Photogrammetric Image Research Articles

Оценка влияния показателей эрозионного потенциала почвы по данным БПЛА на содержание гумуса в условиях опытного поля

The article is devoted to studying the possibilities of digital relief modeling based on UAV survey data when analyzing the spatial distribution of humus in the conditions of an experimental field. The scale of cartographic models was 1:2 000. A key site with an area of 2.62 ha, located within the boundaries of the educational and scientific experimental field of the Perm State Agricultural and Technological University on the territory of the Perm Municipal District of the Perm Territory, was selected as the object of research. The purpose of the study is to assess the spatial distribution of humus using UAV data based on indicators of soil erosion potential. The soil cover of the surveyed area is represented by soddy-podzolic soils of heavy granulometric composition. To obtain a digital terrain model using photogrammetry methods, UAV photography was carried out using a DJI mini 2 quadcopter. Photogrammetric image processing was performed in the Drone Deploy web application. The spatial distribution of humus content in the soils of the key area was determined at 45 points. Soil samples were taken from the depth of the arable layer (0–20 cm) and analyzed in the laboratory of the Department of Agrochemistry and Soil Science of the Perm State Agricultural and Technological University according to GOST 26213-84 with colorimetric finishing. On the territory of the experimental site under study, the humus content has a pronounced spatial autocorrelation. The map of humus content was constructed using the ordinary kriging method. When visually comparing the results of digital relief modeling with a map of the spatial distribution of humus in a key area, the dependence of the increase in the content of organic matter with the values of the elevation of the area, as well as with the distance to the thalwegs (removal zone), is clearly visible. To establish the influence of relief on the humus content in soils, a raster correlation was carried out in the SAGA program, which showed a close relationship between the humus content and the distance to the thalweg and the elevation of the area (r = 0.75). Indicators characterizing moisture content and surface curvature have little effect on the variation of humus content in space.

Multitemporal Quantification of the Geomorphodynamics on a Slope within the Cratère Dolomieu at the Piton de la Fournaise (La Réunion, Indian Ocean) Using Terrestrial LiDAR Data, Terrestrial Photographs, and Webcam Data

In this study, the geomorphological evolution of an inner flank of the Cratère Dolomieu at Piton de La Fournaise/La Réunion was investigated with the help of terrestrial laser scanning (TLS) data, terrestrial photogrammetric images, and historical webcam photographs. While TLS data and the terrestrial images were recorded during three field surveys, the study was also able to use historical webcam images that were installed for the monitoring of the volcanic activity inside the crater. Although the webcams were originally intended to be used only for visual monitoring of the area, at certain times they captured image pairs that could be analyzed using structure from motion (SfM) and subsequently processed to create digital terrain models (DTMs). With the help of all the data, the geomorphological evolution of selected areas of the crater was investigated in high temporal and spatial resolution. Surface changes were detected and quantified on scree slopes in the upper area of the crater as well as on scree slopes at the transition from the slope to the crater floor. In addition to their quantification, these changes could be assigned to individual geomorphological processes over time. The webcam photographs were a very important additional source of information here, as they allowed the observation period to be extended further into the past. Besides this, the webcam images made it possible to determine the exact dates at which geomorphological processes were active.

Assessing the Impact of Prolonged Sitting and Poor Posture on Lower Back Pain: A Photogrammetric and Machine Learning Approach

Prolonged static sitting at the workplace is considered one of the main risks for the development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar discomfort and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. The current study focused on evaluating the relationship between LBP and prolonged sitting in poor posture using photogrammetric images, postural angle calculation, machine learning models, and questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed to recognize poor body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

Digitization of the Virtual Reality Laboratory Through Photogrammetry Tools (SfM)

The present paper focuses on generating a digital laboratory model at the department by applying the structure from motion technique. The generated digital model is then utilized to create simulation models for application in virtual and augmented reality environments. Nine different approaches to photogrammetric imaging in the laboratory were tested in the experiment. These approaches involved variations in the number of photographs and scanning positions. The results of the experiment were divided into three categories: unsuitable, which required significant modification; incomplete, which required further modification; and suitable, which was directly usable. Some images were unusable due to the similarity of objects in the scene. Experiments showed that some parts of the laboratory were more appropriate to image separately. It was also found that dark and shiny surfaces of objects presented a problem for the computation of the models. Therefore, it was necessary to use markers or colours to eliminate this problem.

A new method with C-means segmentation for non-uniform image coordinate system definition in panoramic imaging employing Ladybug2 camera

A panorama ensures a stunning wide-angle field of view up to 360° representation of a scene, exceeding the limits of a normal photograph. Panoramic cameras satisfy the single-viewpoint characteristic. There are several types of panoramic cameras for 360-degree imaging. Multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In a variety of practical implementations of panoramic cameras, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. In this study, not only a defining method of the non-uniform image coordinate system is presented by utilizing the C-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in a panoramic calibration room but also the use of an elliptical panoramic projection coordinate system is defined by the Singular Value Decomposition method in a panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system. It has been observed that the proposed method provides more accurate results for the 3D coordinate definition.

ARM4CH: A Methodology for Autonomous Reality Modelling for Cultural Heritage.

Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site's specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research.

Accuracy analysis of UAV aerial photogrammetry based on RTK mode, flight altitude, and number of GCPs

Abstract The optimization of an unmanned aerial vehicle (UAV) aerial photogrammetry scheme is crucial for achieving higher precision mapping results. Three representative factors, namely the real-time kinematic (RTK) mode, flight altitude, and the number of ground control points (GCPs) were selected to analyze their impact on UAV aerial photogrammetry accuracy. Four flight altitude tests were conducted separately in two RTK modes, and five GCP layout schemes were designed. Based on this, the root mean square error (RMSE) values of 40 aerial photogrammetric results were analyzed. The results showed a significant correlation between flight altitude and resolution of the UAV aerial photogrammetric results. Further, conversion formulas between actual image resolution and flight altitude for different GCP values were also derived in RTK and non-RTK modes. In the case of precise positioning, the horizontal and vertical accuracy of the aerial photogrammetric image decreased with increasing flight altitude. Under the same flight altitude, the addition or no addition of GCPs, including changes in GCP numbers, had no significant effect on improving the accuracy of aerial photogrammetry in RTK mode. However, in non-RTK mode, the number of GCPs significantly affected accuracy. The horizontal and vertical RMSE values decreased rapidly with the increase in GCP numbers and then stabilized. However, regardless of whether RTK was activated, an excessive number of GCPs was not conducive to improving the accuracy of aerial photogrammetric results. The mapping accuracy of UAVs in RTK mode without GCPs was equivalent to that in non-RTK mode with GCPs. Therefore, when using RTK-UAVs, deploying GCPs is unnecessary under suitable circumstances. Finally, practical suggestions for optimizing the UAV aerial photogrammetry scheme are provided as a reference for related applications.

COMPARATION OF PHOTOGRAMMETRY AND TERRESTRIAL LASER SCANNING METHODS FOR EROSION MONITORING IN THE AREA OF DEVIL’S TOWN: PROJECT “DEMONITOR”

<p>Project "Devils’ town Erosion MONITORing - DEMONITOR" involves the monitoring of accessible earth pillars in the area of Devil’s town, by using a combination of several non‐invasive methods. Terrestrial laser scanning (TLS) and photogrammetric imaging with unmanned aircraft (UAV) as a platform showed as great solutions for 3D modeling of this site and erosion monitoring. In this work it is shown that using manual free flight mode for imaging with UAV gave much better results than the missions performed with predefined flight plans. The desired long‐term effect from this research should have a significant part in the overall socioeconomic development of the municipality of Kuršumlija, and the entire Toplica district.</p>

Comparative performance analysis of precise point positioning technique in the UAV − based mapping

Uncrewed Aerial Vehicle (UAV) photogrammetry is a very efficient, low-cost approach for mapping small or medium-sized local areas. This approach is adequate for achieving the needed accuracy in many applications, and onboard positioning sensors have made this technique more feasible by decreasing the need for ground control stations (GCPs). When real-time positioning strategies are used, photogrammetric mapping can also be performed instantaneously in the field. This is especially crucial in applications like disaster management, where current situation must be detected rapidly. It is critical to choose a georeferencing approach in applications that is appropriate for the physical parameters of the mapping area, the intended use of the photogrammetric mapping, and the needed accuracy. In order to accurately georeference the photogrammetric outputs, this study assesses the real-time and post-processed performances of absolute and relative GNSS positioning approaches. Finally, it compares these strategies in terms of the position accuracy of the created models. In the evaluations, the 3D models of the study area were created utilizing network-based Real Time Kinematic (n-RTK), Post-Processed Kinematic (PPK) Positioning, and Precise Point Positioning (PPP) approaches, with direct georeferencing of the UAV-based photogrammetric images. These models were then compared to models developed using traditional indirect georeferencing with GCPs and aided indirect georeferencing. According to the findings, direct georeferencing using the n-RTK and PPK techniques provided a horizontal position accuracy in terms of root mean square errors (rms2D = 1.6 cm and 1.9 cm) almost equal to indirect georeferencing using GCPs (rms2D = 1.9 cm). In terms of vertical position accuracies, the n-RTK and PPK approaches in direct georeferencing (rmsh = 3.7 cm and 3.9 cm, respectively) produced rather different results than the GCPs-based conventional strategy (rmsh = 1.6 cm), although the discrepancies were within acceptable error limits. Direct georeferencing with the PPP approach produced the lowest horizontal and vertical position accuracies (rms2D = 5.9 cm, rmsh= 5.7 cm). However, this level of accuracy was deemed adequate for use in large-scale mapping in Turkey's authorized regulation.

Innovative K-Means based machine learning method for determination of non-uniform image coordinate system in panoramic imaging: a case study with Ladybug2 camera.

Currently, the practical implementations of panoramic cameras range from vehicle navigation to space studies due to their 360-degree imaging capability in particular. In this variety of uses, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. There are several types of omnidirectional cameras which can be classified mainly as central and non-central cameras for 360-degree imaging. The central omnidirectional cameras are those which satisfy the single-viewpoint characteristic. Multi-camera systems are usually developed for applications for which two-image stereo vision is not flexible enough to capture the environment surrounding a moving platform. Although the technology based on multi-view geometry is inexpensive, accessible, and highly customizable, multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In this study, not only a defining method of the non-uniform image coordinate system is suggested by means of the K-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in the panoramic calibration room but also the use of an elliptical panoramic projection coordinate system definition by Singular Value Decomposition (SVD) method in panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system.

Internal orientation, instrument calibration and lens distortion of photogrammetric imaging systems and aerial cameras

Internal orientation, instrument calibration and lens distortion of photogrammetric imaging systems and aerial cameras

Accuracy assessment of UAS photogrammetry and structure from motion in surveying and mapping

Rapid and accurate surveying has always attracted great interest in all scientific and industrial activities that require high-resolution topographic data. The latest automation and advancement in geomatics engineering are remote sensing solutions using Unmanned Aerial Systems (UAS) and Structure from Motion (SfM) with Multi-View Stereo (MVS) photogrammetry. This research aimed to find the influence of flight height, Ground Control Point (GCP), and software on the geometric accuracy of UAS-SfM-derived Digital Surface Models (DSMs) and orthoimages, as well as to analyze and evaluate the accuracy of UAS-SfM as a rapid and low-cost alternative to conventional survey methods. To achieve the aim of the study, aerial surveys using a fixed-wing UAS and field surveys using RTK GNSS and total station were conducted. A total of 16 photogrammetric projects were processed using different GCP configurations, and detailed statistical analysis was performed on the results. Moreover, the contribution of cross flight on bundle adjustment was investigated empirically by conducting a combined photogrammetric image processing. The analysis revealed that flight height, GCP number and distribution, and the processing software significantly affect products' quality and accuracy. Evaluation of the achieved accuracies was made based on the American Society for Photogrammetry and Remote Sensing (ASPRS) positional accuracy standard for digital geospatial data. The findings of this study revealed that using the optimal flight height and GCP configuration, 3D models, orthomosaics and DSMs can be rapidly reconstructed from 2D images with the quality and accuracy sufficient for most terrain analysis applications, including civil engineering projects.

An Assessment of the Epicenter Location and Surroundings of the 24 January 2020 Sivrice Earthquake, SE Türkiye

The Sivrice earthquake (Mw 6.8) occurred on 24 January 2020 along the East Anatolian Fault (EAF) zone of Türkiye, and epicentral information and focal mechanism solutions were published by two national and six international seismic stations. Here, we analyzed epicentral locations and the major fault trace using aerial photogrammetric images taken two days after, and synthetic aperture radar (SAR) interferometry. Although the focal mechanism solutions were similar, the epicenters were largely displaced. Several bright lineaments with a stair-like geometry were observed in aerial images of the Euphrates River channel along the fault trace. These lineaments, also called en echelon fractures in structural geology, are like right-lateral segments of a fault plane aligning the river channel, cut and offset by those similar in trend with the EAF and with alignments of a left lateral sense, as is the EAF motion sense. We interpret that the river local channel follows a right-lateral fault structure. The traces were lost a few days later, which proves the essentiality of remote sensing technologies for obtaining precise information in large regions. The time series analysis for one year period from Sentinel-1 SAR data also illustrated the displacements in the region sourced from the earthquake.

ROCK MASS DISCONTINUITY DETERMINATION WITH TRANSFER LEARNING

Abstract. Rock mass discontinuity and orientation are among the important rock mass features. They are conventionally determined with scan-line surveys by engineering geologists in field, which can be difficult or impossible depending on site accessibility. Photogrammetry and computer vision techniques can aid to automatically perform these measurements, although variations in size, shape and appearance of rock masses make the task challenging. Here we propose an automated approach for the detection of rock mass discontinuities using deep learning and photogrammetric image processing methods. Two deep convolutional neural network (DCNNs) were implemented for this purpose and applied to basalts in Kizilcahamam Guvem Geosite near Ankara, Türkiye. Red-green-blue (RGB) band images of the site were taken from an off-the-shelf camera with 1.7 mm resolution and a 3D digital surface model and orthophotos were produced by using photogrammetric software. The discontinuities were delineated manually on the orthophoto and converted to masks. The first DCNN model was based on the open-source crack dataset consisting of a total of 11,298 road and pavement images, which were used to train the Resnet-18 model (Model-1). The second model (Model-2) was based on fine-tuning of Model-1 using the study data from Kizilcahamam. After fine-tuning, Model-2 was able to achieve high performance with a Jaccard Score of 88% on the test data. The results show high potential of the methodology for transfer learning with fine-tuning of a small amount of data that can be applied to other sites and rock mass types as well.

Automated pipeline reconstruction using deep learning & instance segmentation

BIM is a powerful tool for the construction industry as well as for various other industries, so that its use has increased massively in recent years. Laser scanners are usually used for the measurement, which, in addition to the high acquisition costs, also cause problems on reflective surfaces. The use of photogrammetric techniques for BIM in industrial plants, on the other hand, is less widespread and less automated. CAD software (for point cloud evaluation) contains at best automated reconstruction algorithms for pipes. Fittings, flanges or elbows require a manual reconstruction. We present a method for automated processing of photogrammetric images for modeling pipelines in industrial plants. For this purpose we use instance segmentation and reconstruct the components of the pipeline directly based on the edges of the segmented objects in the images. Hardware costs can be kept low by using photogrammetry instead of laser scanning. Besides the autmatic extraction and reconstruction of pipes, we have also implemented this for elbows and flanges. For object recognition, we fine-tuned different instance segmentation models using our own training data, while also testing various data augmentation techniques. The average precision varies depending on the object type. The best results were achieved with Mask R–CNN. Here, the average precision was about 40%. The results of the automated reconstruction were examined with regard to the accuracy on a test object in the laboratory. The deviations from the reference geometry were in the range of a few millimeters and were comparable to manual reconstruction. In addition, further tests were carried out with images from a plant. Provided that the objects were correctly and completely recognized, a satisfactory reconstruction is possible with the help of our method.

Estimation of Aboveground Biomass Stock in Tropical Savannas Using Photogrammetric Imaging

The use of photogrammetry technology for aboveground biomass (AGB) stock estimation in tropical savannas is a challenging task and is still at a preliminary stage. This work aimed to use metrics derived from point clouds, constructed using photogrammetric imaging obtained by an RGB camera on board a remotely piloted aircraft (RPA), to generate a model for estimating AGB stock for the shrubby-woody stratum in savanna areas of Central Brazil (Cerrado). AGB stock was estimated using forest inventory data and an allometric equation. The photogrammetric digital terrain model (DTM) was validated with altimetric field data, demonstrating that the passive sensor can identify topographic variations in sites with discontinuous canopies. The inventory estimated an average AGB of 18.3 (±13.3) Mg ha−1 at the three sampled sites. The AGB model selected was composed of metrics used for height at the 10th and 95th percentile, with an adjusted R2 of 93% and a relative root mean squared error (RMSE) of 16%. AGB distribution maps were generated from the spatialization of the metrics selected for the model, optimizing the visualization and our understanding of the spatial distribution of forest AGB. The study represents a step forward in mapping biomass and carbon stocks in tropical savannas using low-cost remote sensing platforms.

Connected pixels-based image smoothing filter

Digital image processing heavily relies on the connectivity of pixels, as it is a vital component for accurate object identification and analysis within an image. Grouping together pixels with similar features such as colour and intensity, allows for the formation of meaningful patterns or objects, which is essential for object recognition and segmentation. This approach is particularly valuable in photogrammetric imaging, video surveillance, deep learning as it facilitates the isolation of regions of interest and object tracking. Image smoothing is also a crucial aspect in enhancing visual quality by reducing noise and enhancing details, especially in applications such as aerial mapping, medical imaging, video compression, image resizing and computer vision. The absence of connected pixels and image smoothing would make image processing tasks more challenging and less reliable, making them fundamental to digital image processing and critical to various applications in diverse fields. This paper introduces a novel image smoothing filter called Connected Pixels Based Image Smoothing Filter (CPF), which is based on gray connected pixels. The success of the CPF was compared to that of the Non-Local Means Filter (NLMF) in terms of Structural Similarity Index (SSIM) for the same Mean Squared Error (MSE). The experimental results showed that CPF has a better ability to preserve image details compared to NLMF.

Homomorphic filtering in digital multichannel image processing

Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value is consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.

EVOLUTION OF RECORDING METHODS: THE AACHEN CATHEDRAL WORLD HERITAGE SITE DOCUMENTATION PROJECT

Abstract. Modern terrestrial laser scanners and photogrammetric imaging systems can provide highly accurate and objective as-built records of existing architectural, engineering, and industrial sites. This comprehensive digital recording benefits culturally significant places like heritage buildings, monuments, and other vital structures. The collected data can be instrumental in various ways, including aiding in conservation, management, monitoring and repair efforts and serving as an educational resource for scholars and the general public. These technical capabilities are especially well-suited for architecturally complex, ornate buildings like the Aachen Cathedral UNESCO World Heritage site. This paper describes the recent recording efforts at the Aachen Cathedral and is a comparative study of the previous documentation work done at the Cologne Cathedral.The 3D documentation of the Aachen Cathedral UNESCO World Heritage Site is an ongoing collaborative project between the Sapienza Università di Roma, Rome, Italy, Robert Gordon University, Aberdeen, Scotland, and in partnership with RWTH Aachen University, and the Dombauhütte Aachen.

A SEMI-AUTOMATED METHODOLOGY FOR 3D IMAGING OF LARGE VAULTED CEILING PAINTINGS

Abstract. 3D imaging of large painted and vaulted historic ceilings offers many challenges, including those due to complex small and large-scale geometry, environmental conditions and limited access. In this paper we introduce CHAPI (Cultural Heritage Automated Photogrammetric Imaging), a low cost solution to these problems which provides an efficient, semi-automated method of capturing large vaulted ceiling paintings in high detail, with a consistent photogrammetric network and in limited time. We will present and examine two different case studies from the Plafond3D project, the baroque ceiling paintings of the Schloss Rheinsberg Spiegelsaal (mirror room) and the Ansbach Residence Festsaal (great hall/ballroom). The paper will examine the particular challenges of capturing large painted areas with accurate colour and geometric reproduction, and suggest how the photogrammetric recording and reconstruction process of ceilings can be optimised for large rooms. This paper also gives technical specifications and Open Access Code for the CHAPI build.