Ubiquitous low cost multi-rotor and fixed wing drones or unmanned aerial vehicles (UAVs) have accelerated the need for reliable, robust, and scalable Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipelines suitable for a variety of flightpath trajectories especially in degraded environments. Feature tracking being a core part of SfM and MVS, is essential for multiview scene modeling and perception, but difficult to evaluate in large scale datasets due to the lack of sufficient ground-truth. For large-scale aerial imagery, accurate camera orientation and dense 3D point cloud accuracy can be used to assess the impact of accurate feature localization and track length. We propose a novel view simulation (or synthesis) framework which generates visually realistic new unseen camera views for feature detection using known high fidelity camera poses for modeling. Seven state-of-the-art local handcrafted and learning-based features are quantitatively evaluated for robustness and matchability within the SfM and MVS pipelines using the open source COLMAP software. Our experimental results provide performance rankings of each feature, using twelve different evaluation metrics across three synthetic city-wide aerial image sequences. We show that recent learned features, SuperPoint and LF-Net, have not only reached the quality of the best handcrafted features like SIFT and SURF, but now outperform them in terms of more accurate 3D camera pose estimates and longer feature tracks. SuperPoint produces 1.51 meter average position error and 0.03° average angular error, while SIFT remains competitive (third best for pose and second overall) with 1.78 meter and 0.11° errors respectively.

With the rapid advancement of miniaturized sensors and Unmanned Airborne System platforms (UAS), scientific measurements that previously were carried out by large government or academic institution programs can now be successfully accomplished by smaller collection activities. In the case of vegetation assessment (e.g., forestry and agriculture), spatial, temporal, radiometric and spectral resolution can be rigorously controlled. Although each of these is important, accurate temporal assessment of chemistry and morphological plant attributes is heavily dependent on spectral resolution and correction. A particular phenomenology of interest is the extraction and modelling of Bidirectional Reflectance Distribution Function data (BRDF). In this effort, an approach is discussed to reflectance calibrate and derive BRDF signatures from soybeans through a newly available multispectral sensor integrated to an industry common UAS platform. Methods were developed to extract the reflectance data across the azimuth and elevation observations and fit these field data to previously derived models from the literature. A modular processing pipeline was developed to allow for implementation of additional algorithms and efficient numerical analysis. Results show that the coefficients derived to fit the modelled BRDF data are consistent across spatial resolution and within cover type. Additionally, the modeled-fit root mean square error was inversely proportional to the spatial resolution of the image data used for signature extraction. In conjunction with more traditional spectral and ratio-based analytical indices, this approach provides important dimensionality in both classification and land-cover assessment applications critical to more accurate temporal vegetation assessment.

In this paper, we evaluated image quality, algorithms and a workflow associated with matching horizons derived from 3D terrain data to ground imagery as a way to visually estimate a geographic position. The evaluation represented a passive, vision-based navigation technique using long-standoff (>1 KM) terrain features and a horizon detection algorithm hosted within an Android-based geospatial application. Our method involved quantitatively grading edge image feature quality based on pixel data between sky and terrain from high to poor. We tested the algorithm and processing to derive a position using images of varied quality, representing fine and gross, and near and far geographic terrain structures. The site chosen for our tests was located near the Organ Mountains in New Mexico to take advantage of largely unobstructed, long-distance features that challenged both image quality and horizon detection. Testing used the Samsung S23 Ultra (S23U) phone’s primary internal camera to acquire the necessary ground images and native compute power. Our evaluation workflow featured both pre-processing and near-real-time processing elements for position estimations. Pre-processing involved building a Geopackage containing geolocated, synthetic horizons extracted from available 3D terrain data of the test area and camera/sensor configuration data. These data were pre-loaded onto the phone to accomplish the live, near-real-time positional determinations matched to the extracted horizons generated from images acquired by the S23U camera. Our results showed that single-image processing, where only one high-quality ground photo was acquired, 75% of solutions were within 100 m of the actual camera position (compared with the internal sensor-based, Exchangeable Image File Format (EXIF) metadata). Single images of fair quality resulted in positional accuracies where only 38% of the solutions were within 100 m of the EXIF. Improvement was realized when four or more images from varying directions collected from a single location resulted in over 90% of positions falling within 100 m of the EXIF.