Using visibility analysis to improve point density and processing time of SfM‐MVS techniques for 3D reconstruction of landforms

Abstract

Image network geometry, including the number and orientation of images, impacts the error, coverage, and processing time of 3D terrain mapping performed using structure‐from‐motion and multiview‐stereo (SfM‐MVS). Few studies have quantified trade‐offs in error and processing time or ways to optimize image acquisition in diverse topographic conditions. Here, we determine suitable camera locations for image acquisition by minimizing the occlusion produced by topography. Viewshed analysis is used to select the suitable images, which requires a preliminary digital elevation model (DEM), potential camera locations, and sensor parameters. One aerial and two ground‐based image collections were used to analyse differences between SfM‐MVS models produced using: (1) all available images (ALL); (2) images selected using conventional methods (CON); and (3) images selected using the viewshed analysis (VIEW). The resulting models were compared with benchmark point clouds acquired by a terrestrial laser scanner (TLS) and TLS‐derived DEMs. The VIEW datasets produced denser point clouds (28–32% more points) and DEMs with up to 66% reduction in error compared with CON datasets due to reduction of gaps in the DEM. VIEW datasets reduced processing time by 37–76% compared with ALL, with no reduction in coverage or increase in error. DEMs produced with ALL and VIEW datasets had similar slope and roughness, while slight differences that may be locally important were observed for the CON dataset. The new method helps optimize SfM‐MVS image collection strategies that significantly reduce the number of images required with minimal loss in coverage or accuracy over complex surfaces. © 2020 John Wiley & Sons, Ltd.

Using Geomatic Techniques to Estimate Volume–Area Relationships of Watering Ponds

“…The combination of terrestrial SfM-MVS photogrammetry (for the emerged terrain) and GNSS (for the submerged terrain) probed to produce accurate topographic models of small watering ponds, with errors in the range of 1-3 cm. These findings were in agreement with other specific previous applications of the SfM-MVS technique to reconstruct similar features (topographic depressions), such as channels or gully headcuts, also using convergent image network geometries [68,69].…”

“…Additionally, the cost of the equipment is still an important limitation for most users [79]. The camera-based SfM-MVS method provides better coverage in very complex topography or rough surfaces [69,80]. On the other hand, there are some experiences that open the door to the use of a single TLS to survey submerged and emerged terrain, but only under specific hydraulic and physical waterquality conditions [49], and this is not the case for watering ponds in the study area.…”

“…On the other hand, the resulting TLS point clouds commonly show heterogeneous point densities (larger close to stations) and occlusions due to the line-of-sight effect [69]. The TLS coverage may be improved by increasing the number of TLS locations [69], which is not always efficient, as the setup of the equipment and data acquisition may take approximately 30 min for each station, and the resulting cloud may show unnecessarily high point densities in other regions. Additionally, the cost of the equipment is still an important limitation for most users [79].…”

3D Reconstruction of Coastal Cliffs from Fixed-Wing and Multi-Rotor UAS: Impact of SfM-MVS Processing Parameters, Image Redundancy and Acquisition Geometry

“…Image redundancy could be a key parameter for processing excessive image datasets acquired frequently in coastal surveys with multi-rotor UAS in manual operated flights or in autonomous operated flights performed by the autopilot and flight/mission planning software [19]. The Metashape "Reduce overlap" tool was very effective for reducing the number of images and therefore for decreasing the memory requirements and processing time while maintaining the 3D point density, data gaps and the accuracy of the reconstructed cliff surface that could be achieved by using the full image dataset.…”

“…Off-nadir aerial coverages were adopted in several UAS-based surveys such as in agroforestry [14,15], urban areas [16,17] and coastal cliffs [7,8,11,18]. In fact, the acquisition geometry of aerial coverages from UAS can be a key factor in the geometric accuracy of the Bundle Block Adjustment (BBA), the density of points in the cloud and the reduction of the occlusions, that is, in reducing the gaps (holes) in point clouds [19]. Combining nadiral and oblique (off-nadir) images optimizes the photogrammetric surveys in stepped areas [20], improves the accuracy of the generated 3D models, but also increases the processing time [21,22].…”