Structure From Motion Algorithm Research Articles

Using structure-from-motion to estimate cover crop biomass and characterize canopy structure

ContextVariability in biomass production poses a challenge for growers when using cover crops for weed control. However, most methods for assessing cover crop biomass are laborious and impractical on a field scale. ObjectiveThe goal of the present study was to determine the feasibility of using Structure-from-Motion (SfM) photogrammetry to estimate biomass in cereal rye (Secale cereale L.) and winter wheat (Triticum aestivum L.) cover crops by correlating biomass with 3-D point cloud pixel density and crop height. MethodsPoint clouds were generated using a SfM algorithm from RGB (red, green, and blue) videos collected by a hand-held GoPro camera over sixteen crop fields in North Carolina, Iowa, and Maryland, USA, throughout two growing seasons (2021–2023). Crop height, leaf area index (LAI), and photosynthetically active radiation (PAR) were also measured. ResultsBiomass was positively correlated with crop height for both cereal rye (R2 = 0.621) and wheat (R2 = 0.55). LAI was positively correlated with biomass accumulation and crop height for both species, increasing linearly in rye and exponentially in wheat. Conversely, PAR penetration below the canopy decreased with biomass accumulation and crop height in both species, with a more rapid extinction in wheat than rye. Point cloud pixel density showed a positive linear relationship with biomass in rye but saturated after 2.5 tonnes ha−1 (2500 kg ha−1). In wheat, point cloud pixel density was weakly and negatively correlated with biomass due to a denser canopy causing faster saturation of tissue detection by SfM point clouds. However, considering crop height and point cloud density integrating them both in the model allowed obtaining a positive relationship with biomass through levels of 8 tonnes ha−1 (8000 kg ha−1) in both species. When models were validated with independent data, predicted and measured biomass were positively correlated for both rye (R2 = 0.86) and wheat (R2 = 0.78). ConclusionsBased on the results, using SfM to generate 3-D point clouds can provide a more accurate estimation of biomass than canopy height alone by capturing species-level differences in canopy architecture. ImplicationsThe results of this study suggest that SfM can potentially be used as a non-destructive tool for growers to monitor biomass production in cereal cover crops other systems such as energy/forage crops, which can help inform management decisions and conserve resources.

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN.

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

Fast 3D Mapping Solution with UAV

Abstract The photogrammetric method of 3D mapping uses Structure from Motion (SfM) algorithm, where 3D structures of a scene are created from 2D sets of data taken from multiple angles. This 3D mapping process is used in many advanced and important applications in the digital and robotics industry. The use of omnidirectional camera along with UAV for this photogrammetric process allows faster data acquisition and data processing to construct 3D structures compared to traditional method. However, the metric accuracy of spherical photogrammetry is relatively less compared to traditional framed camera photogrammetry. Hence, this research will focus on improving metric accuracy of spherical photogrammetry by enhancing SfM algorithm for the purpose of providing an enhanced and value-added fast 3D mapping solution using UAV. The research was conducted at 4 main levels. 1) Data acquisition phase, where the 360-degree camera with GPS tracker are attached to a UAV, and it is used to collect different sets of images at different heights and angles. 2) Camera calibration, for the purpose of identifying the camera intrinsic and extrinsic. These 2 parameters are crucial in feature detection and camera position estimation. 3) Data processing phase, where the collected data would be processed through the enhanced SfM algorithm to create the 3D structures. 4) Data analysis phase, where the 3D model created from enhanced SfM algorithm would be compared against a reference 3D model created by commercial software. However, the results attained were not as expected due to undesired feature detection process. This research is expected to help geographical and land surveyors to conduct surveys at a faster, more efficient and at a higher accuracy. A practical use case of this solution is to survey the terrain of an area under natural disaster, accurately and faster for search and rescue missions.

Physical Structure Expression for Dense Point Clouds of Magnetic Levitation Image Data.

The research and development of an intelligent magnetic levitation transportation system has become an important research branch of the current intelligent transportation system (ITS), which can provide technical support for state-of-the-art fields such as intelligent magnetic levitation digital twin. First, we applied unmanned aerial vehicle oblique photography technology to acquire the magnetic levitation track image data and preprocessed them. Then, we extracted the image features and matched them based on the incremental structure from motion (SFM) algorithm, recovered the camera pose parameters of the image data and the 3D scene structure information of key points, and optimized the bundle adjustment to output 3D magnetic levitation sparse point clouds. Then, we applied multiview stereo (MVS) vision technology to estimate the depth map and normal map information. Finally, we extracted the output of the dense point clouds that can precisely express the physical structure of the magnetic levitation track, such as turnout, turning, linear structures, etc. By comparing the dense point clouds model with the traditional building information model, experiments verified that the magnetic levitation image 3D reconstruction system based on the incremental SFM and MVS algorithm has strong robustness and accuracy and can express a variety of physical structures of magnetic levitation track with high accuracy.

Two-View Structure-from-Motion with Multiple Feature Detector Operators

This paper presents a novel two-view Structure-from-Motion (SfM) algorithm with the application of multiple Feature Detector Operators (FDO). The key of this study is the implementation of multiple FDOs into a two-view SfM algorithm. The two-view SfM algorithm workflow can be divided into three general steps: feature detection and matching, pose estimation and point cloud (PCL) generation. The experimental results, the quantitative analyses and a comparison with existing algorithms demonstrate that the implementation of multiple FDOs can effectively improve the performance of a two-view SfM algorithm. Firstly, in the Oxford test dataset, the RMSE reaches on average 0.11 m (UBC), 0.36 m (bikes), 0.52 m (trees) and 0.37 m (Leuven). This proves that illumination changes, blurring and JPEG compression can be handled satisfactorily. Secondly, in the EPFL dataset, the number of features lost in the processes is 21% with a total PCL of 27,673 pt, and this is only minimally higher than ORB (20.91%) with a PCL of 10,266 pt. Finally, the verification process with a real-world unmanned aerial vehicle (UAV) shows that the point cloud is denser around the edges, the corners and the target, and the process speed is much faster than existing algorithms. Overall, the framework proposed in this study has been proven a viable alternative to a classical procedure, in terms of performance, efficiency and simplicity.

Ispitivanje uticaja broja orijentacionih tačaka na geometrijsku tačnost UAV fotogrametrijskih proizvoda formiranih pomoću pristupa strukture iz pokreta

The positional and vertical accuracy of UAV aerial photogrammetry products generated using the Structure from Motion (SfM) approach depends on various factors, such as flight plan parameters, camera quality, camera calibration, the SfM algorithm used, and the georeferencing process. The influence of the quantity of Ground Control Points (GCPs) on the geometric quality of generated models and the stability of camera calibration parameters assessed through self-calibration in the block-aerotriangulation process was investigated in this study. Three software systems were used to process the collected UAV photogrammetry images: Pix4D Mapper, Agisoft Metashape, and Trimble Inpho UASMaster. Standard statistical quality assessments were employed to assess the accuracy of the block-aerotriangulation. The research findings indicate that augmenting the quantity of GCPs enhances model reliability and decreases the RMSE values of vertical deviation on the control points. The RMSE values of vertical deviation on the check points for all three used software systems converged to approximately twice the value of the average spatial resolution. Additionally, the RMSE values of positional deviation on check points converged to the value of the average spatial resolution.

Differential SfM and image correction for a rolling shutter stereo rig

• A simple and flexible generalized RS stereo differential SfM algorithm over two consecutive frames. • Two effective and effcient RS-stereo non-linear optimization techniques based on the maximum likelihood criterion. • An RS-stereo image correction method to remove the inaccuracies induced by the RS effect. • The proposed model and method are universal and tractable. Most modern consumer-grade cameras are equipped with an electronic rolling shutter (RS), leading to image distortions when the camera moves during image acquisition. We explore the first structure and motion estimation problem of a dynamic generalized RS stereo camera. Such a general configuration is commonplace in robots and autonomous driving applications. We propose a tractable RS stereo differential structure from motion (SfM) algorithm, taking into account the RS effect during consecutive imaging, which effectively compensates for the RS-stereo image distortion by a linear scaling operation on each optical flow. We further propose embedding the cheirality into RANSAC and develop a robust RS-stereo-aware full-motion estimation framework. We demonstrate that the RS stereo motion and depth map refined by our non-linear optimization schemes within the maximum likelihood criterion can be used for image correction to recover high-quality global shutter (GS) stereo images. Moreover, using the proposed generalized RS stereo differential SfM pipeline, the corrected images produce an accurate 3D scene structure as the ground-truth structure. Extensive experiments on both synthetic and real RS stereo data demonstrate the effectiveness of our model and method in various configurations.

SEX DISCRIMINATION OF INDIVIDUAL TREES USING UAV IMAGERY

Abstract. The sex ratio is the proportion of male to female trees, which has a substantial impact on reproductive success and conservation status. Appropriate sex-related differences in dioecious trees commonly result in leading to a robustly structured population. Fieldwork for sex discrimination is time-consuming and labor-required. Benefiting from the unmanned aerial vehicle (UAV) and SfM techniques, the present study aims to detect male and female Caspian poplar (Populus caspica) trees. In March 2021, a heterogeneous forest in Noor city located in Mazandaran province was photographed, then 3D point clouds were extracted from the images using structure from motion algorithm (SfM) to generate an orthomosaic and a point cloud. The field survey was carried out to record the species, sex, and position of the overstory trees which were identifiable on the orthomosaics. A random forest classification algorithm was applied using R software to classify the trees into male and female. By assessing the producer's accuracy, user's accuracy, and overall accuracy, the classification accuracy for identified trees was computed using 10-fold cross-validation. The results showed an accuracy of 83% for identifying Caspian poplar trees and 52% accuracy for Sex discrimination. Overall, our effort to evaluate sex discrimination of dioecious trees using UAV imagery represents a promising preliminary step in forest data collection.

인공지능 기반 전시 활주로의 불발탄 및 폭파구 정량화에 관한 연구

At the beginning of the war, the runway of a military airfield becomes the top target for enemy missile strikes, and crater of various sizes are formed on the attacked runway, while many unexploded ordnance bombs remain. According to the existing airfield damage analysis technology to solve this problem, manual survey of unexploded ordnance and crater by reconnaissance team relying on visual observation and measuring tape measure, transmission of information about the size and location of crater by radio to the facility control room, and detonation by the facility control room Classification of old buildings, manual selection of MOS, and runway repair (damage restoration) process by the damage recovery team are sequential is done. However, the existing technology takes a long time to work due to the manual surveying of unexploded ordnance munitions and crater, and there is a high possibility that the reference point will be lost. There is a problem, and there is a possibility of secondary work by the information morning moon in the runway repair process. A fixed FOD(Foreign Object Debris) detection system can be applied to solve this visual inspection problem, but there is a problem in that the installation of a fixed tower itself is not preferred around the runway due to flight safety issues. This study is proposed to solve the above problems, and based on drone footage, artificial intelligence algorithm, and SfM(Structure from Motion) algorithm for point cloud generation, it is possible to analyze the damage at the emergency airfield in wartime and to develop an automated system for repairing damage to the flight pavement. Its purpose is to develop.

Generating UAV high-resolution topographic data within a FOSS photogrammetric workflow using high-performance computing clusters

Photogrammetry is one of the most reliable techniques to generate high-resolution topographic data and it is key to territorial mapping and change detection analysis of landforms in hydro-geomorphological high-risk areas. Specifically, the Structure from Motion (SfM) is an emerging topographic survey technique that addresses the problem of determining the 3D position of image descriptors to estimate three-dimensional structures. Thanks to the potential of SfM algorithm and the development of Unmanned Aerial Vehicles (UAVs) that allow the on-demand acquisition of high-resolution aerial images, it is possible to survey extended areas of the Earth surface and monitor active phenomena through multi-temporal surveys. However, the ability to detect remote and wide areas with a very high-resolution is countered by the need to capture large datasets which can limit the photogrammetric process, due to the need for high-performance hardware. This paper presents a photogrammetric workflow based on Free and Open-Source Software (FOSS), which is able to return different outputs and to manage a large amount of data in reasonable time, through the distribution of the most computationally expensive steps on computing clusters hosted by the ReCaS-Bari data center for scientific research. The results are given in terms of performance evaluations based on different computing configurations of the clusters and setups of the steps of the workflow. The HTC cluster test with a parallel SSH approach involved an important reduction of several hours in the processing time of thousands UAV images, especially compared to classic photogrammetric process on a single workstation with commercial software.A parallel test, aimed to validate the performance of a single sever of the new HPC cluster, involved really good results halving the processing time with respect to the HTC cluster test.

Performance Evaluation of Parallel Structure from Motion (SfM) Processing with Public Cloud Computing and an On-Premise Cluster System for UAS Images in Agriculture

Thanks to sensor developments, unmanned aircraft system (UAS) are the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications in these days. UAS imagery datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS and sensors. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm to generate 3D point clouds, orthomosaic images, and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel computing to process big UAS data faster for certain applications. This study evaluated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for performance evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the most powerful machine in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in speed-up and efficiency metrics for scalability, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system could be a good alternative computing environment in UAS data processing for agricultural applications.

Robust hierarchical structure from motion for large-scale unstructured image sets

Structure from Motion (SfM) is key to mixed computer vision and photogrammetry applications. However, the fast-growing needs for large-scale SfM bring challenges to current SfM solutions. Unlike traditional global and incremental SfM solutions, hierarchical SfM approaches demonstrate promising potential in effectively reconstructing large-scale image sets by dividing the image set into multiple image clusters, reconstructing each cluster separately, and gradually merging partial models into a complete model. However, current hierarchical SfM approaches still suffer from the following problems: accurate image clustering without ancillary information; automatic quality evaluation of each reconstruction unit and unreliable partial reconstruction removal; effective and efficient reconstruction of each image cluster; robust and accurate cluster merging considering the merging order and the handling of images taken with the same camera but divided into different clusters. These unstable factors limit the robustness and accuracy of hierarchical SfM approaches on different unstructured image sets.To systematically improve the performance of hierarchical SfM, we propose a novel robust hierarchical structure from motion (RHSfM) method for large-scale image sets, which does not rely on any additional information, such as Global Positioning System (GPS) and Inertial Navigation System (INS). (1) We develop an automatic image clustering method based on image correlation and present a dynamic adjustment strategy, obtaining reliable image clustering results. (2) We remove the poor reconstructions by introducing multiple quality evaluation standards. (3) We put forward a fast incremental SfM algorithm that optimizes the image adding mode with an image pre-screening strategy and gets rid of the dependence by the proposed dynamic adjustment strategy. (4) We achieve accurate cluster merging by creating an optimal merging list and employing a stepwise global optimization strategy that merges structures first and then cameras. Significantly, the entire process is fully automated with only a few input parameters, and the final result is not sensitive to these parameters.We verify our method on various real image sets that cover different image conditions, different scenes, and different image scales, especially two large-scale image sets with 121,506 and 153,396 images, respectively. The experimental results reveal that our approach outperforms the state-of-the-art SfM systems Colmap, 3DF Samantha, and Metashape in terms of robustness, accuracy, and efficiency. In particular, only our method successfully reconstructed all the seven challenging datasets. For the five datasets that the other systems can also reconstruct, our method obtains the highest accuracy, which is 25 percent better than the best result of the comparable methods on average; for the remaining two datasets, the accuracy of our method is higher than 0.75 pixels. Moreover, the efficiency of our method is about 18, 4.85, and 0.25 times faster than Colmap, 3DF Samantha, and Metashape averagely on the experimental image sets, respectively. After all, our contribution provides a comprehensive and practical solution for large-scale SfM.

3D Foot Reconstruction Based on Mobile Phone Photographing

Foot measurement is necessary for personalized customization. Nowadays, people usually obtain their foot size by using a ruler or foot scanner. However, there are some disadvantages to this, namely, large measurement error and variance when using rulers, and high price and poor convenience when using a foot scanner. To tackle these problems, we obtain foot parameters by 3D foot reconstruction based on mobile phone photography. Firstly, foot images are taken by a mobile phone. Secondly, the SFM (Structure-from-Motion) algorithm is used to acquire the corresponding parameters and then to calculate the camera position to construct the sparse model. Thirdly, the PMVS (Patch-based Multi View System) is adopted to build a dense model. Finally, the Meshlab is used to process and measure the foot model. The result shows that the experimental error of the 3D foot reconstruction method is around 1 mm, which is tolerable for applications such as shoe tree customization. The experiment proves that the method can construct the 3D foot model efficiently and easily. This technology has broad application prospects in the fields of shoe size recommendation, high-end customized shoes and medical correction.

THERMAL AND OPTICAL DATA FUSION SUPPORTING BUILT HERITAGE ANALYSES

Abstract. The recent developments of passive sensors techniques, that have been able to take advantage of the technological innovations related to sensors technical features, sensor calibration, the use of UAV systems (Unmanned Aerial Vehicle), the integration of image matching techniques and SfM (Structure from Motion) algorithms, enable to exploit both thermal and optical data in multi-disciplinary projects. This synergy boost the application of Infrared Thermography (IRT) to new application domains, since the capability to provide thematic information of the analysed objects benefits from the typical advantages of data georeferencing and metric accuracy, being able to compare results investigating different phenomena.This paper presents a research activity in terrestrial and aerial (UAV) applications, aimed at generating photogrammetric products with certified and controlled geometric and thematic accuracy even when the acquisitions of thermal data were not initially designed for the photogrammetric process. The basic principle investigated and pursued is the processing of a photogrammetric block of images, including thermal IR and optical imagery, using the same reference system, which allows the use of co-registration algorithms. Such approach enabled the generation of radiance maps, orthoimagery and 3D models embedding the thermal information of the investigated surfaces, also known as texture mapping; these geospatial dataset are particularly useful in the context of the built Heritage documentation, characterised by complex analyses challenges that a perfect fit for investigations based on interdisciplinary approaches.

Relations between the Number of GCPs and Accuracy of UAV Photogrammetry in the Foreshore of the Sandy Beach

Yang, H.; Li, H.; Gong, Z.; Dai, W., and Lu, S., 2020. Relations between the number of GCPs and accuracy of UAV photogrammetry in the foreshore of the sandy beach. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1372–1376. Coconut Creek (Florida), ISSN 0749-0208.The combination of UAV (Unmanned Aerial Vehicles) and SFM (Structure from Motion) algorithm is an advanced method. This method makes the observations of sandy beaches more efficient and accurate. The DEM (Digital Elevation Model) from this method can encode elevation data and create highly detailed digital terrain models with a root mean square error (RMSE) of only 10 cm. But needing too many GCPs (Ground Control Points) to guarantee the accuracy of DEM is a main drawback of this method. This paper is aimed to solve this problem and assess how the number of GCPs will impact the accuracy of UAV photogrammetry to find the optimum number of GCPs.

A Concept for Three-Dimensional Particle Metrology Based on Scanning Electron Microscopy and Structure-from-Motion Photogrammetry.

Scanning electron microscopy (SEM) has been frequently used for size and shape measurements of particles. SEM images offer two-dimensional (2D) information about a particle’s lateral dimensions. Unfortunately, information about the particle’s three-dimensional (3D) size and shape remains unavailable. To resolve this issue, I propose a new concept in SEM: 3D particle metrology obtained by applying structure-from-motion (SfM) algorithms to multiple rotational SEM images of particles deposited onto a cylindrical substrate to generate a 3D model from which size and shape information can be extracted. Particles can have any size that is suitable for SEM imaging. SEM images of the sample can be acquired from 0° to 360° using a rotational-tip SEM substage. Here, I will discuss the concept and, for clarity, illustrate it with aquarium gravel particles that are glued onto a craft roll and imaged optically before generating the 3D model of that handmade craft. Future work will include the experimental SEM realization, as well as further development of the SfM algorithms. In my view, this proposed concept may become an integral part of SEM-based particle metrology.

Underwater Photogrammetry Application for Coral Reef Mapping and Monitoring

Underwater 3D reconstruction techniques such as underwater photogrammetry are the latest trend in low cost survey techniques. This article explains how the data acquisition process is up to the process of producing an underwater photo map. Automatic data processing for coral reef objects from the initial data acquisition stage to building 3D models from seabed objects to rendering in virtual reality. In this paper, underwater photogrammetry was applied with SfM (Structure from Motion) algorithm in performing 3D reconstruction of coral reefs that will be produce orthophoto map. Geodetic measurements also performed to the model for control point. The analysis will be divided into t-test and F-test. From the processing, coral reef modeling has obtained the result of a linear error which t-test produce <50% data, both at 95% and at 99 % confidence level. The F-test gain a positive value where the point value used in the linear error test on marker L and K, the value at α = 0.05. The 3D coral reef model in the resulting orthophoto still has weaknesses, for example the color appearance that depends on the level of clarity of the water as well as the camera filter used. However, in 2D display is sufficient and can be an alternative to other methods, for example, the LIT method.

Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images

Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3-D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to an unmanned aerial vehicle (UAV). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and RMSE values of 89% and 3.41, respectively, while those for LAI had R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February, and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and center-west regions presenting LAI values > 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.

Vertical light transmission profiles in structured mixed deciduous forest canopies assessed by UAV-based hemispherical photography and photogrammetric vegetation height models

Light availability below and within forest canopies governs many biological processes. Its estimation, however, is often time consuming, especially in complex canopy structures where vertical profiles of light transmission need to be known to estimate plant performance, habitat suitability or biophysical properties across the canopy. To overcome the technical limitations of assessing vertical canopy space by ground-based systems like ropes, towers or cranes, we developed a prototype to take hemispherical photographs (HP) from an unmanned aerial vehicle (UAV). With the new airborne system, we aimed to assess the differences of vertical light availability profiles from bottom to the top of the canopy in even-aged, uneven-aged and coppice-with-standards forests. Measured vertical light profiles typically followed a sigmoid shape that differed markedly depending on canopy gap size and forest structure. The results showed important variations in the transmission of sunlight at different heights within forest canopies, highlighting the importance of vertical canopy stratification when considering radiation transfer, plant area index (PAI), leaf area index (LAI) and light availability. To further cover cases where branches hinder UAV access to dense canopy spaces, we present a workflow to estimate light availability from photogrammetrically reconstructed high-resolution vegetation height models (VHM) and validate the outputs against HP. We conclude that VHMs based on high-resolution aerial photography, photogrammetry and a structure-from-motion algorithm (SfM) deliver a solid foundation to derive a wide range of canopy metrics. With an appropriate VHM point density, direct or beam light index (BLI), diffuse light index (DLI), gap or global light index (GLI) and PAI in mixed broadleaved forests can be reliably calculated. The novel development of UAV-based HP and the workflow to calculate light regimes from VHMs thus open a wide range of new applications in forest and agricultural research and management.

SEMANTIC ROAD SCENE KNOWLEDGE FOR ROBUST SELF-CALIBRATION OF ENVIRONMENT-OBSERVING VEHICLE CAMERAS

Abstract. Environment-observing vehicle camera self-calibration using a structure from motion (SfM) algorithm allows calibration over vehicle lifetime without the need of special calibration objects being present in the calibration images. Scene-specific problems with feature-based correspondence search and reconstruction during the SfM pipeline might be caused by critical objects like moving objects, poor-texture objects or reflecting objects and might have negative influence on camera calibration. In this contribution, a method to use semantic road scene knowledge by means of semantic masks for a semantic-guided SfM algorithm is proposed to make the calibration more robust. Semantic masks are used to exclude image parts showing critical objects from feature extraction, whereby semantic knowledge is obtained by semantic segmentation of the road scene images. The proposed method is tested with an image sequence recorded in a suburban road scene. It has been shown that semantic guidance leads to smaller deviations of the estimated interior orientation and distortion parameters from reference values obtained by test field calibration compared to a standard SfM algorithm.