Topographic structure from motion: a new development in photogrammetric measurement

By M. Fonstad, J. Dietrich, Brittany C. Courville, Jennifer L. R. Jensen, P. Carbonneau. 2013

1,136 citations recorded by the source corpus.

The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware and/or software. However, image‐based methods such as digital photogrammetry have been decreasing in costs. Developed for the purpose of rapid, inexpensive and easy three‐dimensional surveys of buildings or small objects, the ‘structure from motion’ photogrammetric approach (SfM) is an image‐based method which could deliver a methodological leap if transferred to geomorphic applications, requires little training and is extremely inexpensive. Using an online SfM program, we created high‐resolution digital elevation models of a river environment from ordinary photographs produced from a workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three‐dimensional space. The basic product of the SfM process is a point cloud of identifiable features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected in the field or from measurements of camera positions at the time of image acquisition. The georeferenced point cloud can then be used to create a variety of digital elevation products. We examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand‐held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low‐altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Copyright © 2012 John Wiley & Sons, Ltd.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/9d833c031b682020f3975c3e759936671b38ff20

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Citations in the corpus

Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests (2017, methodology): “Recent advances in computer science make it possible to generate a point cloud semi-automatically using the Structure from Motion (SfM) approach [7,8].”
Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review (2016, methodology): “It is a 3D reconstruction technique based on conventional 2D images commonly used in architecture [29–32], topography [33,34], geology [35], archaeology [36], engineering [37,38], and manufacturing [39].”
Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries (2017, methodology): “Seven GCPs were used to geo‐reference the models and the remaining 7 were used for accuracy assessment (check points) [9].”
Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, background): “Such approaches require relatively little training and are extremely inexpensive, and thus potentially represent a methodological leap in ad hoc HRT data collection (Fonstad et al., 2013).”
Structure from motion photogrammetry in physical geography (2016, methodology): “123dapp.com/catch), Microsoft Photosynth (https://photosynth.net/) and ARC3D (http:// www.arc3d.be).”
Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments (2013, background): “Whenever a rigid transformation is performed, a crucial assumption is therefore accepted in the SfM; the automated image matching process produces none or little non-linear deformation [19].”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, methodology): “Nowadays, getting a DSM out of UAV photogrammetry data can be as easy as a simple button click with a capable software [91,92], and this is achieved through advanced photogrammetry, structure from motion [93], and highly e ﬃ cient stereo-matching algorithm.”
Connectivity as an emergent property of geomorphic systems (2018, positive): “…primarily by understanding how connectivity affects specific aspects of landscape dynamics, such as the movement of sediment between hillslopes and channels or through a stream network (Fryirs and Brierley, 2001; Fryirs, 2013; Bracken et al ., 2015; Gran and Czuba, 2017; Lane et al ., 2017).”