Enhancing UAV–SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images

Abstract

Complex landscapes with high topographic relief and intricate geometry present challenges for complete and accurate mapping of both lateral (x, y) and vertical (z) detail without deformation. Although small uninhabited/unmanned aerial vehicles (UAVs) paired with structure-from-motion (SfM) image processing has recently emerged as a popular solution for a range of mapping applications, common image acquisition and processing strategies can result in surface deformation along steep slopes within complex terrain. Incorporation of oblique (off-nadir) images into the UAV–SfM workflow has been shown to reduce systematic errors within resulting models, but there has been no consensus or documentation substantiating use of particular imaging angles. To address these limitations, we examined UAV–SfM models produced from image sets collected with various imaging angles (0–35°) within a high-relief ‘badland’ landscape and compared resulting surfaces with a reference dataset from a terrestrial laser scanner (TLS). More than 150 UAV–SfM scenarios were quantitatively evaluated to assess the effects of camera tilt angle, overlap, and imaging configuration on the precision and accuracy of the reconstructed terrain. Results indicate that imaging angle has a profound impact on accuracy and precision for data acquisition with a single camera angle in topographically complex scenes. Results also confirm previous findings that supplementing nadir image blocks with oblique images in the UAV–SfM workflow consistently improves spatial accuracy and precision and reduces data gaps and systematic errors in the final point cloud. Subtle differences among various oblique camera angles and imaging patterns suggest that higher overlap and higher oblique camera angles (20–35°) increased precision and accuracy by nearly 50% relative to nadir-only image blocks. We conclude by presenting four recommendations for incorporating oblique images and adapting flight parameters to enhance 3D mapping applications with UAV–SfM in high-relief terrain.

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

“…Aerial imaging, as applied in this study, may also be limited in capturing significantly occluded panicles [75]. The value of applying both nadir and oblique has been demonstrated in other UAS-based studies [76][77][78]. In our case multi-angle images should provide better view of panicles and likely lead to better detection.…”

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

“…The advent of UAVs as carrier platforms of active and passive mapping sensors had a huge impact in the field of photogrammetry and remote sensing [14]. The introduction of Structure-from-Motion (SfM) and Dense Image Matching (DIM) techniques providing automatic orientation of entire image blocks and height estimates for every image pixel [15,16] has democratized image-based 3D mapping of topography and dramatically increased the achievable point densities [17,18]. The applicability of UAV-photogrammetry is further facilitated due to the existence of easy-to-use software solutions Remote Sens.…”

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“…For example, if wanting to produce a fine spatial resolution map, or look at animal species relationship to habitat structure, absolute precision and accuracy might not be as important as it would be with wanting to establish plant below ground biomass derived from above ground biomass (13) or other volume critical measurements (58). Alongside the aforementioned, there are some more useful general principles for data collection which can be applied to any and all SfM-MVS projects; Collect data with as high an overlap as is practicable – Nearly every SfM-MVS software will have its own guidelines for the amount of side and forward overlap between images, these range from 60%/60% (63) to 60%/80% (64), though recent studies have shown that higher overlap, upto 90% in both side and forward, is better for accuracy and precision within SfM-MVS derived data (65). Collect both nadir and oblique imagery - James and Robson (66) demonstrated that nadir - that is looking straight down - aerial images captured via drone, along with inherent lens distortion of a camera, can induce a systematic error in the form of a doming effect in DEMs.…”

“…Collect both nadir and oblique imagery - James and Robson (66) demonstrated that nadir - that is looking straight down - aerial images captured via drone, along with inherent lens distortion of a camera, can induce a systematic error in the form of a doming effect in DEMs. Cunliffe et al (13) have shown that incorporating oblique imagery can help improve the accuracy of SfM-MVS derived DTMs, whilst Nesbit and Hugenholtz (65) have gone on to quantify that incorporating oblique imagery between 20–35° from nadir, along with higher overlap can improve both precision and accuracy by up to 50%. Collect data over a wider area than is required – SfM-MVS data quality deteriorates towards the edges of a reconstruction. Making sure your AOI (area of interest) is central within the data set will ensure there is enough overlap and subsequently be of higher quality (60).…”

“…Collect data with as high an overlap as is practicable – Nearly every SfM-MVS software will have its own guidelines for the amount of side and forward overlap between images, these range from 60%/60% (63) to 60%/80% (64), though recent studies have shown that higher overlap, upto 90% in both side and forward, is better for accuracy and precision within SfM-MVS derived data (65).…”