Digital elevation models (DEMs) are critical to a wide range of geoscience investigations. High-latitude and polar regions are particularly challenging for automated, stereo-photogrammetric DEM extraction due to the abundance of surfaces that are low-contrast and repetitively textured, such as snow and shadowed terrain, and have discontinuities such as in crevasse fields, glacier calving faces or iceberg edges. Sub-meter, stereo-mode satellite imagery of high geometric and radiometric quality is becoming increasingly accessible, offering the potential for dramatically increasing the spatial coverage and quality of high-latitude DEMs. Here we demonstrate and validate automated DEMs generated from the Surface Extraction with Triangulated Irregular Network-based Search-space Minimization (SETSM) algorithm designed for these challenging terrains using only the satellite rational polynomial coefficients (RPCs). Comparison between 2-m DEMs created from WorldView image pairs and low-altitude LiDAR point clouds i...

Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions

Unmanned aerial vehicle (UAV) images have gained extensive attention in varying fields, and the Structure from Motion (SfM) technique has become the gold standard for aerial triangulation of UAV images. With increasing data volume caused by the use of multi-view and high-resolution imaging systems and the enhancement of UAV platform’s endurance, the capability for orientation of large-scale UAV images is becoming a prominent and necessary feature for SfM-based solutions. A classical SfM pipeline consists of three major steps, i.e., (i) feature extraction for an individual image, (ii) feature matching for each image pair, and (iii) parameter solving based on iterative bundle adjustment. Most of the time costs are consumed in the second and third steps. This can be explained from three main aspects. First, for feature matching the large number of images and high overlapping degrees cause high combinational complexity of match pairs. Second, the efficiency of commonly utilized techniques for outlier removal would be seriously degenerated because of high outlier ratios of initial matches. Third, for parameter solving of camera poses and scene structures, the iterative execution of bundle adjustment (BA) leads to high computational costs in the incremental SfM workflow. Thus, this paper gives a systematic survey of the state-of-the-art for match pair selection from both ordered and unordered datasets, for outlier removal of initial matches dominated by outliers, and for efficiency improvement of BA, and conducts an experimental evaluation for six well-known SfM-based software packages on UAV image orientation.

Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.

/pdf/automated-ortho-rectification-of-uav-based-hyperspectral-4umwnnwb04.pdf

Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm, and the axis is optimized using a global extraction strategy based on segment-wise fitting. The cross-sectional planes, which are orthogonal to the central axis, are then determined for every interval. The cross-sectional points are extracted by intersecting straight lines that rotate orthogonally around the central axis within the cross-sectional plane with the tunnel point cloud. An interpolation algorithm based on quadric parametric surface fitting, using the BaySAC (Bayesian SAmpling Consensus) algorithm, is proposed to compute the cross-sectional point when it cannot be acquired directly from the tunnel points along the extraction direction of interest. Because the standard shape of the tunnel cross section is a circle, circle fitting is implemented using RANSAC to reduce the noise. The proposed approach is tested on terrestrial point clouds that cover a 150-m-long segment of a Shanghai subway tunnel, which were acquired using a LMS VZ-400 laser scanner. The results indicate that the proposed quadric parametric surface fitting using the optimized BaySAC achieves a higher overall fitting accuracy (0.9 mm) than the accuracy (1.6 mm) obtained by the plain RANSAC. The results also show that the proposed cross section extraction algorithm can achieve high accuracy (millimeter level, which was assessed by comparing the fitted radii with the designed radius of the cross section and comparing corresponding chord lengths in different cross sections) and high efficiency (less than 3 s/section on average).

Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds

Mobile Laser Scanner data for automatic surface detection based on line arrangement

Fitting primitives is of great importance for remote sensing applications, such as 3-D modeling and as-built surveys This letter presents a method for fitting primitives that fuses the Bayesian sample consensus (BaySAC) algorithm with a statistical testing of candidate model parameters for unorganized 3-D point clouds Instead of randomly choosing initial data sets, as in the random sample consensus (RANSAC), we implement a conditional sampling method, which is the BaySAC, to always select the minimum number of data required with the highest inlier probabilities As the primitive parameters calculated by the different inlier sets should be convergent, this letter presents a statistical testing algorithm for the histogram of the candidate model parameter to compute the prior probability of each data point Moreover, the probability update is implemented using the simplified Bayes formula The proposed approach is tested with the data sets of planes, tori, and curved surfaces The results show that the proposed optimized BaySAC can achieve high computational efficiency (five times higher than the efficiency of the RANSAC for fitting a subset of 12 500 points) and high fitting accuracy (on average, 20% higher than the accuracy of the RANSAC) Moreover, the strategy of prior probability determination is proven to be model-free and, thus, highly applicable

An Optimized BaySAC Algorithm for Efficient Fitting of Primitives in Point Clouds

This paper proposes a robust image-matching method, which integrates SIFT with the optimized Bayes SAmpling Consensus (BaySAC). As the point correspondences are likely contaminated by outliers, we present a novel robust estimation method involving an efficient BaySAC for eliminating falsely accepted correspondences. The key points of the proposed hypothesis testing algorithm are determining and updating the prior probabilities of pseudo-correspondences. First, we propose a strategy for prior probability determination in terms of the statistical characteristics of a deterministic mathematical model for hypothesis testing. Moreover, the inlier probability updating is simplified based on a memorable form of Bayes’ Theorem. The proposed approach is validated on a variety of image pairs. The results indicate that when compared with the performance of RANdom SAmpling Consensus (RANSAC) and the original BaySAC, the proposed optimized BaySAC consumes less computation and obtains higher matching accuracy when the hypothesis set is contaminated with more outliers.

A Robust Image Matching Method based on Optimized BaySAC

PurposeDesigning and implementing effective strategies for managing heritage resources throughout the world has become critically important as the impacts of climate change and human-caused destruction are increasingly felt. Of particular importance is the ability to identify and track fast- and slow-moving processes associated with weathering, erosion and the movement or removal of heritage objects by natural and human agents. In this paper, the authors demonstrate how 3D laser scanning can be used to detect and monitor changes to the Okotoks Erratic “Big Rock” Provincial Historic Resource in Alberta, Canada, over a period of 7 years.Design/methodology/approachTerrestrial laser scanning surveys of the Okotoks Erratic “Big Rock” Provincial Historic Resource were undertaken in 2013, 2016 and 2020. Registration was used to place the three epochs of point clouds into a unique datum for comparison using the cloud-to-cloud distance function in Cloud Compare.FindingsThe movement/repositioning of rocks around the base of the erratic, the emergence of “unofficial” paths and changes to interpretive trails and fencing were all identified at the site over the time period of the study.Practical implicationsCurrent conservation at the Okotoks Big Rock focus primarily on the rock art panels that are scattered over the erratic. The results of this study indicate they should be broadened so that the geological integrity of the site, which is intrinsically linked to its cultural value, can also be maintained.Originality/valueThis is the first study the authors are aware of that utilizes terrestrial laser scanning + change detection analysis to identify and track changes to a heritage site over a period as long as 7 years.

A method for detecting and monitoring changes to the Okotoks Erratic – “Big Rock” provincial historic site

: Applications using terrestrial laser scanners (TLS) have been skyrocketing in the past two decades. In a scanning project, the conﬁguration of scans is a critical issue as it has signiﬁcant effects on the project cost and the quality of the product. In this paper, a practical strategy is proposed to resolve the problem of the optimal placement of the terrestrial laser scanner. The method attempts to reduce the number of viewpoints under the premise that the scenes are fully covered. In addition, the approach is designed in a way that the solutions can be efﬁciently explored. The method has been tested on 540 polygons simulated with different sizes and complexities. The results have also been compared with a benchmark strategy in terms of the optimality of the solutions and runtime. It is concluded that our proposed algorithm ties or reduces the number of viewpoints in the benchmark paper in 85.6% of the 540 tests. For complex environments, the method can potentially reduce the project cost by 10%. Although with relatively lower efﬁciency, our method can still reach the solution within a few minutes for a polygon with up to 500 vertices.

A Practical Algorithm for the Viewpoint Planning of Terrestrial Laser Scanners

Reservoir development in the petroleum industry starts with the drill bit. A drill bit’s dull condition must be closely monitored since it significantly influences the efficiency and the cost of drilling operations. The dull condition check procedure is called drill bit grading and is essentially a change detection problem to determine the state of the drill bit, in particular the wear of the cutting teeth inserts. Currently, the grading is conducted manually on-site, which is error-prone and highly subjective. Laser scanning technology offers a potential solution to overcome the shortcomings of existing practice. The integration of LiDAR (Light Detection and Ranging) on the newly-launched iDevices, the iPhone 12 Pro and the iPad Pro 2020 offers new opportunities for close-range measurement given their huge customer base and low cost. The goal of this research is to investigate the performance of these devices, and to develop a tool for the drill bit grading. Since bit grading is significantly impacted by the performance of the sensor, several basic tests were first conducted under controlled experimental conditions, e.g., the room temperature and ambient lighting and measurement surface. The temporal stability of the iDevices was examined by capturing a series of datasets of a flat wall over forty-five (45) minutes, then the effect of range, reflectivity and incidence angle on data quality was tested by measuring the Spectralon targets at different situations. The performance tests found that using only the LiDAR data was not sufficient for drill bit grading. Thus, a preliminary grading system based on the fusion of LiDAR and color camera is proposed by modelling the post-drilling bit and detecting the changes.

/pdf/drill-bit-grading-using-lidar-and-imagery-on-the-apple-smart-1zfdd3m4.pdf

Fengman Jia

Papers

An Optimized BaySAC Algorithm for Efficient Fitting of Primitives in Point Clouds

A Robust Image Matching Method based on Optimized BaySAC

A method for detecting and monitoring changes to the Okotoks Erratic – “Big Rock” provincial historic site

A Practical Algorithm for the Viewpoint Planning of Terrestrial Laser Scanners

Drill bit grading using LiDAR and imagery on the apple smart devices