Dataset Information

Intercomparison of photogrammetry software for three-dimensional vegetation modelling.

ABSTRACT: Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and in situ-based reconstructions.

SUBMITTER: Probst A

PROVIDER: S-EPMC6083669 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Intercomparison of photogrammetry software for three-dimensional vegetation modelling.

Probst Alexandra A Gatziolis Demetrios D Strigul Nikolay N

Royal Society open science 20180711 7

Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The eva ...[more]

PMID: 30109055

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Project description:Image-based modeling, and more precisely, Structure from Motion (SfM) and Multi-View Stereo (MVS), is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimeter-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software-Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset (n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets (n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for "High," "Medium," and "Low" quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using "High" and "Medium" quality settings are of small overall magnitude.

Dataset Information

Intercomparison of photogrammetry software for three-dimensional vegetation modelling.

Publications

Intercomparison of photogrammetry software for three-dimensional vegetation modelling.

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