scholarly journals Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 9 ◽  
Author(s):  
Yuri Taddia ◽  
Francesco Stecchi ◽  
Alberto Pellegrinelli
Keyword(s):  
Ease Of Use ◽  
Reference Station ◽  
Post Processing ◽  
Coastal Mapping ◽  
Digital Terrain ◽  
Ground Control Points ◽  
Processing Strategies ◽  
Camera Location ◽  
Aerial Mapping ◽  
Oblique Images

Topographic and geomorphological surveys of coastal areas usually require the aerial mapping of long and narrow sections of littoral. The georeferencing of photogrammetric models is generally based on the signalization and survey of Ground Control Points (GCPs), which are very time-consuming tasks. Direct georeferencing with high camera location accuracy due to on-board multi-frequency GNSS receivers can limit the need for GCPs. Recently, DJI has made available the Phantom 4 Real-Time Kinematic (RTK) (DJI-P4RTK), which combines the versatility and the ease of use of previous DJI Phantom models with the advantages of a multi-frequency on-board GNSS receiver. In this paper, we investigated the accuracy of both photogrammetric models and Digital Terrain Models (DTMs) generated in Agisoft Metashape from two different image datasets (nadiral and oblique) acquired by a DJI-P4RTK. Camera locations were computed with the Post-Processing Kinematic (PPK) of the Receiver Independent Exchange Format (RINEX) file recorded by the aircraft during flight missions. A Continuously Operating Reference Station (CORS) located at a 15 km distance from the site was used for this task. The results highlighted that the oblique dataset produced very similar results, with GCPs (3D RMSE = 0.025 m) and without (3D RMSE = 0.028 m), while the nadiral dataset was affected more by the position and number of the GCPs (3D RMSE from 0.034 to 0.075 m). The introduction of a few oblique images into the nadiral dataset without any GCP improved the vertical accuracy of the model (Up RMSE from 0.052 to 0.025 m) and can represent a solution to speed up the image acquisition of nadiral datasets for PPK with the DJI-P4RTK and no GCPs. Moreover, the results of this research are compared to those obtained in RTK mode for the same datasets. The novelty of this research is the combination of a multitude of aspects regarding the DJI Phantom 4 RTK aircraft and the subsequent data processing strategies for assessing the quality of photogrammetric models, DTMs, and cross-section profiles.

scholarly journals Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

2020 ◽  
Vol 12 (19) ◽  
pp. 3144
Author(s):  
Yuri Taddia ◽  
Laura González-García ◽  
Elena Zambello ◽  
Alberto Pellegrinelli
Keyword(s):  
Real Time ◽  
Accuracy Assessment ◽  
Three Dimensional ◽  
Ground Sample ◽  
Aerial Photogrammetry ◽  
Ground Control Points ◽  
Real Time Kinematic ◽  
Camera Location ◽  
Scale Range ◽  
Oblique Images

Aerial photogrammetry by Unmanned Aerial Vehicles (UAVs) is a widespread method to perform mapping tasks with high-resolution to reconstruct three-dimensional (3D) building and façade models. However, the survey of Ground Control Points (GCPs) represents a time-consuming task, while the use of Real-Time Kinematic (RTK) drones allows for one to collect camera locations with an accuracy of a few centimeters. DJI Phantom 4 RTK (DJI-P4RTK) combines this with the possibility to acquire oblique images in stationary conditions and it currently represents a versatile drone widely used from professional users together with commercial Structure-from-Motion software, such as Agisoft Metashape. In this work, we analyze the architectural application of this drone to the photogrammetric modeling of a building with particular regard to metric survey specifications for cultural heritage for 1:20, 1:50, 1:100, and 1:200 scales. In particular, we designed an accuracy assessment test signalizing 109 points, surveying them with total station and adjusting the measurements through a network approach in order to achieve millimeter-level accuracy. Image datasets with a designed Ground Sample Distance (GSD) of 2 mm were acquired in Network RTK (NRTK) and RTK modes in manual piloting and processed both as single façades (S–F) and as an overall block (4–F). Subsequently, we compared the results of photogrammetric models generated in Agisoft Metashape to the Signalized Point (SP) coordinates. The results highlight the importance of processing an overall photogrammetric block, especially whenever part of camera locations exhibited a poorer accuracy due to multipath effects. No significant differences were found between the results of network real-time kinematic (NRTK) and real-time kinematic (RTK) datasets. Horizontal residuals were generally comparable to GNSS accuracy in NRTK/RTK mode, while vertical residuals were found to be affected by an offset of about 5 cm. We introduced an external GCP or used one SP per façade as GCP, assuming a poorer camera location accuracy at the same time, in order to fix this issue and comply with metric survey specifications for the widest architectural scale range. Finally, both S–F and 4–F projects satisfied the metric survey requirements of a scale of 1:50 in at least one of the approaches tested.

scholarly journals 3D MODELING OF INDUSTRIAL HERITAGE BUILDING USING COTSs SYSTEM: TEST, LIMITS AND PERFORMANCES

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 281-288 ◽  
Author(s):  
M. Piras ◽  
V. Di Pietra ◽  
D. Visintini
Keyword(s):  
New Technologies ◽  
Sensor Calibration ◽  
Industrial Heritage ◽  
Imaging Geometry ◽  
Processing Strategies ◽  
Two Factors ◽  
Industrial Architecture ◽  
Dense Point ◽  
Heritage Building ◽  
Oblique Images

The role of UAV systems in applied geomatics is continuously increasing in several applications as inspection, surveying and geospatial data. This evolution is mainly due to two factors: new technologies and new algorithms for data processing. About technologies, from some years ago there is a very wide use of commercial UAV even COTSs (Commercial On-The-Shelf) systems. Moreover, these UAVs allow to easily acquire oblique images, giving the possibility to overcome the limitations of the nadir approach related to the field of view and occlusions. In order to test potential and issue of COTSs systems, the Italian Society of Photogrammetry and Topography (SIFET) has organised the SBM2017, which is a benchmark where all people can participate in a shared experience. This benchmark, called “Photogrammetry with oblique images from UAV: potentialities and challenges”, permits to collect considerations from the users, highlight the potential of these systems, define the critical aspects and the technological challenges and compare distinct approaches and software. The case study is the “Fornace Penna” in Scicli (Ragusa, Italy), an inaccessible monument of industrial architecture from the early 1900s. The datasets (images and video) have been acquired from three different UAVs system: Parrot Bebop 2, DJI Phantom 4 and Flytop Flynovex. The aim of this benchmark is to generate the 3D model of the “Fornace Penna”, making an analysis considering different software, imaging geometry and processing strategies. This paper describes the surveying strategies, the methodologies and five different photogrammetric obtained results (sensor calibration, external orientation, dense point cloud and two orthophotos), using separately – the single images and the frames extracted from the video – acquired with the DJI system.

scholarly journals OpSeF: Open source Python framework for collaborative instance segmentation of bioimages

2020 ◽  
Author(s):  
Tobias M. Rasse ◽  
Réka Hollandi ◽  
Péter Horváth
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Complex Analysis ◽  
Ease Of Use ◽  
Problem Definition ◽  
Training Data ◽  
Post Processing ◽  
Gpu Clusters ◽  
User Tasks ◽  
Instance Segmentation

AbstractVarious pre-trained deep learning models for the segmentation of bioimages have been made available as ‘developer-to-end-user’ solutions. They usually require neither knowledge of machine learning nor coding skills, are optimized for ease of use, and deployability on laptops. However, testing these tools individually is tedious and success is uncertain.Here, we present the ‘Op’en ‘Se’gmentation ‘F’ramework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts’ knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. All analyst tasks are optimized for deployment on Linux workstations or GPU clusters, all user tasks may be performed on any laptop in ImageJ.OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and post-processing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and post-processing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data.We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows.Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little, the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease.

scholarly journals Enhancing UAV–SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images

2019 ◽  
Vol 11 (3) ◽  
pp. 239 ◽  
Author(s):  
Paul Nesbit ◽  
Christopher Hugenholtz
Keyword(s):  
Surface Deformation ◽  
Laser Scanner ◽  
Systematic Errors ◽  
Camera Angle ◽  
Processing Strategies ◽  
Accuracy And Precision ◽  
Complex Scenes ◽  
Precision And Accuracy ◽  
Oblique Images ◽  
Steep Slopes

Complex landscapes with high topographic relief and intricate geometry present challenges for complete and accurate mapping of both lateral (x, y) and vertical (z) detail without deformation. Although small uninhabited/unmanned aerial vehicles (UAVs) paired with structure-from-motion (SfM) image processing has recently emerged as a popular solution for a range of mapping applications, common image acquisition and processing strategies can result in surface deformation along steep slopes within complex terrain. Incorporation of oblique (off-nadir) images into the UAV–SfM workflow has been shown to reduce systematic errors within resulting models, but there has been no consensus or documentation substantiating use of particular imaging angles. To address these limitations, we examined UAV–SfM models produced from image sets collected with various imaging angles (0–35°) within a high-relief ‘badland’ landscape and compared resulting surfaces with a reference dataset from a terrestrial laser scanner (TLS). More than 150 UAV–SfM scenarios were quantitatively evaluated to assess the effects of camera tilt angle, overlap, and imaging configuration on the precision and accuracy of the reconstructed terrain. Results indicate that imaging angle has a profound impact on accuracy and precision for data acquisition with a single camera angle in topographically complex scenes. Results also confirm previous findings that supplementing nadir image blocks with oblique images in the UAV–SfM workflow consistently improves spatial accuracy and precision and reduces data gaps and systematic errors in the final point cloud. Subtle differences among various oblique camera angles and imaging patterns suggest that higher overlap and higher oblique camera angles (20–35°) increased precision and accuracy by nearly 50% relative to nadir-only image blocks. We conclude by presenting four recommendations for incorporating oblique images and adapting flight parameters to enhance 3D mapping applications with UAV–SfM in high-relief terrain.

scholarly journals Accuracy Assessment of a UAV Block by Different Software Packages, Processing Schemes and Validation Strategies

2020 ◽  
Vol 9 (3) ◽  
pp. 164 ◽  
Author(s):  
Vittorio Casella ◽  
Filiberto Chiabrando ◽  
Marica Franzini ◽  
Ambrogio Maria Manzino
Keyword(s):  
Accuracy Assessment ◽  
Comparative Approach ◽  
Ground Control Points ◽  
Bundle Block Adjustment ◽  
Software Packages ◽  
Processing Strategies ◽  
High Resolution Imagery ◽  
Aerial Vehicle ◽  
Leave One Out ◽  
Validation Strategy

Unmanned aerial vehicle (UAV) systems are heavily adopted nowadays to collect high-resolution imagery with the purpose of documenting and mapping environment and cultural heritage. Such data are currently processed by programs based on the Structure from Motion (SfM) concept, coming from the computer vision community, rather than from classical photogrammetry. It is interesting to check whether some widely accepted rules coming from old-fashioned photogrammetry still holds: the relation between accuracy and ground sampling distance (GSD), the ratio between the vertical and horizontal accuracy, accuracy estimated on ground control points (GCPs) vs. that estimated with check points (CPs) also in relation to their ratio and distribution. To face the envisaged aspects, the paper adopts a comparative approach, as several programs are used and numerous configurations considered. The paper illustrates the dataset adopted, the carefully tuned processing strategies and bundle block adjustment (BBA) results in terms of accuracy for both GCPs and CPs. Finally, a leave-one-out (LOO) cross-validation strategy is proposed to assess the accuracy for one of the proposed configurations. Some of the reported results were previously presented in the 5th GISTAM Conference.

scholarly journals Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

2019 ◽  
Vol 7 (3) ◽  
pp. 807-827 ◽  
Author(s):  
He Zhang ◽  
Emilien Aldana-Jague ◽  
François Clapuyt ◽  
Florian Wilken ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Change Detection ◽  
Structure From Motion ◽  
Low Cost ◽  
Post Processing ◽  
Positional Accuracy ◽  
Surface Change ◽  
Ground Control Points ◽  
Single Lens ◽  
Surface Representations ◽  
Uav Images

Abstract. Images captured by unmanned aerial vehicles (UAVs) 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 two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

scholarly journals Image georectification and feature tracking toolbox: ImGRAFT

2015 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
A. Messerli ◽  
A. Grinsted
Keyword(s):  
Feature Tracking ◽  
Cost Effective ◽  
Time Lapse ◽  
Post Processing ◽  
Outlet Glacier ◽  
Use Of Time ◽  
Camera Systems ◽  
Wide Range ◽  
Oblique Images ◽  
Minimal Effort

Abstract. The use of time-lapse camera systems is becoming an increasingly popular method for data acquisition. The camera setup is often cost-effective and simple, allowing for a large amount of data to be accumulated over a variety of environments for relatively minimal effort. The acquired data can, with the correct post-processing, result in a wide range of useful quantitative and qualitative information in remote and dangerous areas. The post-processing requires a significant amount of steps to transform images into meaningful data for quantitative analysis, such as velocity fields. To the best of our knowledge at present a complete, openly available package that encompasses georeferencing, georectification and feature tracking of terrestrial, oblique images is still absent. This study presents a complete, yet adaptable, open-source package developed in MATLAB, that addresses and combines each of these post-processing steps into one complete suite in the form of an "Image GeoRectification and Feature Tracking" (ImGRAFT: http://imgraft.glaciology.net) toolbox. The toolbox can also independently produce other useful outputs, such as viewsheds, georectified and orthorectified images. ImGRAFT is primarily focused on terrestrial oblique images, for which there are currently limited post-processing options available. In this study, we illustrate ImGRAFT for glaciological applications on a small outlet glacier Engabreen, Norway.

scholarly journals Wave measurements with a modified HydroBall® buoy using different GNSS processing strategies

GEOMATICA ◽  
2019 ◽  
Vol 73 (1) ◽  
pp. 1-14
Author(s):  
Benoit Crépeau Gendron ◽  
Mohamed Ali Chouaer ◽  
Rock Santerre ◽  
Mathieu Rondeau ◽  
Nicolas Seube
Keyword(s):  
Precise Point Positioning ◽  
Reference Station ◽  
Controlled Environment ◽  
Wave Measurements ◽  
Wave Measurement ◽  
Processing Strategies ◽  
Wave Heights ◽  
Kinematic Ppp ◽  
Gnss Processing

One of the CIDCO’s (The Interdisciplinary Center for the Development of Ocean Mapping) HydroBall® GNSS buoys has been specifically adapted to evaluate its potential for wave measurement at centimeter accuracy level. Multiple GNSS processing strategies were tested, namely PPK (Post-Processed Kinematic), PPP (Precise Point Positioning), and TRP (Time Relative Positioning). Experiments were carried out in a hydraulic flume where waves of different amplitudes and periods were generated in a controlled environment. The wave heights obtained by the various GNSS solutions were compared with ultrasonic gauge measurements placed along the flume. The best results were obtained with the PPK and TRP solutions with root mean squared (RMS) values of 2 cm (on average). The main advantages of the TRP solution are that it does not require any reference station nearby (contrary to PPK) or precise ephemerides (required by PPP). A sinusoidal regression comparison of the wave height time series allowed determination of the wave period and amplitude with mean errors of 0.06 s and 0.8 cm, respectively.

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