scholarly journals DETECTION OF SLOPE MOVEMENT BY COMPARING POINT CLOUDS CREATED BY SFM SOFTWARE

2016 ◽  
Vol XLI-B5 ◽  
pp. 553-556 ◽  
Author(s):  
Kazuo Oda ◽  
Satoko Hattori ◽  
Toko Takayama
Keyword(s):  
Detection Method ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
Dense Point ◽  
Initial Setting ◽  
Point To Point ◽  
Precision Test ◽  
Loose Rock

This paper proposes movement detection method between point clouds created by SFM software, without setting any onsite georeferenced points. SfM software, like Smart3DCaputure, PhotoScan, and Pix4D, are convenient for non-professional operator of photogrammetry, because these systems require simply specification of sequence of photos and output point clouds with colour index which corresponds to the colour of original image pixel where the point is projected. SfM software can execute aerial triangulation and create dense point clouds fully automatically. This is useful when monitoring motion of unstable slopes, or loos rocks in slopes along roads or railroads. Most of existing method, however, uses mesh-based DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. And in some cases movement is smaller than precision of ground control points and registering two point clouds with GCP is not appropriate. Change detection method in this paper adopts CCICP (Classification and Combined ICP) algorithm for registering point clouds before / after movement. The CCICP algorithm is a type of ICP (Iterative Closest Points) which minimizes point-to-plane, and point-to-point distances, simultaneously, and also reject incorrect correspondences based on point classification by PCA (Principle Component Analysis). Precision test shows that CCICP method can register two point clouds up to the 1 pixel size order in original images. Ground control points set in site are useful for initial setting of two point clouds. If there are no GCPs in site of slopes, initial setting is achieved by measuring feature points as ground control points in the point clouds before movement, and creating point clouds after movement with these ground control points. When the motion is rigid transformation, in case that a loose Rock is moving in slope, motion including rotation can be analysed by executing CCICP for a loose rock and background slope independently.

scholarly journals DETECTION OF SLOPE MOVEMENT BY COMPARING POINT CLOUDS CREATED BY SFM SOFTWARE

2016 ◽  
Vol XLI-B5 ◽  
pp. 553-556 ◽  
Author(s):  
Kazuo Oda ◽  
Satoko Hattori ◽  
Toko Takayama
Keyword(s):  
Detection Method ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
Dense Point ◽  
Initial Setting ◽  
Point To Point ◽  
Precision Test ◽  
Loose Rock

This paper proposes movement detection method between point clouds created by SFM software, without setting any onsite georeferenced points. SfM software, like Smart3DCaputure, PhotoScan, and Pix4D, are convenient for non-professional operator of photogrammetry, because these systems require simply specification of sequence of photos and output point clouds with colour index which corresponds to the colour of original image pixel where the point is projected. SfM software can execute aerial triangulation and create dense point clouds fully automatically. This is useful when monitoring motion of unstable slopes, or loos rocks in slopes along roads or railroads. Most of existing method, however, uses mesh-based DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. And in some cases movement is smaller than precision of ground control points and registering two point clouds with GCP is not appropriate. Change detection method in this paper adopts CCICP (Classification and Combined ICP) algorithm for registering point clouds before / after movement. The CCICP algorithm is a type of ICP (Iterative Closest Points) which minimizes point-to-plane, and point-to-point distances, simultaneously, and also reject incorrect correspondences based on point classification by PCA (Principle Component Analysis). Precision test shows that CCICP method can register two point clouds up to the 1 pixel size order in original images. Ground control points set in site are useful for initial setting of two point clouds. If there are no GCPs in site of slopes, initial setting is achieved by measuring feature points as ground control points in the point clouds before movement, and creating point clouds after movement with these ground control points. When the motion is rigid transformation, in case that a loose Rock is moving in slope, motion including rotation can be analysed by executing CCICP for a loose rock and background slope independently.

scholarly journals Determining the Optimal Number of Ground Control Points for Varying Study Sites through Accuracy Evaluation of Unmanned Aerial System-Based 3D Point Clouds and Digital Surface Models

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 49 ◽  
Author(s):  
Jae Jin Yu ◽  
Dong Woo Kim ◽  
Eun Jung Lee ◽  
Seung Woo Son
Keyword(s):  
Image Processing ◽  
Point Clouds ◽  
Optimal Number ◽  
Ground Control ◽  
Control Points ◽  
Widespread Application ◽  
Ground Control Points ◽  
3D Point Clouds ◽  
Surface Models ◽  
Study Sites

The rapid development of drone technologies, such as unmanned aerial systems (UASs) and unmanned aerial vehicles (UAVs), has led to the widespread application of three-dimensional (3D) point clouds and digital surface models (DSMs). Due to the number of UAS technology applications across many fields, studies on the verification of the accuracy of image processing results have increased. In previous studies, the optimal number of ground control points (GCPs) was determined for a specific area of a study site by increasing or decreasing the amount of GCPs. However, these studies were mainly conducted in a single study site, and the results were not compared with those from various study sites. In this study, to determine the optimal number of GCPs for modeling multiple areas, the accuracy of 3D point clouds and DSMs were analyzed in three study sites with different areas according to the number of GCPs. The results showed that the optimal number of GCPs was 12 for small and medium sites (7 and 39 ha) and 18 for the large sites (342 ha) based on the overall accuracy. If these results are used for UAV image processing in the future, accurate modeling will be possible with minimal effort in GCPs.

scholarly journals Reconstructing 3D coastal cliffs from airborne oblique photographs without ground control points

2014 ◽  
Vol II-5 ◽  
pp. 113-116 ◽  
Author(s):  
T. J. B. Dewez
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Open Door ◽  
View Point ◽  
Ground Control Points ◽  
3D Point Clouds ◽  
Coastal Cliff ◽  
Coastal Cliffs

Coastal cliff collapse hazard assessment requires measuring cliff face topography at regular intervals. Terrestrial laser scanner techniques have proven useful so far but are expensive to use either through purchasing the equipment or through survey subcontracting. In addition, terrestrial laser surveys take time which is sometimes incompatible with the time during with the beach is accessible at low-tide. By comparison, structure from motion techniques (SFM) are much less costly to implement, and if airborne, acquisition of several kilometers of coastline can be done in a matter of minutes. In this paper, the potential of GPS-tagged oblique airborne photographs and SFM techniques is examined to reconstruct chalk cliff dense 3D point clouds without Ground Control Points (GCP). The focus is put on comparing the relative 3D point of views reconstructed by Visual SFM with their synchronous Solmeta Geotagger Pro2 GPS locations using robust estimators. With a set of 568 oblique photos, shot from the open door of an airplane with a triplet of synchronized Nikon D7000, GPS and SFM-determined view point coordinates converge to X: ±31.5 m; Y: ±39.7 m; Z: ±13.0 m (LE66). Uncertainty in GPS position affects the model scale, angular attitude of the reference frame (the shoreline ends up tilted by 2°) and absolute positioning. Ground Control Points cannot be avoided to orient such models.

scholarly journals Short Communication: A simple workflow for robust low-cost UAV-derived change detection without ground control points

2019 ◽  
Author(s):  
Kristen L. Cook ◽  
Michael Dietze
Keyword(s):  
Change Detection ◽  
Low Cost ◽  
Point Clouds ◽  
Standard Approach ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
3D Point Clouds ◽  
Tectonically Active ◽  
Comparative Accuracy

Abstract. High quality 3D point clouds generated from repeat camera-equipped unmanned aerial vehicle (UAV) surveys are increasingly being used to investigate landscape changes and geomorphic processes. Point cloud quality can be expressed as accuracy in a comparative (i.e., from survey to survey) and absolute (between survey and an external reference system) sense. Here we present a simple workflow for calculating pairs or sets of point clouds with a high comparative accuracy, without the need for ground control points or a dGPS equipped UAV. We demonstrate the efficacy of the new approach using a consumer-grade UAV in two contrasting landscapes: the coastal cliffs on the Island of Rügen, Germany, and the tectonically active Daan River gorge in Taiwan. Compared to a standard approach using ground control points, our workflow results in a nearly identical distribution of measured changes. Compared to a standard approach without ground control, our workflow reduces the level of change detection from several meters to 10–15 cm. This approach enables robust change detection using UAVs in settings where ground control is not possible.

scholarly journals Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

2020 ◽  
Vol 9 (11) ◽  
pp. 656
Author(s):  
Muhammad Hamid Chaudhry ◽  
Anuar Ahmad ◽  
Qudsia Gulzar
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Computational Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
High Computational Cost

Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.

scholarly journals Geocoding Error Correction for InSAR Point Clouds

2018 ◽  
Vol 10 (10) ◽  
pp. 1523 ◽  
Author(s):  
Sina Montazeri ◽  
Fernando Rodríguez González ◽  
Xiao Zhu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Persistent Scatterer Interferometry ◽  
Sar Images ◽  
Localization Accuracy ◽  
Ground Control Points ◽  
Persistent Scatterer ◽  
The City

Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.

scholarly journals CHANGE DETECTION IN PHOTOGRAMMETRIC POINT CLOUDS FOR MONITORING OF ALPINE, GRAVITATIONAL MASS MOVEMENTS

2020 ◽  
Vol V-2-2020 ◽  
pp. 687-693
Author(s):  
A. Dinkel ◽  
L. Hoegner ◽  
A. Emmert ◽  
L. Raffl ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Noise Level ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Gravitational Mass ◽  
Ground Control ◽  
Mass Movements ◽  
Control Points ◽  
The Third ◽  
Ground Control Points

Abstract. This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. For the first epoch, two sequences are recorded, one in the morning and one in the afternoon to evaluate the noise level within the point clouds for a static geometry and changing light conditions. The second epoch is recorded a few months after the first epoch where also no significant change has occurred in between. The third epoch is recorded after one year with changes detected. As all point clouds are given in the same local coordinate frame and thus are co-registered via the ground control points, change detection is based on calculating the Multiscale-Model-to-Model-Cloud distances (M3C2) of the point clouds. Results show no movements for the first year, but identify significant movements comparing the third epoch taken in the second year. Besides the noise level estimation, the quality checks include the accuracy of the camera orientations based on ground control points, the covariances of the bundle adjustment, and a comparison the Geodetic measurements of additional control points and a laser scanning point cloud of a part of the crevice. Additionally, geological measurements of the movements have been performed using extensometers.

scholarly journals GLACIER VOLUME CHANGE MONITORING FROM UAV OBSERVATIONS: ISSUES AND POTENTIALS OF STATE-OF-THE-ART TECHNIQUES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1041-1048
Author(s):  
M. Di Rita ◽  
D. Fugazza ◽  
V. Belloni ◽  
G. Diolaiuti ◽  
M. Scaioni ◽  
...  
Keyword(s):  
State Of The Art ◽  
Morphological Evolution ◽  
Ground Control ◽  
Control Points ◽  
Volume Changes ◽  
Alpine Glacier ◽  
Ice Volume ◽  
Ground Control Points ◽  
Dense Point ◽  
Multi Temporal

Abstract. Alpine glaciers play a key role in our society through the production of freshwater for domestic, industrial and agricultural use. As they are severely affected by climate change, it is of crucial importance to understand their behaviour and monitor their morphological evolution, with the primary aims to estimate ice volume and mass changes. However, the accurate retrieval of glacier morphology changes over time is not an easy task. In this context, the use of Unmanned Aerial Vehicles (UAVs) is of interest to the glaciological community because of their flexibility, fine spatial detail and ease of processing with state-of-the-art software packages, which makes them an ideal candidate to investigate glacier changes. The goal of this work is to assess the accuracy that can be obtained with UAVs observations when comparing volume changes computed from multi-temporal acquisitions on an Alpine glacier, on the basis of a photogrammetric pipeline implemented in Leica Infinity software. The study area is Forni Glacier in Raethian Alps, Italy. Two photogrammetric blocks were acquired in 2014 and 2016 using different UAVs: a fixed-wing drone in 2014 and an in-house multicopter in 2016. Ground Control Points (GCPs) were established only during the 2016 survey which was used to establish the reference datum. Different techniques to co-register the 2014 dataset to the 2016 dataset were applied and compared: 1) using points extracted from the 2016 Dense Point Cloud (DPC) as GCPs for the 2014 DPC generation; 2) shifting and rotating the raw 2014 DPC, using manually digitised common points from the 2014 and 2016 DPCs in Leica Infinity; 3) first manually shifting, then automatically roto-translating with the Iterative Closest Point (ICP) algorithm the raw 2014 DPC in CloudCompare. The investigation shows a good agreement of the three co-registration methods in terms of height and ice volume changes and the potential of UAV data processing with Leica Infinity for glacier monitoring even when the acquisition conditions are problematic (lack of ground control points, sub-optimal image quality).

scholarly journals Analysis of the impacts on the quality of UAV photogrammetric products

2020 ◽  
Vol 64 (04) ◽  
pp. 489-507
Author(s):  
Mojca Kosmatin Fras ◽  
Urška Drešček ◽  
Anka Lisec ◽  
Dejan Grigillo
Keyword(s):  
Data Acquisition ◽  
Unmanned Aerial Vehicles ◽  
Spatial Data ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Aerial Vehicles

Unmanned aerial vehicles, equipped with various sensors and devices, are increasingly used to acquire geospatial data in geodesy, geoinformatics, and environmental studies. In this context, a new research and professional field has been developed – UAV photogrammetry – dealing with photogrammetry data acquisition and data processing, acquired by unmanned aerial vehicles. In this study, we analyse the selected factors that impact the quality of data provided using UAV photogrammetry, with the focus on positional accuracy; they are discussed in three groups: (a) factors related to the camera properties and the quality of images; (b) factors related to the mission planning and execution; and (c) factors related to the indirect georeferencing of images using ground control points. These selected factors are analysed based on the detailed review of relevant scientific publications. Additionally, the influence of the number of ground control points and their spatial distribution on point clouds' positional accuracy has been investigated for the case study. As the conclusion, key findings and recommendations for UAV photogrammetric projects are given; we have highlighted the importance of suitable lighting and weather conditions when performing UAV missions for spatial data acquisition, quality equipment, appropriate parameters of UAV data acquisition, and a sufficient number of ground control points, which should be determined with the appropriate positional accuracy and their correct distribution in the field.

scholarly journals Discrete and Distributed Error Assessment of UAS-SfM Point Clouds of Roadways

2020 ◽  
Vol 5 (10) ◽  
pp. 87
Author(s):  
Yijun Liao ◽  
Richard L. Wood
Keyword(s):  
Region Of Interest ◽  
Point Clouds ◽  
Site Investigation ◽  
Optimal Number ◽  
Ground Control ◽  
Control Points ◽  
Elevation Range ◽  
Ground Control Points ◽  
Site Access ◽  
The Impact

Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey.

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