Application Of Photogrammetry Research Articles

Applications of Photogrammetry in Cadastral Inventory of the Pubic Rural Land

Abstract The purpose of this work is to describe the realization of a photogrammetric mapping work, i.e. the creation of a high-resolution orthophotoplan mosaic, as a support in the inventory of land parcels belonging to the public domain of a territorial administrative unit in the rural area, subsequent registration in the Integrated Cadaster and Land Registry System, with all related technical-legal aspects.

Application of UAV photogrammetry and electrical resistivity tomography for characterization of a complex landslide: a case study from northwest Himalayas, Pakistan

Application of UAV photogrammetry and electrical resistivity tomography for characterization of a complex landslide: a case study from northwest Himalayas, Pakistan

Validation of three-dimensional facial imaging captured with smartphone-based photogrammetry application in comparison to stereophotogrammetry system

Statement of problemThe development of facial scanners has improved capabilities to create three-dimensional (3D) virtual patients for accurate facial and smile analysis. However, most of these scanners are expensive, stationary and involve a significant clinical footprint. The use of the Apple iPhone and its integrated “TrueDepth” near-infrared (NIR) scanner combined with an image processing application (app) offers the potential to capture and analyze the unique 3D nature of the face; the accuracy and reliability of which are yet to be established for use in clinical dentistry. PurposeThis study was designed to validate both the trueness and precision of the iPhone 11 Pro smartphone TrueDepth NIR scanner in conjunction with the Bellus3D Face app in capturing 3D facial images in a sample of adult participants in comparison to the conventional 3dMDface stereophotogrammetry system. Material and methodsTwenty-nine adult participants were prospectively recruited. Eighteen soft tissue landmarks were marked on each participant's face before imaging. 3D facial images were captured using a 3dMDface system and the Apple iPhone TrueDepth NIR scanner combined with the Bellus3D Face app respectively. The best fit of each experimental model to the 3dMD scan was analyzed using Geomagic Control X software. The root mean square (RMS) was used to measure the “trueness” as the absolute deviation of each TrueDepth scan from the reference 3dMD image. Individual facial landmark deviations were also assessed to evaluate the reliability in different craniofacial regions. The “precision” of the smartphone was tested by taking 10 consecutive scans of the same subject and comparing those to the reference scan. Intra-observer and inter-observer reliabilities were assessed using the intra-class correlation coefficient (ICC). ResultsRelative to the 3dMDface system, the mean RMS difference of the iPhone/Bellus3D app was 0.86 ± 0.31 mm. 97% of all the landmarks were within 2 mm of error compared with the reference data. The ICC for intra-observer reproducibility or precision of the iPhone/Bellus3D app was 0.96, which was classified as excellent. The ICC for inter-observer reliability was 0.84, which was classified as good. ConclusionsThese results suggest that 3D facial images acquired with this system, the iPhone TrueDepth NIR camera in conjunction with the Bellus3D Face app, are clinically accurate and reliable. Judicious use is advised in clinical situations that require high degrees of detail due to a lack of image resolution and a longer acquisition time. Generally, this system possesses the potential to serve as a practical alternative to conventional stereophotogrammetry systems for use in a clinical setting due to its accessibility and relative ease of use and further research is planned to appraise its updated clinical use.

The Spatial Analysis of Vegetation Cover and Permafrost Degradation for a Subarctic Palsa Mire Based on UAS Photogrammetry and GPR Data in the Kola Peninsula

Subarctic palsa mires undergo substantial transformation under climate impacts, and today a reliable marker of their degradation is the vegetation cover. We studied the correspondence between the surface traits of palsa degradation, as expressed in the vegetation composition, and the interior condition of permafrost within subarctic palsa mires in the central part of the Kola Peninsula. We have employed a set of methods to collect the data, including geobotanical relevés, unmanned aerial system (UAS) photogrammetry, and ground-penetrating radar (GPR) survey. Based on RGB orthophoto values and morphometric variables, we produced a land cover classification (LCC) consistent with the vegetation classes identified during field measurements. The outcome proves that the additional morphometric predictors improve the accuracy of classification algorithms. We identified three major patterns in GPR cross-sections defining (i) permafrost in palsas, (ii) water saturated peat, and (iii) the regular peat layer. As a result, our GPR data demonstrated a high correlation with land cover classes and pointed to some vegetation features controlled by the peat deposit inner structure. Under our results, palsas with thawing permafrost can be appraised using sequences of LCC. This is primarily the lichen hummock—tall shrub—carpet vegetation (LH–TSh–C) sequence from palsa top to foot. We have also detected an asymmetric configuration of permafrost in some palsas in the west-to-east direction and hypothesized that it can relate to the wind regime of the area and snow accumulation on the eastern slopes. Our results highlight that the combined application of the remote UAS photogrammetry and GPR survey enables a more precise delineation of the lateral degradation of palsas.

A deep learning-based automatic tool for measuring the lengths of linear scars: forensic applications.

It is important to measure scars in forensic and clinical medicine. In practice, scars are mostly manually measured, and the results are diverse and influenced by various subjective factors. With the development of digital image technology and artificial intelligence, noncontact and automatic photogrammetry has been gradually used in some practical applications. In this article, we propose an automatic method for measuring the length of linear scars based on multiview stereo and deep learning, which combines the 3D reconstruction algorithm of structure from motion and the image segmentation algorithm based on a convolutional neural network. With a few pictures taken by a smart phone, automatic segmentation and measurement of scars can be realized. The reliability of the measurement was first demonstrated through simulation experiments on five artificial scars, giving errors of length <5%. Then, experiment results on 30 clinical scar samples showed that our measurements were in high agreement with manual measurements, with an average error of 3.69%. Our study demonstrates that the application of photogrammetry in scar measurement is effective and that the deep learning technique can realize the automation of scar measurement with high accuracy.

Application of UAV photogrammetry for the assessment of forest structure and species network in the tropical forests of Southern Nigeria

Using the object-based image analysis (OBIA) technique, this study examined structural characteristics, quantified carbon stocks, and identified tree species in the tropical forests of southwestern Nigeria. The Structure from Motion (SfM) technique was used to produce orthomosaics with a ground sampling distance of 10 m from orthophotos recorded during a survey and optimized for forest assessment with the near-infrared band. In addition to tree species variables, GPS coordinates, tree diameter, and tree crown were all employed to estimate carbon stock in the study area. Carbon stock estimates of 450 tons per hectare were estimated , with a tree-specific range of 65 kg per tree to 8,488 kg per tree. There was a correlation between tree canopies and diameters, but no conclusive relationships between tree species distribution and position in their natural formations. OBIA significantly enhanced the current carbon inventory and provided a spatial characterization of species distribution in the research area.

Application of Three-Dimensional Digital Photogrammetry to Quantify the Surface Roughness of Milk Powder.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.

COMPARISON OF UAV IMAGE SPATIAL RESOLUTION BASED ON THE SIEMENS STAR TARGET

Abstract. In recent years, as the use of Unmanned Aerial Vehicle (UAV) imaging systems has increased, the photogrammetry community has conducted extensive research on the unique advantages of these systems. The UAVs are considered as one of the most important platforms for photogrammetry applications from various urban and non-urban areas at different scales. In UAV photogrammetry projects the spatial resolution of the images must be determined prior to the imaging stage. The spatial resolution of the images is a commonly-used criterion for detecting the smallest distance between two adjacent separable objects in the images. Numerous methods have been developed to precisely evaluate the spatial resolution of images. In this study, the Siemens star target, which is one of the most commonly used artificial targets for analysing spatial resolution was studied. The objective of this paper is to evaluate and compare the reduction of spatial resolution coefficient using the Siemens star target in images captured by UAVs. To this end, a method for automatically detecting the radius of the circle of ambiguity and calculating spatial resolution in UAV images has been developed. According to the findings of this study, the initial step in creating the Siemens star target, in terms of size and the number of acceptable arms, is dependent on the flying altitude of the UAV and the level of image blur. In addition, the reduction in spatial resolution of images captured by various UAVs varies, and its coefficient must be calculated for each project.

Lineament Extraction Analysis Using Digital Elevation Model (DEM) in Lahendong Geothermal Area, North Sulawesi

Sulawesi is located in the triple junction which makes it has a high complex tectonic setting implicated for the emergence of geothermal potential. Existing geological structure near the research area is only in the top-right of the research area which far from any of the hot spring points. This research aims to analyze the lineament extraction to find out the alleged structure and the density area to determine the permeability of the Lahendong geothermal area using National Digital Elevation Model (DEMNAS) data imagery. Digital Elevation Model (DEM) data is processed using Remote Sensing Photogrammetry application to produce lineament extraction. Lineament density map is carried out using a Geographic Information System (SIG) application and then processed to generate rosette diagram. Based on the lineament extraction and lineament density analysis result, we can interpret that the Lahendong geothermal area is dominated with high density, which is interpreted as good permeability. Lineaments in the Lahendong geothermal area is northwest-southeast and almost north-south. The extracted lineament is also providing the alleged structure in the research area.

Determining Forest Fire Position from UAV Photogrammetry using Color Filtration Algorithm

Forest fires frequently happen worldwide, especially in the dry season. A forest fire early warning system (EWS) is needed to prevent this disaster. The main part of EWS is the hotspot detection system. On the other side, Unmanned Aerial Vehicle (UAV) technology offers an alternative solution to detect the hotspot for poor satellite image processing accuracy. Remote sensing techniques with UAV working drones are progressively challenging. Drones can provide results in 2D and 3D images with high resolution and real-time. Therefore, in this research, we have used a photogrammetry application from the number of images collected by a UAV with an optimum flight plan for the mission to determine the location of the forest fire. This paper describes remote sensing experiments using drones to detect land fires. The experiment was carried out using a quadcopter drone of the DJI Phantom 4 Pro. The photos are processed using Agisoft Metashape Professional image processing software and become a 2D image. These images captured a fire simulation in a known location. After a high resolution (GSD – Ground Sampling Distance – 0.87cm/px) orthophoto had been generated, a color filtration algorithm detected a hotspot to detect a fire at the exact location. The results are almost zero deviation of longitude and latitude from the real location with 1.44 m2 and 1.06 m2 fire area from 2 experiments. This algorithm program has TPR and FPR are 0,78 and 0, respectively. Further research can develop an EWS with a combination of UAV and Wireless Sensor Networks.

Application of photogrammetry and in-situ test technology in the stability evaluation of gangue dump slope

Application of photogrammetry and in-situ test technology in the stability evaluation of gangue dump slope

Determination of the coefficient of proportionality between horizontal displacement and tilt change using UAV photogrammetry

To date, research on the use of unmanned aerial vehicle (UAV) photogrammetry to monitor deformations caused by underground mining has focused on the methods and accuracy of determining displacements. This study presents the results of research on the new application of UAV photogrammetry to determine the value of the coefficient of proportionality (B) between horizontal displacements and the change in the tilt of the terrain surface. The developed methodology for processing photogrammetric products is fully automated and robust. It includes automatic determination of displacement and the values of the B coefficient, as well as outlier removal. The processing results were compared to reference data derived from measurements on observation lines. The analyzed UAV photogrammetric products allowed the displacements to be determined with an accuracy of 2–3 cm for the horizontal component and 3–5 cm for the subsidence. As a result of the analyses, it was also found that the average value of the B coefficient determined using the proposed methodology is consistent with the average reference value. The values of the B coefficient based on UAV photogrammetry are also characterized by a similar coefficient of variation as the reference values. However, the accuracies obtained from the proposed methodology are not sufficient for studying the spatial distribution of the B coefficient, mainly due to the influence of the relatively low accuracy of the tilt determined from UAV photogrammetry. The improvement of the accuracy of the tilt and B coefficient determined from UAV photogrammetry will be the subject of further research.

Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest.

This paper investigated the utility of drone-based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The use of drone-derived images and the subsequent application of digital photogrammetry to obtain information about PNFs are underinvestigated in southeast Queensland vegetation types. In this study, we used image processing to separate individual trees and digital photogrammetry to derive a canopy height model (CHM). The study was supported with tree height data collected in the field for one site. The paper addressed the research question “How well do drone-derived point clouds estimate the height of trees in PNF ecosystems?” The study indicated that a drone with a basic RGB camera can estimate tree height with good confidence. The results can potentially be applied across multiple land tenures and similar forest types. This informs the development of drone-based and remote-sensing image-processing methods, which will lead to improved forest inventories, thereby providing forest managers with recent, accurate, and efficient information on forest resources.

Analysis of the Applicability of Photogrammetry in Building Façade

Abstract This article evaluates the accuracy of 3D models made from point clouds obtained from photogrammetry. Photographs were taken from ground level and using a drone, and data processing was performed in 3DF Zephyr. The models were compared with the actual dimensions of the buildings. Four different building objects with varying degrees of complexity were analysed. The aim of the research is to analyse the conditions for taking photographs and how they are transformed into a point cloud, and to see how and whether the complexity of the shape of the facade affects the accuracy of the 3D model made from the point cloud. The inaccuracy of the point cloud in the form of point spread for all analysed cases was 1.8±0.4 cm on average. The largest measurement error was found in the case of a multi-storey building. Despite the presented inaccuracies, it was considered advantageous to use the point cloud obtained through photogrammetry in the inventory. No difference was observed in the accuracy of the model depending on the complexity of the building. Recommendations were made regarding the conditions for taking photographs.

Large Aerial Image Tie Point Matching in Real and Difficult Survey Areas via Deep Learning Method

Image tie point matching is an essential task in real aerial photogrammetry, especially for model tie points. In current photogrammetry production, SIFT is still the main matching algorithm because of the high robustness for most aerial image tie points matching. However, when there is a certain number of weak texture images in a surveying area (mountain, grassland, woodland, etc.), these models often lack tie points, resulting in the failure of building an airline network. Some studies have shown that the image matching method based on deep learning is better than the SIFT method and other traditional methods to some extent (even for weak texture images). Unfortunately, these methods are often only used in small images, and they cannot be directly applied to large image tie point matching in real photogrammetry. Considering the actual photogrammetry needs and motivated by the Block-SIFT and SuperGlue, this paper proposes a SuperGlue-based LR-Superglue matching method for large aerial image tie points matching, which makes learned image matching possible in photogrammetry application and promotes the photogrammetry towards artificial intelligence. Experiments on real and difficult aerial surveying areas show that LR-Superglue obtains more model tie points in forward direction (on average, there are 60 more model points in each model) and more image tie points between airline(on average, there are 36 more model points in each adjacent images). Most importantly, the LR-Superglue method requires a certain number of points between each adjacent model, while the Block-SIFT method made a few models have no tie points. At the same time, the relative orientation accuracy of the image tie points matched by the proposed method is significantly better than block-SIFT, which reduced from 3.64 μm to 2.85 μm on average in each model (the camera pixel is 4.6 μm).

VLSG-SANet: A feature matching algorithm for remote sensing image registration

Finding a reliable correspondence between two feature point sets efficiently and accurately is the first step in many computer vision applications. It is a crucial prerequisite for many remote sensings, target recognition, and photogrammetry applications. However, in different application scenarios, images may undergo different degrees of deformation, which makes the predefined geometric model no longer appropriate. This article attempts to remove mismatches between the set of assumed matching points. In order to achieve this goal, we propose an effective matching method, called ”visualized local structure generation-Siamese attention” network (VLSG-SANet), which transform the task of eliminating mismatching of feature points into a dynamic visual similarity assessment. The VLSG-SANet can generate visual descriptors by using the local structure information about each feature point. We embed the attention module based on the Siamese network for the generated feature descriptors, allowing the network to adjust the descriptors dynamically. In order to prove the robustness and versatility of the VLSG-SANet algorithm, we have performed good experiments on open remote sensing data images and got good experimental results.

FEATURE MATCHING ENHANCEMENT USING THE GRAPH NEURAL NETWORK (GNN-RANSAC)

Abstract. Improving the performance of feature matching plays a key role in computers vision and photogrammetry applications, such as fast image recognition, Structure from Motion (SFM), aerial triangulation, Visual Simultaneous Localization and Mapping (VSLAM), etc., where the RANSAC algorithm is frequently used for outlier detection; note that RANSAC is the most widely used robust approach in photogrammetry and computer vision for outlier detection. It is known that the outlier ratio used in RANSAC primarily determines the number of trial runs needed, which eventually, determines the computation time. Over time, different methods have been proposed to reject the false-positive correspondences and improve RANSAC, such as GR_RANSAC, SuperGlue, and LPRANSAC. The specific objective of this study is to propose a filtering algorithm based on Graph Neural Networks (GNN), as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers. The research is based on the idea that descriptors of corresponding points, as well as their spatial relationship, should be similar in image sequences. In graph representation, built by the adjacency matrix of data (nodes features), there should be similarity for corresponding points that are close to each other in the image domain. From the many GNNs techniques, Graph Attention Networks (GATs) were selected for this study as they assign different importance to each neighbour’s contribution as anisotropic operations, so the features of neighbour nodes are not considered in the same way, unlike other GNNs techniques. In our approach, we build a graph in each image, because the similarity of the two-dimensional spatial relationships between points in the image domain of consecutive images should be similar. Then during processing, points with any significantly different neighbours are considered as outliers. Next, the points can be updated in the GNN layer. GNN-RANSAC is tested experimentally on real image pairs. Clearly, the proposed pre-filtering increases the inlier ratio and results in faster convergence compared to ordinary RANSAC, making it attractive for real-time applications. Furthermore, there is no need to learn the features.

DISASTER DAMAGE INVESTIGATION USING ARTIFICIAL INTELLIGENCE AND DRONE MAPPING

Abstract. This study aims to testify the applicability of UAV photogrammetry and artificial intelligence (AI) for the management of natural disaster. Recently artificial intelligence is considered as an emerging tool for recognizing disaster events from aerial imagery of drones. In this paper, we present firstly the approach related to use of AI techniques for disaster detecting and identification. Secondly, we suggest small easy-to-use UAV-based investigation procedure for natural disaster damaged area in the phase of disaster recovery in Korea. Finally, we evaluate the mapping accuracy and work efficiency of drone mapping for disaster investigation application through comparing with traditional investigation work process which was dependent on labor-intensive field survey. The resolution ortho-image map of within less 5cm of GSD generated by aerial photos acquired from UAVs at the altitude of 100m–250m enabled us to check damage information such as facilities destroy or the trace of soil erosion around the river flooded and reservoir collapsed area. The photogrammetry-based drone mapping technology for the disaster damage investigation is expected to be an alternative approach to support or replace the labor-intensive disaster site survey that needs to investigate the disaster site quickly and timely.

REMOTE SURVEY OF TRADITIONAL DWELLINGS USING ADVANCED PHOTOGRAMMETRY INTEGRATED WITH ARCHIVAL DATA: THE CASE OF LISBON

Abstract. The application of advanced photogrammetry and the integration of cross-sourced maps of historic cityscapes contribute to a thorough understanding of our surrounding, enabling the comparison between present physical layout/use of the built environment and its changes over time. Remote surveys integrated with the analysis of building dossiers, stored in municipal archives, and onsite surveys are carried out in this work for providing insight into a traditional building typology in Portugal, which is so far overlooked in heritage studies. Two examples of humble multi-family dwellings with overhanging timber-framed façades (16th century) are virtually reconstructed using structure-from-Motion (SfM) of selected historic photographs. During a first phase, a photogrammetric reconstruction of the state of the buildings, as conceived originally, was implemented by reconstructing two parts (due to the number of images available for each case study), and then a metric evaluation of the models was conducted comparing past and current state of the buildings. This research shows the importance of integrating distinct tools and databases to foster truly tourists' perception of authenticity in cultural heritage sites and towards a more sensitive heritage management. Potential interactive applications capable of increasing the visibility and impact of these virtual reconstructions, such as GIS data enrichment or digital travel apps, are also briefly discussed.

INTEGRATING GIS AND PHOTOGRAMMETRIC RECORDING FOR EXTENDED SEABED ARCHAEOLOGICAL RESEARCH, MARSA BAGOUSH, EGYPT

Abstract. Photogrammetry and GIS technologies are developing rapidly and becoming more affordable. They have also emerged as significant archaeological tools as a result of an increasing level of automated workflow in data acquisition and processing. Moreover, these techniques offer considerable reduction in the cost of underwater archaeological research. That is in addition to significant enhancement in research plans, management, and results, especially in extended seabed archaeological projects. Therefore, these technologies are utilized as essential aspects of the underwater archaeological research project in the site of Marsa Bagoush, Egypt. The site is located 250 km west of Alexandria, and it takes the form of a bay, almost 650,000 m2. Marsa Bagoush (ancient Zygris) was mentioned by Claudius Ptolemaeus in the 2nd century AD as one of the main anchorages along the northwest coast of Egypt between Alexandria and Marsa Matrouh (ancient Paraetonium).This paper presents the results of the on-going digital documentation of the site which is a major part of an underwater archaeological investigation project. The paper will discuss the promising application of photogrammetry in underwater archaeology. It will also look at the integration of GIS Database techniques, which represent accurate methods of documenting topographical and archaeological elements and obtaining three-dimensional digital models of the site and its contents.