Feasibility of employing a smartphone as the payload in a photogrammetric UAV system

Kim, Jin-Soo; Lee, Seongkyu; Ahn, Ho-yong; Seo, Dong Il; Park, Soyoung; Choi, Chuluong

doi:10.1016/j.isprsjprs.2013.02.001

Cited by 43 publications

(29 citation statements)

References 18 publications

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“…The choice of vehicle for positioning sensors directly impacts the scale of research that can be conducted as well as the achievable sensor resolution and associated costs (for review of vehicles, considerations, and limitations, see White et al, 2012;Sankaran et al, 2015). For phenotyping large ecological systems, satellites and manned aircraft remain the only practical option (Kerr and Ostrovsky, 2003;Asner et al, 2012), but small unmanned aircraft systems (UASs) are now a viable alternative for smaller, geographically distinct areas (Kim et al, 2013). Finally, for small-scale experimental field studies, several vehicle options include high-clearance tractors, cable gantries, cranes, linear move irrigation systems, and small UASs in addition to stationary observation platforms (Haberland, 2010;Andrade-Sanchez et al, 2013;Busemeyer et al, 2013a;Chapman et al, 2014;Liebisch et al, 2015).…”

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confidence: 99%

The quest for understanding phenotypic variation via integrated approaches in the field environment

et al. 2016

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confidence: 99%

The quest for understanding phenotypic variation via integrated approaches in the field environment

et al. 2016

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“…GCP and CP residuals were compared between PhotoScan and DBAT in Experiment 1 and PhotoScan and Apero in Experiment 2. This method of bundle adjustment assessment is often used in the literature [52][53][54]. After the comparisons were performed and the results validated, metrics from the open source algorithms were then used to perform a quality assessment of the project.…”

Section: Methodsmentioning

confidence: 99%

Open Source and Independent Methods for Bundle Adjustment Assessment in Close-Range UAV Photogrammetry

Murtiyoso

Grussenmeyer

Börlin

et al. 2018

Drones

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Close-range photogrammetry as a technique to acquire reality-based 3D data has, in recent times, seen a renewed interest due to developments in sensor technologies. Furthermore, the strong democratization of UAVs (Unmanned Aerial Vehicles) or drones means that close-range photogrammetry can now be used as a viable low-cost method for 3D mapping. In terms of software development, this led to the creation of many commercial black-box solutions (PhotoScan, Pix4D, etc.). This paper aims to demonstrate how the open source toolbox DBAT (Damped Bundle Adjustment Toolbox) can be used to generate detailed photogrammetric network diagnostics to help assess the quality of surveys processed by the commercial software, PhotoScan. In addition, the Apero module from the MicMac software suite was also used to provide an independent assessment of the results. The assessment is performed by the careful examination of some of the bundle adjustment metrics generated by both open source solutions. A UAV project was conducted on a historical church in the city center of Strasbourg, France, in order to provide a dataset with a millimetric level of precision. Results showed that DBAT can be used to reprocess PhotoScan projects under similar conditions and derive useful metrics from them, while Apero provides a completely independent verification of the results of commercial solutions. Overall, this paper shows that an objective assessment of photogrammetric results is important. In cases where problems are encountered in the project, this assessment method can be useful to detect errors that may not be explicitly presented by PhotoScan.

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“…In the past few decades, the UAV communities have made significant progresses in light-weighted sensor and UAV system developments [21,22], improving data preprocessing [23], registration [24][25][26][27], and image matching [28][29][30]. As a result, the low-cost system and automated mapping software have allowed users to conveniently apply UAV to various applications including change detection [31], leaf area index (LAI) mapping [32], vegetation cover estimation [33], vegetation species mapping [34], water stress mapping [35], power line surveys [36], building detection and damage assessment [37,38], and comparison with terrestrial LiDAR surveys [39].…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

et al. 2017

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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

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Feasibility of employing a smartphone as the payload in a photogrammetric UAV system

Cited by 43 publications

References 18 publications

The quest for understanding phenotypic variation via integrated approaches in the field environment

The quest for understanding phenotypic variation via integrated approaches in the field environment

Open Source and Independent Methods for Bundle Adjustment Assessment in Close-Range UAV Photogrammetry

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

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