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 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 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 Methodology of Processing Single-Strip Blocks of Imagery with Reduction and Optimization Number of Ground Control Points in UAV Photogrammetry

2020 ◽  
Vol 12 (20) ◽  
pp. 3336 ◽  
Author(s):  
Marta Lalak ◽  
Damian Wierzbicki ◽  
Michał Kędzierski
Keyword(s):  
Optimal Number ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
High Resolution Imagery ◽  
Levenberg Marquardt ◽  
Aerial Vehicle ◽  
Mean Square Errors ◽  
Powell Method ◽  
Single Strip

Unmanned aerial vehicle (UAV) systems are often used to collect high-resolution imagery. Data obtained from UAVs are now widely used for both military and civilian purposes. This article discusses the issues related to the use of UAVs for the imaging of restricted areas. Two methods of developing single-strip blocks with the optimal number of ground control points are presented. The proposed methodology is based on a modified linear regression model and an empirically modified Levenberg–Marquardt–Powell algorithm. The effectiveness of the proposed methods of adjusting a single-strip block were verified based on several test sets. For method I, the mean square errors (RMSE) values for the X, Y, Z coordinates of the control points were within the range of 0.03–0.13 m/0.08–0.09 m, and for the second method, 0.03–0.04 m/0.06–0.07 m. For independent control points, the RMSE values were 0.07–0.12 m/0.06–0.07 m for the first method and 0.07–0.12 m/0.07–0.09 m for the second method. The results of the single-strip block adjustment showed that the use of the modified Levenberg–Marquardt–Powell method improved the adjustment accuracy by 13% and 16%, respectively.

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 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 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 An automatic high precision registration method between large area aerial images and aerial light detection and ranging data

2015 ◽  
Vol XL-7/W4 ◽  
pp. 17-21
Author(s):  
Q. Du ◽  
D. Xie ◽  
Y. Sun
Keyword(s):  
High Precision ◽  
Image Matching ◽  
Aerial Images ◽  
Control Points ◽  
Single Image ◽  
Large Area ◽  
Registration Method ◽  
Aerial Photogrammetry ◽  
Ground Control Points ◽  
Manual Registration

The integration of digital aerial photogrammetry and Light Detetion And Ranging (LiDAR) is an inevitable trend in Surveying and Mapping field. We calculate the external orientation elements of images which identical with LiDAR coordinate to realize automatic high precision registration between aerial images and LiDAR data. There are two ways to calculate orientation elements. One is single image spatial resection using image matching 3D points that registered to LiDAR. The other one is Position and Orientation System (POS) data supported aerotriangulation. The high precision registration points are selected as Ground Control Points (GCPs) instead of measuring GCPs manually during aerotriangulation. The registration experiments indicate that the method which registering aerial images and LiDAR points has a great advantage in higher automation and precision compare with manual registration.

scholarly journals GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY

2020 ◽  
Vol 92,2020 (92) ◽  
pp. 15-23
Author(s):  
O. L., Dorozhynskyy ◽  
◽  
I. Z. Kolb ◽  
L. V. Babiy ◽  
L. V. Dychko ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Aerial Survey ◽  
Reference Points ◽  
Aerial Images ◽  
Aerial Image ◽  
Ground Control ◽  
Reference Image ◽  
Control Points ◽  
Ground Control Points ◽  
Reference Images

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

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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