A Robust Photogrammetric Processing Method of Low-Altitude UAV Images

Ai, Mingyao; Hu, Qingwu; Li, Jiayuan; Wang, Ming; Yuan, Hui; Wang, Shaohua

doi:10.3390/rs70302302

Cited by 52 publications

(31 citation statements)

References 21 publications

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“…In addition, the abovementioned height differences characterizing the different microforms are very small (e.g., variation at a centimeter scale matters; see Figure 2) and require an extremely accurate surface model for a successful integration into the classification process. There is an ongoing discussion in recent research articles about the achievable accuracy of surface models created by UAS based stereo photogrammetric analysis [52][53][54][55]. Thus, it is unclear if this achievable accuracy is sufficient for ecosystems, where the morphology is characterized by slight surface differences.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

Lehmann¹,

Münchberger²,

Knoth

et al. 2016

Remote Sensing

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South Patagonian peat bogs are little studied sources of methane (CH 4 ). Since CH 4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH 4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH 4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species-and 0.83 for the microform-level classification, respectively. CH 4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH 4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH 4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg¨m´2¨day´1 to 26.8 mg¨m´2¨day´1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH 4 fluxes when plot-scale fluxes are up-scaled.

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Section: Discussionmentioning

confidence: 99%

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

Lehmann¹,

Münchberger²,

Knoth

et al. 2016

Remote Sensing

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“…However, despite the existence of well-established workflows in photogrammetry, manned aircraft, and satellite-based remote sensing to address such fundamental aspects, UAS systems introduce various additional complexities, which to date have not been thoroughly addressed. Consequently, best practice workflows for producing high-quality remote sensing products from UAS are still lacking, and further studies that focus on validating UAS-collected measurements with robust processing methods are important for improving the final quality of the processed data [36,37].…”

Section: Data Collection Processing and Limitationsmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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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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A Robust Photogrammetric Processing Method of Low-Altitude UAV Images

Cited by 52 publications

References 21 publications

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

High-Resolution Classification of South Patagonian Peat Bog Microforms Reveals Potential Gaps in Up-Scaled CH4 Fluxes by use of Unmanned Aerial System (UAS) and CIR Imagery

On the Use of Unmanned Aerial Systems for Environmental Monitoring

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

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