scholarly journals Positional Accuracy Assessment of Digital Orthophoto Based on UAV Images: An Experience on an Archaeological Area

Heritage ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 1304-1327
Author(s):  
Saadet Armağan Güleç Korumaz ◽  
Ferruh Yıldız
Keyword(s):  
Accuracy Assessment ◽  
Rapid Development ◽  
Accuracy Evaluation ◽  
Generation Process ◽  
Positional Accuracy ◽  
Archaeological Heritage ◽  
Aerial Vehicle ◽  
American Society ◽  
Uav Images ◽  
Digital Orthophoto

Rapid development in UAV (unmanned aerial vehicle) photogrammetry made it preferable in many applications including cultural heritage documentation. Usability, quickness and accuracy of digital images have grabbed also the attention of archaeologists. Especially orthoimages by UAVs have become considerably significant in the field of archaeological heritage documentation since they are fast and accurate images of the object with high detailed information. However their accuracy and quality are the most important features of these images for archaeological documentation. The aim of this paper is to evaluate horizontal and vertical accuracy of an orthophoto taken by a fixed-wing UAV in an archaeological area. The evaluation is made according to ASPRS (American Society for Photogrammetry and Remote Sensing) Accuracy Standards for Digital Geospatial Data. The archaeological area, the name of which is Kubad Abad Palace in Beyşehir Province in Konya, is the only Anatolian Seljuk Palace structure that has survived to the present day. The study describes the orthophoto generation process and positional accuracy evaluation results within the frame of the importance of accuracy for archaeological documentation.

UAV–LiDAR accuracy in vegetated terrain

2018 ◽  
Vol 6 (4) ◽  
pp. 212-234
Author(s):  
Maja Kucharczyk ◽  
Chris H. Hugenholtz ◽  
Xueyang Zou
Keyword(s):  
Vegetation Type ◽  
Laser Scanner ◽  
Ground Surface ◽  
Vegetation Types ◽  
Positional Accuracy ◽  
Tall Grass ◽  
Aerial Vehicle ◽  
Vertical Accuracy ◽  
American Society ◽  
Reference Measurements

We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.

scholarly journals Evaluating the Potential of PPK Direct Georeferencing for UAV-SfM Photogrammetry and Precise Topographic Mapping

2019 ◽  
Author(s):  
He Zhang ◽  
Emilien Aldana-Jague ◽  
François Clapuyt ◽  
Florian Wilken ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Focal Length ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Time Period ◽  
Surface Models ◽  
Flight Height ◽  
Aerial Vehicle ◽  
Repeatability And Reproducibility ◽  
Uav Images ◽  
Topography Data

Abstract. Images captured by Unmanned aerial vehicle (UAV) and processed by Structure from Motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time- and cost-effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (Post-Processing Kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV-PPK-SfM workflow, we carried out multiple flight missions with different camera/UAV systems. Our analysis showed that the PPK solution provides the same accuracy (mean: ca. 0.01 m, RMSE: ca. 0.03 m) as the GCP method. Furthermore, our results indicated that camera properties (i.e., focal length, resolution, sensor quality) have an impact on the accuracy but planimetric and altimetric errors remained in the range of 0.011 to 0.024 m. By analysing the repeatability of DSM construction over a time period of a few months, our study demonstrates that a UAV-PPK-SfM workflow can provide consistent, repeatable 4D data with an accuracy of a few centimetres without the use of GCPs. An uncertainty analysis showed that the minimum level of topographical change detection was ca. ±0.04 m for a high-end DSLR camera and ca. ±0.08 m for an action camera (for a flight height of 45 m). The level of detection substantially improved when reducing the UAV flight height. This study demonstrates the repeatability, reproducibility and efficiency of a PPK-SfM workflow in the context of 4D earth surface monitoring with time-laps SfM photogrammetry. As such, it should be considered as an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

scholarly journals Generating a High-Precision True Digital Orthophoto Map Based on UAV Images

2018 ◽  
Vol 7 (9) ◽  
pp. 333 ◽  
Author(s):  
Yu Liu ◽  
Xinqi Zheng ◽  
Gang Ai ◽  
Yi Zhang ◽  
Yuqiang Zuo
Keyword(s):  
High Precision ◽  
Image Texture ◽  
Surface Model ◽  
Ground Sample ◽  
Planning And Design ◽  
Ground Control Points ◽  
Sensing Technology ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Digital Orthophoto

Unmanned aerial vehicle (UAV) low-altitude remote sensing technology has recently been adopted in China. However, mapping accuracy and production processes of true digital orthophoto maps (TDOMs) generated by UAV images require further improvement. In this study, ground control points were distributed and images were collected using a multi-rotor UAV and professional camera, at a flight height of 160 m above the ground and a designed ground sample distance (GSD) of 0.016 m. A structure from motion (SfM), revised digital surface model (DSM) and multi-view image texture compensation workflow were outlined to generate a high-precision TDOM. We then used randomly distributed checkpoints on the TDOM to verify its precision. The horizontal accuracy of the generated TDOM was 0.0365 m, the vertical accuracy was 0.0323 m, and the GSD was 0.0166 m. Tilt and shadowed areas of the TDOM were eliminated so that buildings maintained vertical viewing angles. This workflow produced a TDOM accuracy within 0.05 m, and provided an effective method for identifying rural homesteads, as well as land planning and design.

scholarly journals Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species

2018 ◽  
Vol 10 (10) ◽  
pp. 1662 ◽  
Author(s):  
François-Marie Martin ◽  
Jana Müllerová ◽  
Laurent Borgniet ◽  
Fanny Dommanget ◽  
Vincent Breton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Invasive Plant ◽  
Accuracy Assessment ◽  
Spatial Dynamics ◽  
Detection Rates ◽  
Fallopia Japonica ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Accuracy Assessments

Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping of invasive plant populations and the monitoring of colonization fronts. Remote sensing using very high resolution satellite and Unmanned Aerial Vehicle (UAV) imagery is increasingly considered for such purposes. Here, we assessed the potential of several single- and multi-date indices derived from satellite and UAV imagery (i.e., UAV-generated Canopy Height Models—CHMs; and Bi-Temporal Band Ratios—BTBRs) for the detection and mapping of the highly problematic Asian knotweeds (Fallopia japonica; Fallopia × bohemica) in two different landscapes (i.e., open vs. highly heterogeneous areas). The idea was to develop a simple classification procedure using the Random Forest classifier in eCognition, usable in various contexts and requiring little training to be used by non-experts. We also rationalized errors of omission by applying simple “buffer” boundaries around knotweed predictions to know if heterogeneity across multi-date images could lead to unfairly harsh accuracy assessment and, therefore, ill-advised decisions. Although our “crisp” satellite results were rather average, our UAV classifications achieved high detection accuracies. Multi-date spectral indices and CHMs consistently improved classification results of both datasets. To the best of our knowledge, it was the first time that UAV-generated CHMs were used to map invasive plants and their use substantially facilitated knotweed detection in heterogeneous vegetation contexts. Additionally, the “buffer” boundary results showed detection rates often exceeding 90–95% for both satellite and UAV images, suggesting that classical accuracy assessments were overly conservative. Considering these results, it seems that knotweed can be satisfactorily mapped and monitored via remote sensing with moderate time and money investment but that the choice of the most appropriate method will depend on the landscape context and the spatial scale of the invaded area.

scholarly journals ASSESSMENT OF DIFFERENT UNMANNED AERIAL VEHICLE SYSTEM FOR PRODUCTION OF PHOTOGRAMMERTY PRODUCTS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 549-554
Author(s):  
M. H. M. Room ◽  
A. Ahmad ◽  
M. A. Rosly
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Accuracy Assessment ◽  
Digital Camera ◽  
Lidar System ◽  
Cloud Data ◽  
Aerial Photogrammetry ◽  
Vehicle System ◽  
Aerial Vehicle ◽  
Digital Orthophoto

Abstract. The demand of aerial photogrammetry has increased recently especially after the development of unmanned aerial vehicle system. This study explores the use of different UAV systems which comprised of conventional UAV, UAV RTK and UAV Lidar systems. This study also comprises of three experiments. The first experiment involved the mapping of Lingkaran Ilmu, UTM by using fixed wing Ebee UAV with 20megapixel digital camera. This first experiment used conventional UAV. The second experiment involved the fixed wing Ebee UAV equipped with real time kinematic (RTK) system on-board for mapping the study area. The last experiment is the used of octacopter UAV equipped with Riegl Lidar system for mapping the study area. The study area for all experiments is located in Lingkaran Ilmu of main campus Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia. Ebee UAV and Ebee RTK UAV are flown in autonomous mode at 200 meters altitude. Both systems are used to capture high resolution aerial photo of the study area. Riegl UAV Lidar system is flown at 100 meter altitude for capture high resolution and point cloud data. GPS rapid static method was used for establishing ground control points (GCP) and check point (CP) in the study area. Three different GCP configuration was applied in geometry correction. Meanwhile, CPs is used for accuracy assessment where RMSE equation was employed. The 15CGP configuration produce more accurate result compared to another. Where, the planimetric RMSE values of Ebee UAV, Ebee RTK UAV and Riegl UAV Lidar are 0.21 m, 0.09 m and 0.15 m respectively. For height RMSE values for Ebbe, Ebee RTK and Octacopter Lidar are 0.34 m, 0.13 m and 0.07 m respectively. In conclusion, Ebee RTK UAV is identified as a system that can produce an accurate digital orthophoto compared to other systems while Riegl UAV Lidar system can produce highest accurate DEM and DTM compared to other systems in 15GCP configuration.

scholarly journals INTEGRATION BETWEEN UNMANNED AERIAL VEHICLE AND TERRESTRIAL LASER SCANNER IN PRODUCING 3D MODEL

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 391-398 ◽  
Author(s):  
A. Mat Adnan ◽  
N. Darwin ◽  
M. F. M. Ariff ◽  
Z. Majid ◽  
K. M. Idris
Keyword(s):  
Data Acquisition ◽  
3D Model ◽  
Accuracy Assessment ◽  
Laser Scanner ◽  
Point Clouds ◽  
Aerial Survey ◽  
3D Data ◽  
Ground Survey ◽  
Aerial Vehicle ◽  
Uav Images

Abstract. Unmanned Aerial Vehicles (UAV) frequently used for obtaining 2D or 3D data acquisition. Meanwhile, Terrestrial Laser Scanners (TLS) are used for obtaining only 3D data acquisition. However if both are integrated, they were able to produce a more accurate data. The purpose of this study is to investigate the possible integration of point clouds obtained by TLS with UAV images at T06 FBES building through the aerial survey where the roof is scanned and ground survey which scans the facades‟ building. Topcon GLS 2000 and DJI Inspire 1 UAV were used to acquire the data at the field. The aerial data and ground data were processed using Pix4D and Scanmaster respectively. The data integration process is done by converting both point clouds into the same coordinate system and then by aligning the same points of both points clouds in Cloud Compare. For verification purposes, dimensional survey was done and there are several distances were taken from the study area to validate the accuracy assessment. The result of residuals between the dimension survey and integration is 0.183 m which is below 1 meter. The result of this study is a 3D model of UTM T06 FBES building based on the point cloud accuracy in cm level. To conclude, the integration between these two methods can be implemented to produce an accurate 3D model.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

scholarly journals Design of bio-inspired binocular UAV detection system based on improved STC algorithm of scale transformation and occlusion detection

2021 ◽  
Vol 13 ◽  
pp. 175682932110048
Author(s):  
Huajun Song ◽  
Yanqi Wu ◽  
Guangbing Zhou
Keyword(s):  
Detection System ◽  
Rapid Development ◽  
Reference Value ◽  
Scale Transformation ◽  
Hardware System ◽  
Time Requirements ◽  
Aerial Vehicle ◽  
Spatio Temporal ◽  
Small Targets ◽  
Vision Detection

With the rapid development of drones, many problems have arisen, such as invasion of privacy and endangering security. Inspired by biology, in order to achieve effective detection and robust tracking of small targets such as unmanned aerial vehicles, a binocular vision detection system is designed. The system is composed of long focus and wide-angle dual cameras, servo pan tilt, and dual processors for detecting and identifying targets. In view of the shortcomings of spatio-temporal context target tracking algorithm that cannot adapt to scale transformation and easy to track failure in complex scenes, the scale filter and loss criterion are introduced to make an improvement. Qualitative and quantitative experiments show that the designed system can adapt to the scale changes and partial occlusion conditions in the detection, and meets the real-time requirements. The hardware system and algorithm both have reference value for the application of anti-unmanned aerial vehicle systems.

scholarly journals Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

2021 ◽  
Vol 13 (7) ◽  
pp. 1238
Author(s):  
Jere Kaivosoja ◽  
Juho Hautsalo ◽  
Jaakko Heikkinen ◽  
Lea Hiltunen ◽  
Pentti Ruuttunen ◽  
...  
Keyword(s):  
Reference Data ◽  
Measurement Techniques ◽  
Synthetic Data ◽  
Ground Truth ◽  
Precision Farming ◽  
Aerial Imaging ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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