scholarly journals GEOREFERENCING UAS DERIVATIVES THROUGH POINT CLOUD REGISTRATION WITH ARCHIVED LIDAR DATASETS

2016 ◽  
Vol IV-2/W1 ◽  
pp. 195-199 ◽  
Author(s):  
M. S. L. Y. Magtalas ◽  
J. C. L. Aves ◽  
A. C. Blanco
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Aerial Images ◽  
Ground Control ◽  
Control Points ◽  
Model Errors ◽  
Validation Data ◽  
Positioning Systems ◽  
Ground Control Points ◽  
Skeleton Point

Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a ‘skeleton point cloud’. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of CANUPO and manual skeleton clouds were obtained with values for both equal to around 0.67 meters at 1.73 standard deviation.

scholarly journals Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
Margaret Kalacska ◽  
Oliver Lucanus ◽  
J. Pablo Arroyo-Mora ◽  
Étienne Laliberté ◽  
Kathryn Elmer ◽  
...  
Keyword(s):  
Low Cost ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Model Errors ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Digital Elevation ◽  
High Level ◽  
Aerial Systems

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

Spatiotemporal assessment of ephemeral gully characteristics using low altitude aerial imagery: an approach for quantifying

2020 ◽  
Author(s):  
Henrique Momm ◽  
Robert Wells ◽  
Carlos Castillo ◽  
Ronald Bingner
Keyword(s):  
Point Cloud ◽  
Ground Control ◽  
Control Points ◽  
Benchmark Point ◽  
Rainfall Events ◽  
Ground Control Points ◽  
Software Packages ◽  
Ephemeral Gully ◽  
Ephemeral Gullies ◽  
Low Altitude

<p>In agricultural fields, ephemeral gullies are defined as erosional channels formed primarily by overland flow from rainfall events. These channels are characterized by small dimensions, approximately 0.5 to 25 cm in depth, which allows their removal during regular farming operations. This dynamic characteristic coupled with their small size often can conceal soil losses by ephemeral gullies and poses challenges to efforts devised for soil loss quantification and mitigation. In this study, novel surveying and data processing techniques were employed to capture the small scale in topographic variation between two surveys and to assure that changes were due to erosional processes rather than survey miss-alignment. An agricultural field located in Iowa, U.S.A. with an area of approximately 54,500 m<sup>2</sup> was surveyed twice: right after the field was planted with corn and approximately one month later, following several rainfall events. A static benchmark point was established at the edge of the field and tied to public geodesic locations. A set of removable ground control points were spread throughout the field and surveyed in relation to the benchmark point. Low altitude aerial images were collected using a quadcopter UAS. Ground control points were used to aid in geospatial registration and to assess final survey accuracy. Standard off-the-shelf commercial software packages were unable compensate for less distortion and a new procedure using Micmac open-source photogrammetry software package was used to account for complex distortion patterns in the raw image data set. The undistorted images were then processed using Agisoft Photoscan for camera alignment, model georeferencing, and dense point cloud generation. Each point cloud representing a time period contained over 1 billion of points (file size > 100GB) and was processed using custom algorithms for filtering outliers and rasterization into a 2.5 cm raster grid (DEM). Analysis of differences between the two high spatial resolution DEMs revealed changes in the landscape due to natural (erosion/deposition) and anthropogenic (farming activities) factors. Specifically, for ephemeral gully analysis, morphological features in the form of headcut position and size, channel incision, sinuosity, lateral expansion, and depositional patterns were easily identified. Findings of this study shed light on potential pitfalls inherent to the utilization of off-the-shelf commercial software packages for such fine scale multi-temporal analysis, describe the need for standardization of procedures that assure accurate erosional response amongst different studies, and support the generation of accurate datasets critical in advancing our understanding of ephemeral gully processes needed for improved model development and validation.</p>

scholarly journals Archival Aerial Images Georeferencing: A Geostatistically-Based Approach for Improving Orthophoto Accuracy with Minimal Number of Ground Control Points

2020 ◽  
Vol 12 (14) ◽  
pp. 2232
Author(s):  
Manuela Persia ◽  
Emanuele Barca ◽  
Roberto Greco ◽  
Maria Immacolata Marzulli ◽  
Patrizia Tartarino
Keyword(s):  
Southern Italy ◽  
Aerial Images ◽  
Ground Control ◽  
Control Points ◽  
Territorial Planning ◽  
Ground Control Points ◽  
Study Results ◽  
Open Issue ◽  
Historical Photographs

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.

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 THE IMPLEMENTATION OF A PHOTOGRAMMETRIC PROCEDURE FOR THE ADJUSTMENT OF THE OLD RASTER CADASTRAL PLANS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 177-182
Author(s):  
K. Si youcef ◽  
I. Boukerch ◽  
F. Z. Belhouari ◽  
A. M. Seddiki ◽  
B. Takarli
Keyword(s):  
Optimal Number ◽  
Aerial Images ◽  
Second Step ◽  
Ground Control ◽  
Optimal Distribution ◽  
Control Points ◽  
Large Area ◽  
Ground Control Points

Abstract. Algeria faces challenges of globalization. It classifies the establishment of the national general urban and rural territory cadastre as top priority. The National Cadastre Agency has implemented a policy aimed at improving the quality and accuracy of the resulting documentation, in order to widen the scope of the latter in the various fields.Since the launching of the first operations to establish the general cadastre of the national territory, the graphic cadastral documentation which was carried out based on aerial images (ortho-photographs or restitution plans) present mismatch either between the external borders or between the section plans that compose the communal (municipal) cadastral plane.This article describes one of the simultaneous plane adjustment techniques inspired by the aero triangulation used in photogrammetry. In a first step, we built the photogrammetric unit where we consider the cadastral planes as photogrammetric models. In a second step, the constructed units will be used to form a superstructure covering a very large area like in the photogrammetric block case. Finally, this superstructure is adjusted, where the discrepancies are reduces relatively between these section plans using Tie Points (TP) and absolutely by relying on an optimal number of Ground Control Points (GCP) in the terrain system suitably distributed on the block.This technique makes it possible to preserve the relationships between the data in a precise way and to guarantee the continuity in the acquisition of the data which can be added later. It also makes it possible to solve the problem of the overlap between the isolated section plans due to the non-optimal distribution, the insufficiency, or the absence of control points.The evaluation results obtained after the experiments report that the proposed adjustment technique is efficient to solve such a problem.

scholarly journals PANTIR - A DUAL CAMERA SETUP FOR PRECISE GEOREFERENCING AND MOSAICING OF THERMAL AERIAL IMAGES

2015 ◽  
Vol XL-3/W2 ◽  
pp. 269-272 ◽  
Author(s):  
I. Weber ◽  
A. Jenal ◽  
C. Kneer ◽  
J. Bongartz
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Spatial Resolution ◽  
Common Ground ◽  
Aerial Images ◽  
Ground Control ◽  
Thermal Activity ◽  
Control Points ◽  
High Quality ◽  
Ground Control Points

Research and monitoring in fields like hydrology and agriculture are applications of airborne thermal infrared (TIR) cameras, which suffer from low spatial resolution and low quality lenses. Common ground control points (GCPs), lacking thermal activity and being relatively small in size, cannot be used in TIR images. Precise georeferencing and mosaicing however is necessary for data analysis. Adding a high resolution visible light camera (VIS) with a high quality lens very close to the TIR camera, in the same stabilized rig, allows us to do accurate geoprocessing with standard GCPs after fusing both images (VIS+TIR) using standard image registration methods.

scholarly journals Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 46 ◽  
Author(s):  
I-Kuai Hung ◽  
Daniel Unger ◽  
David Kulhavy ◽  
Yanli Zhang
Keyword(s):  
Flight Control ◽  
Fundamental Question ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Ground Control ◽  
Image Mosaic ◽  
Control Points ◽  
Ground Control Points ◽  
Positional Precision

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight.

scholarly journals ANALYSIS AND CORRECTION OF SYSTEMATIC HEIGHT MODEL ERRORS

2016 ◽  
Vol XLI-B1 ◽  
pp. 333-339 ◽  
Author(s):  
K. Jacobsen
Keyword(s):  
Bias Correction ◽  
Low Frequency ◽  
Ground Control ◽  
Control Points ◽  
Base Length ◽  
Model Errors ◽  
Ground Control Points ◽  
Small Base ◽  
Image Orientation ◽  
Height Model

The geometry of digital height models (DHM) determined with optical satellite stereo combinations depends upon the image orientation, influenced by the satellite camera, the system calibration and attitude registration. As standard these days the image orientation is available in form of rational polynomial coefficients (RPC). Usually a bias correction of the RPC based on ground control points is required. In most cases the bias correction requires affine transformation, sometimes only shifts, in image or object space. For some satellites and some cases, as caused by small base length, such an image orientation does not lead to the possible accuracy of height models. As reported e.g. by Yong-hua et al. 2015 and Zhang et al. 2015, especially the Chinese stereo satellite ZiYuan-3 (ZY-3) has a limited calibration accuracy and just an attitude recording of 4 Hz which may not be satisfying. Zhang et al. 2015 tried to improve the attitude based on the color sensor bands of ZY-3, but the color images are not always available as also detailed satellite orientation information. There is a tendency of systematic deformation at a Pléiades tri-stereo combination with small base length. The small base length enlarges small systematic errors to object space. But also in some other satellite stereo combinations systematic height model errors have been detected. The largest influence is the not satisfying leveling of height models, but also low frequency height deformations can be seen. <br><br> A tilt of the DHM by theory can be eliminated by ground control points (GCP), but often the GCP accuracy and distribution is not optimal, not allowing a correct leveling of the height model. In addition a model deformation at GCP locations may lead to not optimal DHM leveling. Supported by reference height models better accuracy has been reached. As reference height model the Shuttle Radar Topography Mission (SRTM) digital surface model (DSM) or the new AW3D30 DSM, based on ALOS PRISM images, are satisfying. They allow the leveling and correction of low frequency height errors and lead to satisfying correction of the DSM based on optical satellite images. <br><br> The potential of DHM generation, influence of systematic model deformation and possibilities of improvement has been investigated.

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