photos captured with smartphones are used to: (1) build test models for evaluating the accuracy of the method, and (2) build smartphone-derived models of outcrops, used to reference higher-resolution models reconstructed from image data collected using digital single-lens reflex (DSLR) and mirrorless cameras. Our results are encouraging and indicate that the proposed workflow can produce registrations with high relative accuracies using consumer-grade smartphones. We also find that comparison between measured and estimated photo orientation can be successfully used to detect errors and distortions within the 3-D models.

Assessment of UAV-based digital surface model and the effects of quantity and distribution of ground control points

2020 ◽  
Vol 42 (1) ◽  
pp. 65-83
Author(s):  
Guilherme Gomes Pessoa ◽  
André Caceres Carrilho ◽  
Gabriela Takahashi Miyoshi ◽  
Amilton Amorim ◽  
Mauricio Galo
Keyword(s):  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Digital Surface Model ◽  
Ground Control Points

scholarly journals PRECISE AERIAL IMAGE ORIENTATION USING SAR GROUND CONTROL POINTS FOR MAPPING OF URBAN LANDMARKS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 61-66
Author(s):  
F. Kurz ◽  
T. Krauß ◽  
H. Runge ◽  
D. Rosenbaum ◽  
P. d’Angelo
Keyword(s):  
Image Data ◽  
Bundle Adjustment ◽  
Aerial Image ◽  
Ground Control ◽  
Data Sets ◽  
Control Points ◽  
Ground Control Points ◽  
Autonomous Cars ◽  
New Applications ◽  
Image Orientation

<p><strong>Abstract.</strong> Highly precise ground control points, which are globally available, can be derived from the SAR satellite TerraSAR-X. This opens up many new applications like for example the precise aerial image orientation. In this paper, we propose a method for precise aerial image orientation using spaceborne geodetic Synthetic Aperture Radar Ground Control Points (SAR-GCPs). The precisely oriented aerial imagery can then be used e.g. for mapping of urban landmarks, which support the ego-positioning of autonomous cars. The method for precise image orientation was validated based on two aerial image data sets. SAR-GCPs were measured in images, then the image orientation has been improved by a bundle-adjustment. Results based on check points show, that the accuracy of the image orientation is better than 5&amp;thinsp;cm in X and Y coordinates.</p>

Structure-from-Motion applied to historical aerial photographs: parameter variability and application on landslide cyclonic evolution on France's La Réunion Island, Indian Ocean

2021 ◽  
Author(s):  
Thomas JB Dewez ◽  
Claire Rault ◽  
Bertrand Aunay
Keyword(s):  
Indian Ocean ◽  
Structure From Motion ◽  
Focal Length ◽  
Aerial Photographs ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Ground Control Points ◽  
Check Points ◽  
Correlated Parameters

&lt;p&gt;Geographical Surveys now distribute online their historical aerial photographs. The batches of digital images, holding the appearance and relief of the forever gone landscape, can be processed with automated Structure-from-Motion (SFM) photogrammetric pipelines. Are the results trustworthy? In this communication, we report the results of exploratory tests performed with Agisoft Metashape on sets of 1978, ~1/27.000, vertical aerial photographs from IGN-France over la R&amp;#233;union volcanic island in the Indian Ocean. Georeferencing deliberately used ground control points and check points collected on IGN's web mapping portal. Validation was obtained from lidar and photogrammetric acquisition of 2015.&lt;/p&gt;&lt;p&gt;First, our results show that scanned photographs do not strictly map camera coordinates to image coordinates from one file to the next. Photos are slightly shifted and rotated on each scan. The photogrammetric assumption of a single camera per batch of images is thus violated. A preprocessing step, automated with Python, locates fiducials, computes camera principal point, rotates and crops the image file to a unique image reference frame. This feature is absent from Agisoft Metashape when fiducial coordinates are unknown.&lt;/p&gt;&lt;p&gt;Second, in the photogrammetric pipeline, camera calibration parameters are deduced from matched sparse points. The sensitivity of the &quot;align&quot; function was explored. The smallest RMS errors were &amp;#177;7.03m for 11 ground-control points and &amp;#177;5.45m for 9 independent check points when setting Align quality to &quot;high&quot; and a 4-parameters camera model using focal length (f), eccentricity (cx, cy), one radial distortion parameter (K1). A higher number of parameters delivered no accuracy improvement and correlated parameters. Intensive random sampling of sparse points subsets conducted to stable estimates of focal length and eccentricity. Improving the robustness of focal length determination would require additional, oblique photographs, which was not the spirit of historical survey design and were never acquired in past surveys.&lt;/p&gt;&lt;p&gt;Third, collecting ground control points on https://geoportail.gouv.fr resulted in digital surface model elevation accuracy within +/- 3.34m (Median Absolute Deviation). Validation was computed on a 2015 lidar digital terrain model at 5m resolution on stable grounds. Scanning artefacts, probably due to variable scanning velocity of the digitizing head, introduced elevation variation stripes in Difference of DEM (DoD), parallel to the scanner direction. This pattern limits the detection of geomorphologically meaningful&lt;strong&gt; &lt;/strong&gt;differences.&lt;/p&gt;&lt;p&gt;Fourth, a DoD between 2015-1978 for the Cirque de Salazie, in the north-east of La R&amp;#233;union Island, highlighted landsliding masses active some time during the last 37 years and 13 cyclones. Beyond this proof of concept, archive aerial photographs in La R&amp;#233;union go back until 1949 and covered the island twenty times. This time scale offers a welcome hindsight when producing landslide risk mitigation maps.&lt;/p&gt;&lt;p&gt;This work was published in open-access in&lt;/p&gt;&lt;p&gt;Rault, C., Dewez, T. J. B., and Aunay, B., 2020, Structure-from-Motion processing of aerial archive photographs: sensitivity analyses pave the way for quantifying geomorphological changes since 1978 in la R&amp;#233;union island, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 773&amp;#8211;780, https://doi.org/10.5194/isprs-annals-V-2-2020-773-2020, 2020.&lt;/p&gt;

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