Performance Evaluation of 3d Modeling Software for Uav Photogrammetry

Yanagi, Hideharu; Chikatsu, Hirofumi

doi:10.5194/isprs-archives-xli-b5-147-2016

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…UAS-SfM-derived DTM errors are similar to that of other studies of similar habitats (Yanagi and Chikatsu, 2016;Goodbody et al, 2018). For example, Goodbody et al (2018), using a fixed-wing UAS to construct DTMs of vegetated habitats, reported an average of 0-0.5 m error in shrubland habitats.…”

Section: Vertical Errorsupporting

confidence: 79%

“…Though UAS-based methods are increasingly being utilized for monitoring coastal wetlands (Doughty and Cavanaugh, 2019;Farris et al, 2019;DiGiacomo et al, 2020;Thomsen et al, 2021), imaging protocols and analysis methods lack standardization amongst studies. Inter-sensor comparisons reveal differences in surface reconstruction and reflectance values between common consumer-grade UAS platforms (Sona et al, 2014;Yanagi and Chikatsu, 2016). Moreover, other studies have found differences in mapping products produced by different SfM software packages for forested (Fraser and Congalton, 2018;Kameyama and Sugiura, 2021) and unvegetated (Sona et al, 2014;Yanagi and Chikatsu, 2016) areas.…”

Section: Introductionmentioning

confidence: 95%

“…Inter-sensor comparisons reveal differences in surface reconstruction and reflectance values between common consumer-grade UAS platforms (Sona et al, 2014;Yanagi and Chikatsu, 2016). Moreover, other studies have found differences in mapping products produced by different SfM software packages for forested (Fraser and Congalton, 2018;Kameyama and Sugiura, 2021) and unvegetated (Sona et al, 2014;Yanagi and Chikatsu, 2016) areas. Variability introduced by UAS flight protocols and UAS-SfM workflows for mapping dynamic coastal wetland environments has not yet been well explored.…”

Section: Introductionmentioning

confidence: 95%

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Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

DiGiacomo¹,

Giannelli²,

Puckett³

et al. 2022

Front. Remote Sens.

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Coastal wetlands of the Southeastern United States host a high abundance and diversity of critical species and provide essential ecosystem services. A rise in threats to these vulnerable habitats has led to an increased focus on research and monitoring in these areas, which is traditionally performed using manual measurements of vegetative characteristics. As these methods require substantial time and effort, they are often limited in scale and infeasible in areas of dense or impassable habitat. Unoccupied Aircraft Systems (UAS) provide an advantage over traditional ground-based methods by serving as a non-invasive alternative that expands the scale at which we can understand these ecosystems. While recent interest in UAS-based monitoring of coastal wetland habitats has grown, methods and parameters for UAS-based mapping lack standardization. This study addresses variability introduced by common UAS study techniques and forms recommendations for optimal survey designs in vegetated coastal habitats. Applying these parameters, we assess alignment of computed estimations with manually collected measurements by comparing UAS-SfM mapping products to ground-based data. This study demonstrates that, with careful consideration in study design and analysis, there exists great potential for UAS to provide accurate, large-scale estimates of common vegetative characteristics in coastal salt marshes.

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Section: Vertical Errorsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 95%

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Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

DiGiacomo¹,

Giannelli²,

Puckett³

et al. 2022

Front. Remote Sens.

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“…The last category is related to photogrammetric software algorithms. The use of approaches such as Structure from Motion (SfM), Multi-View Stereo (MVS), and conventional affects the accuracy of the resulting datasets [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on the Basis of Spatial Differences

Yurtseven

2019

IJGI

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In this study, different in-situ and close-range sensing surveying techniques were compared based on the spatial differences of the resultant datasets. In this context, the DJI Phantom 3 Advanced and Trimble UX5 Unmanned Aerial Vehicle (UAV) platforms, Zoller + Fröhlich 5010C phase comparison for continuous wave-based Terrestrial Laser Scanning (TLS) system and Network Real Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) receiver were used to obtain the horizontal and vertical information about the study area. All data were collected in a gently (mean slope angle 4%) inclined, flat vegetation-free, bare-earth valley bottom near Istanbul, Turkey (the size is approximately 0.7 ha). UAV data acquisitions were performed at 25-, 50-, 120-m (with DJI Phantom 3 Advanced) and 350-m (with Trimble UX5) flight altitudes (above ground level, AGL). The imagery was processed with the state-of-the-art SfM (Structure-from-Motion) photogrammetry software. The ortho-mosaics and digital elevation models were generated from UAV-based photogrammetric and TLS-based data. GNSS- and TLS-based data were used as reference to calculate the accuracy of the UAV-based geodata. The UAV-results were assessed in 1D (points), 2D (areas) and 3D (volumes) based on the horizontal (X- and Y-directions) and vertical (Z-direction) differences. Various error measures, including the RMSE (Root Mean Square Error), ME (Mean Error) or MAE (Mean Average Error), and simple descriptive statistics were used to calculate the residuals. The comparison of the results is simplified by applying a normalization procedure commonly used in multi-criteria-decision-making analysis or visualizing offset. According to the results, low-altitude (25 and 50 m AGL) flights feature higher accuracy in the horizontal dimension (e.g., mean errors of 0.085 and 0.064 m, respectively) but lower accuracy in the Z-dimension (e.g., false positive volumes of 2402 and 1160 m³, respectively) compared to the higher-altitude flights (i.e., 120 and 350 m AGL). The accuracy difference with regard to the observed terrain heights are particularly striking, depending on the compared error measure, up to a factor of 40 (i.e., false positive values for 120 vs. 50 m AGL). This error is attributed to the “doming-effect”—a broad-scale systematic deformation of the reconstructed terrain surface, which is commonly known in SfM photogrammetry and results from inaccuracies in modeling the radial distortion of the camera lens. Within the scope of the study, the “doming-effect” was modeled as a functional surface by using the spatial differences and the results were indicated that the “doming-effect” increases inversely proportional to the flight altitude.

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“…The choice of the device among the ones available in the market depends on several factors: the size and complexity of the surface to be acquired, its characteristics in terms of colour, shininess and material, the dimension of the smallest details to be measured, the desired accuracy and resolution. Digital Photogrammetry and Macro-Photogrammetry is also a surveying method that can be used for this type of applications, especially where the characteristics of the surface to be surveyed, or the particular situation, make it impossible the use of scanning devices (Fraser 2013, Luhmann, 2010, Yanagi & Chikatsu, 2010. In this paper some experiences in 3D surveying and modelling of objects with very high-resolution are described, carried out by the DICAM Geomatics group of the University of Bologna in multi-disciplinary contexts.…”

Section: Introductionmentioning

confidence: 99%

Very High-Resolution 3d Surveying and Modelling Experiences in Civil Engineering Applications

Girelli

Tini

Bitelli

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper some experiences in 3D modelling of objects with very high-resolution are described, carried out by the DICAM Geomatics group of the University of Bologna in multi-disciplinary contexts within the field of the Civil Engineering. In all the addressed case studies the main aim is the generation of a 3D model of the surface at a sub-millimetric scale, allowing a very accurate characterization of the surface geometry, useful for different purposes. 3D scanning and Structure from Motion photogrammetry have been used to generate the 3D models. In the paper the encountered problems and the adopted solutions in data surveying and processing are underlined, also discussing the added value of very high-resolution 3D modelling in multi-disciplinary activities.

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Performance Evaluation of 3d Modeling Software for Uav Photogrammetry

Cited by 7 publications

References 17 publications

Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on the Basis of Spatial Differences

Very High-Resolution 3d Surveying and Modelling Experiences in Civil Engineering Applications

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