bluuebunny/crossref_metadata_2025 huggingface dataset:
SIMULTANEOUS DETECTION AND TRACKING OF PEDESTRIAN FROM PANORAMIC LASER SCANNING DATA
Authors: Xiao Wen , Vallet Bruno , Schindler Konrad , Paparoditis Nicolas
Published: June 2016

Abstract. Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, Nearest-point and Max-distance. Then, all the points on moving objects are transferred into a space-time (x, y, t) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.
ITERATIVE RE-WEIGHTED INSTANCE TRANSFER FOR DOMAIN ADAPTATION
Authors: Paul A. , Rottensteiner F. , Heipke C.
Published: June 2016

Abstract. Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) where the features may have different distributions. In this paper, we propose a new technique for domain adaptation based on logistic regression. Starting with a classifier trained on training data from the source domain, we iteratively include target domain samples for which class labels have been obtained from the current state of the classifier, while at the same time removing source domain samples. In each iteration the classifier is re-trained, so that the decision boundaries are slowly transferred to the distribution of the target features. To make the transfer procedure more robust we introduce weights as a function of distance from the decision boundary and a new way of regularisation. Our methodology is evaluated using a benchmark data set consisting of aerial images and digital surface models. The experimental results show that in the majority of cases our domain adaptation approach can lead to an improvement of the classification accuracy without additional training data, but also indicate remaining problems if the difference in the feature distributions becomes too large.
POTENTIAL OF MULTI-TEMPORAL OBLIQUE AIRBORNE IMAGERY FOR STRUCTURAL DAMAGE ASSESSMENT
Authors: Vetrivel A. , Duarte D. , Nex F. , Gerke M. , Kerle N. , Vosselman G.
Published: June 2016

Abstract. Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed damage assessment focusing on two methodologies: the first method aims at detecting severe structural damages related to geometrical deformation by combining the complementary information provided by photogrammetric point clouds and oblique images. The developed method detected 87% of damaged elements. The failed detections are due to varying noise levels within the point cloud which hindered the recognition of some structural elements. We observed, in general that the façade regions are very noisy in point clouds. To address this, we propose our second method which aims to detect damages to building façades using the oriented oblique images. The results show that the proposed methodology can effectively differentiate among the three proposed categories: collapsed/highly damaged, lower levels of damage and undamaged buildings, using a computationally light-weight approach. We describe the implementations of the above mentioned methods in detail and present the promising results achieved using multi-temporal oblique imagery over the city of L’Aquila (Italy).
JOINT SIMULTANEOUS RECONSTRUCTION OF REGULARIZED BUILDING SUPERSTRUCTURES FROM LOW-DENSITY LIDAR DATA USING ICP
Authors: Wichmann Andreas , Kada Martin
Published: June 2016

Abstract. There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.
SUPERPIXEL CUT FOR FIGURE-GROUND IMAGE SEGMENTATION
Authors: Ying Yang Michael , Rosenhahn Bodo
Published: June 2016

Abstract. Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.
AN IHS-BASED ENHANCEMENT METHOD WITH IMPROVED SCALE/SHIFT PARAMETER OF LINEAR MODEL
Authors: Bingxian Zhang
Published: June 2016

Abstract. An IHS-based enhancement method for colour images (IEM) is presented in this paper. An improved gamma transformation was applied to enhance the brightness and texture details of colour images taken in dark conditions. Moreover, a spatial filter was used to prevent the loss of details of highlighted areas. The method was tested with the images from the database of Simon Fraser University (SFU) and from the Chinese ZIYUAN-02C satellite. The experimental results show our proposed method could effectively enhance the luminance and texture of the images, especially for those taken in dim conditions, and maintain the hue and saturation of original images.
AUTOMATIC MOSAICKING OF SATELLITE IMAGERY CONSIDERING THE CLOUDS
Authors: Kang Yifei , Pan Li , Chen Qi , Zhang Tong , Zhang Shasha , Liu Zhang
Published: June 2016

Abstract. With the rapid development of high resolution remote sensing for earth observation technology, satellite imagery is widely used in the fields of resource investigation, environment protection, and agricultural research. Image mosaicking is an important part of satellite imagery production. However, the existence of clouds leads to lots of disadvantages for automatic image mosaicking, mainly in two aspects: 1) Image blurring may be caused during the process of image dodging, 2) Cloudy areas may be passed through by automatically generated seamlines. To address these problems, an automatic mosaicking method is proposed for cloudy satellite imagery in this paper. Firstly, modified Otsu thresholding and morphological processing are employed to extract cloudy areas and obtain the percentage of cloud cover. Then, cloud detection results are used to optimize the process of dodging and mosaicking. Thus, the mosaic image can be combined with more clear-sky areas instead of cloudy areas. Besides, clear-sky areas will be clear and distortionless. The Chinese GF-1 wide-field-of-view orthoimages are employed as experimental data. The performance of the proposed approach is evaluated in four aspects: the effect of cloud detection, the sharpness of clear-sky areas, the rationality of seamlines and efficiency. The evaluation results demonstrated that the mosaic image obtained by our method has fewer clouds, better internal color consistency and better visual clarity compared with that obtained by traditional method. The time consumed by the proposed method for 17 scenes of GF-1 orthoimages is within 4 hours on a desktop computer. The efficiency can meet the general production requirements for massive satellite imagery.
GEMMA: A GENERIC, EXTENSIBLE AND MODULAR MULTI-SENSOR NAVIGATION ANALYSIS SYSTEM
Authors: Navarro J. A. , Parés M. E. , Colomina I.
Published: June 2016

Abstract. This paper presents the concept of an architecture for a system that helps researchers in the field of Geomatics to speed up their daily research on kinematic geodesy, navigation and positioning fields. The presented ideas correspond to an extensible and modular software system aimed at the development of new navigation and positioning algorithms as well as at the evaluation of the performance of sensors. The concept, already implemented in the CTTC’s system GEMMA is generic and extensible. This means that it is possible to incorporate new navigation algorithms or sensors at no maintenance cost. Only the effort related to the development tasks required to either create such algorithms or model sensors needs to be taken into account. As a consequence, change poses a much smaller problem for CTTC’s research activities is this specific area. This system includes several standalone tools that may be combined in different ways to accomplish various goals; that is, it may be used to perform a variety of tasks, as, for instance, (1) define positioning and navigation scenarios, (2) simulate different kinds of sensors, (3) validate new navigation algorithms or (4) evaluate the quality of an estimated navigation solution.
APPLICABILITY OF NEW APPROACHES OF SENSOR ORIENTATION TO MICRO AERIAL VEHICLES
Authors: Rehak M. , Skaloud J.
Published: June 2016

Abstract. This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. There, accurate aerial control plays a major role in successful terrain reconstruction and artifact-free ortophoto generation. The presented experiments focus on new approaches of aerial control. We confirm practically that the relative aerial position and attitude control can improve accuracy in difficult mapping scenarios. Indeed, the relative orientation method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified, e.g. the angular misalignment, so called boresight, between the camera and the inertial measurement unit (IMU) does not have to be determined and, second, the effect of possible systematic errors in satellite positioning (e.g. due to multipath and/or incorrect recovery of differential carrier-phase ambiguities) is mitigated. First, we present a typical mapping project over an agricultural field and second, we perform a corridor road mapping. We evaluate the proposed methods in scenarios with and without automated image observations. We investigate a recently proposed concept where adjustment is performed using image observations limited to ground control and check points, so called fast aerial triangulation (Fast AT). In this context we show that accurate aerial control (absolute or relative) together with a few image observations can deliver accurate results comparable to classical aerial triangulation with thousands of image measurements. This procedure in turns reduces the demands on processing time and the requirements on the existence of surface texture. Finally, we compare the above mentioned procedures with direct sensor orientation (DiSO) to show its potential for rapid mapping.
3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS
Authors: Anders K. , Hämmerle M. , Miernik G. , Drews T. , Escalona A. , Townsend C. , Höfle B.
Published: June 2016

Abstract. Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estima-tion. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Fur-thermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.
3D CENTRAL LINE EXTRACTION OF FOSSIL OYSTER SHELLS
Authors: Djuricic A. , Puttonen E. , Harzhauser M. , Mandic O. , Székely B. , Pfeifer N.
Published: June 2016

Abstract. Photogrammetry provides a powerful tool to digitally document protected, inaccessible, and rare fossils. This saves manpower in relation to current documentation practice and makes the fragile specimens more available for paleontological analysis and public education. In this study, high resolution orthophoto (0.5 mm) and digital surface models (1 mm) are used to define fossil boundaries that are then used as an input to automatically extract fossil length information via central lines. In general, central lines are widely used in geosciences as they ease observation, monitoring and evaluation of object dimensions. Here, the 3D central lines are used in a novel paleontological context to study fossilized oyster shells with photogrammetric and LiDAR-obtained 3D point cloud data. 3D central lines of 1121 Crassostrea gryphoides oysters of various shapes and sizes were computed in the study. Central line calculation included: i) Delaunay triangulation between the fossil shell boundary points and formation of the Voronoi diagram; ii) extraction of Voronoi vertices and construction of a connected graph tree from them; iii) reduction of the graph to the longest possible central line via Dijkstra’s algorithm; iv) extension of longest central line to the shell boundary and smoothing by an adjustment of cubic spline curve; and v) integration of the central line into the corresponding 3D point cloud. The resulting longest path estimate for the 3D central line is a size parameter that can be applied in oyster shell age determination both in paleontological and biological applications. Our investigation evaluates ability and performance of the central line method to measure shell sizes accurately by comparing automatically extracted central lines with manually collected reference data used in paleontological analysis. Our results show that the automatically obtained central line length overestimated the manually collected reference by 1.5% in the test set, which is deemed sufficient for the selected paleontological application, namely shell age determination.
MAPPING ERODED AREAS ON MOUNTAIN GRASSLAND WITH TERRESTRIAL PHOTOGRAMMETRY AND OBJECT-BASED IMAGE ANALYSIS
Authors: Mayr Andreas , Rutzinger Martin , Bremer Magnus , Geitner Clemens
Published: June 2016

Abstract. In the Alps as well as in other mountain regions steep grassland is frequently affected by shallow erosion. Often small landslides or snow movements displace the vegetation together with soil and/or unconsolidated material. This results in bare earth surface patches within the grass covered slope. Close-range and remote sensing techniques are promising for both mapping and monitoring these eroded areas. This is essential for a better geomorphological process understanding, to assess past and recent developments, and to plan mitigation measures. Recent developments in image matching techniques make it feasible to produce high resolution orthophotos and digital elevation models from terrestrial oblique images. In this paper we propose to delineate the boundary of eroded areas for selected scenes of a study area, using close-range photogrammetric data. Striving for an efficient, objective and reproducible workflow for this task, we developed an approach for automated classification of the scenes into the classes grass and eroded. We propose an object-based image analysis (OBIA) workflow which consists of image segmentation and automated threshold selection for classification using the Excess Green Vegetation Index (ExG). The automated workflow is tested with ten different scenes. Compared to a manual classification, grass and eroded areas are classified with an overall accuracy between 90.7% and 95.5%, depending on the scene. The methods proved to be insensitive to differences in illumination of the scenes and greenness of the grass. The proposed workflow reduces user interaction and is transferable to other study areas. We conclude that close-range photogrammetry is a valuable low-cost tool for mapping this type of eroded areas in the field with a high level of detail and quality. In future, the output will be used as ground truth for an area-wide mapping of eroded areas in coarser resolution aerial orthophotos acquired at the same time.
AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS
Authors: Protopapadakis E. , Stentoumis C. , Doulamis N. , Doulamis A. , Loupos K. , Makantasis K. , Kopsiaftis G. , Amditis A.
Published: June 2016

Abstract. In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure.
ECCENTRICITY ON AN IMAGE CAUSED BY PROJECTION OF A CIRCLE AND A SPHERE
Authors: Matsuoka R. , Maruyama S.
Published: June 2016

Abstract. Circular targets on a plane are often utilized in photogrammetry, particularly in close range photogrammetry, while spherical targets are sometimes utilized in industrial applications. Both a circle and a sphere are projected as an ellipse onto an image. There is an eccentricity on an image between the centre of the projected ellipse and the projected location of the centre of a circle or a sphere. Since only the centre of the projected ellipse is measured, the correction of the eccentricity is considered to be necessary for highly accurate measurement. This paper shows a process to derive general formulae to calculate an eccentricity of a circle and a sphere using the size and the location of a circle or a sphere, and the focal length, the position and the attitude of a camera. Furthermore the paper shows methods to estimate the eccentricity of a circle and a sphere from the equation of the projected ellipse of a circle or a sphere on an image.
DEFORMATION MONITORING OF MATERIALS UNDER STRESS IN LABORATORY EXPERIMENTS
Authors: Skarlatos D. , Yiatros S.
Published: June 2016

Abstract. Photogrammetry is a valid alternative solution to linear variable differential transformer (LVDT) measurements in structural testing in laboratory conditions. Although the use of LVDTs boasts a high degree of accuracy, on the other hand it is limiting as it offers measurements between two points and it thus might be unable to capture localized deformations and strains over a bigger area of a structural specimen. In this aspect photogrammetry seems to offer certain advantages. Commercial solutions provide limited testing envelopes, while on the other hand, the wide range on new materials need more versatile techniques. Based on the need to develop an in-house photogrammetric toolbox to support several structural and material experiments in the department Advanced Pore Morphology (APM) aluminium foam specimens developed at Fraunhofer IFAM in Germany and cured at CUT, were tested under monotonic compressive load. Data acquisition, analysis and results, along with lessons learnt from the process are presented in this work.
DATA PROVENANCE IN PHOTOGRAMMETRY THROUGH DOCUMENTATION PROTOCOLS
Authors: Carboni N. , Bruseker G. , Guillem A. , Bellido Castañeda D. , Coughenour C. , Domajnko M. , de Kramer M. , Ramos Calles M. M. , Stathopoulou E. K. , Suma R.
Published: June 2016

Abstract. Documenting the relevant aspects in digitisation processes such as photogrammetry in order to provide a robust provenance for their products continues to present a challenge. The creation of a product that can be re-used scientifically requires a framework for consistent, standardised documentation of the entire digitisation pipeline. This article provides an analysis of the problems inherent to such goals and presents a series of protocols to document the various steps of a photogrammetric workflow. We propose this pipeline, with descriptors to track all phases of digital product creation in order to assure data provenance and enable the validation of the operations from an analytic and production perspective. The approach aims to support adopters of the workflow to define procedures with a long term perspective. The conceptual schema we present is founded on an analysis of information and actor exchanges in the digitisation process. The metadata were defined through the synthesis of previous proposals in this area and were tested on a case study. We performed the digitisation of a set of cultural heritage artefacts from an Iron Age burial in Ilmendorf, Germany. The objects were captured and processed using different techniques, including a comparison of different imaging tools and algorithms. This augmented the complexity of the process allowing us to test the flexibility of the schema for documenting complex scenarios. Although we have only presented a photogrammetry digitisation scenario, we claim that our schema is easily applicable to a multitude of 3D documentation processes.
MOTORIZED PANORAMIC CAMERA MOUNT – CALIBRATION AND IMAGE CAPTURE
Authors: Kauhanen H. , Rönnholm P. , Lehtola V. V.
Published: June 2016

Abstract. interesting applications. Among the large variation of panoramic camera systems, we have focused on concentric panoramic imaging with a frame camera. In order to establish the concentric image acquisition, the camera mount must be calibrated so that the projection centre of the camera is located at the rotation centre of the mount. For this purpose, we developed a novel mount calibration method, which allows an accurate recovery of the rotation centre in two image acquisition steps. In addition, we have built a motorized camera mount that can self-calibrate the camera position within the mount, given the previously solved rotation centre, and then be used to automatically capture panoramic images. Hence, we have streamlined the previously laborious manual phase of iterative position calibration, but also automated the capturing of panoramic images. For validation purposes, reference results from a conventional manual mount are provided. In the case of non-motorized mount, the average distance between the projection centre of the camera and the rotation centre of the mount was 0.253 mm and the standard deviation was 0.161 mm. For the motorized mount, the corresponding average distance and standard deviation were 0.549 mm and 0.404 mm, respectively.
ENGLISH FOR SPECIFIC PURPOSES: TEACHING ENGLISH FOR SCIENCE AND TECHNOLOGY
Authors: Musikhin I. A.
Published: June 2016

Abstract. In the era of globalization, English communication for scientists and engineers whose native language is not English has become as important as their major related abilities. The paper describes the results of a four-year experience in the development of English for specific purpose manuals in the field of photogrammetry, interferometry, and GNSS technologies, as well as key teaching methods and didactic approaches used in class and out-of-class activities. The focus of the present study is to provide a detailed description of the development and systematic updating of a relevant manual, aimed at professional language training of learners. The findings of the study reflect the importance of an ESP course for scientists and engineers: conducting a needs analysis for carrying out a specific search of relevant and reliable authentic materials, defining proper teaching methods, software and didactic approaches used in the educational process to develop the language skills necessary to be active and contributive players in the competitive world.
MONITORING OF THE UNDERMINED TERRITORIES OF KARAGANDA COAL BASIN ON THE BASIS OF SATELLITE RADAR INTERFEROMETRY
Authors: Ozhigina S. B. , Mozer D. V. , Ozhigin D. S , Ozhigin S. G. , Bessimbayeva O. G. , Khmyrova E. N.
Published: June 2016

Abstract. In the Karaganda coal basin, mines are located in close proximity to each other and to the city of Karaganda and ongoing mining operations are accompanied by a dangerous process of settling the earth's surface and monitoring are essential for the region's econ-omy. Underground mining leads to the formation of voids in the rock mass, which cause displacement of the earth surface. This paper demonstrates an innovative use of the integrated approach for monitoring on the example of Karaganda coal basin, which includes estimation of the rock mass displacement using leveling profile lines and satellite radar interferometry. It is proved that satellite radar interferometry provides reliable results of surface subsidence measurements in mining areas and can be used for con-sidered sort of monitoring.
INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION
Authors: Teo Tee-Ann , Zhan Kai-Zhi
Published: June 2016

Abstract. The image quality plays an important role for Unmanned Aerial Vehicle (UAV)’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, blinear and bmotion) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.
INTEGRATION OF A GENERALISED BUILDING MODEL INTO THE POSE ESTIMATION OF UAS IMAGES
Authors: Unger J. , Rottensteiner F. , Heipke C.
Published: June 2016

Abstract. A hybrid bundle adjustment is presented that allows for the integration of a generalised building model into the pose estimation of image sequences. These images are captured by an Unmanned Aerial System (UAS) equipped with a camera flying in between the buildings. The relation between the building model and the images is described by distances between the object coordinates of the tie points and building model planes. Relations are found by a simple 3D distance criterion and are modelled as fictitious observations in a Gauss-Markov adjustment. The coordinates of model vertices are part of the adjustment as directly observed unknowns which allows for changes in the model. Results of first experiments using a synthetic and a real image sequence demonstrate improvements of the image orientation in comparison to an adjustment without the building model, but also reveal limitations of the current state of the method.
The Evaluation of GPS techniques for UAV-based Photogrammetry in Urban Area
Authors: Yeh M. L. , Chou Y. T. , Yang L. S.
Published: June 2016

Abstract. The efficiency and high mobility of Unmanned Aerial Vehicle (UAV) made them essential to aerial photography assisted survey and mapping. Especially for urban land use and land cover, that they often changes, and need UAVs to obtain new terrain data and the new changes of land use. This study aims to collect image data and three dimensional ground control points in Taichung city area with Unmanned Aerial Vehicle (UAV), general camera and Real-Time Kinematic with positioning accuracy down to centimetre. The study area is an ecological park that has a low topography which support the city as a detention basin. A digital surface model was also built with Agisoft PhotoScan, and there will also be a high resolution orthophotos. There will be two conditions for this study, with or without ground control points and both were discussed and compared for the accuracy level of each of the digital surface models. According to check point deviation estimate, the model without ground control points has an average two-dimension error up to 40 centimeter, altitude error within one meter. The GCP-free RTK-airborne approach produces centimeter-level accuracy with excellent to low risk to the UAS operators. As in the case of the model with ground control points, the accuracy of x, y, z coordinates has gone up 54.62%, 49.07%, and 87.74%, and the accuracy of altitude has improved the most.
RANDOM FOREST AND OBJECTED-BASED CLASSIFICATION FOR FOREST PEST EXTRACTION FROM UAV AERIAL IMAGERY
Authors: Yuan Yi , Hu Xiangyun
Published: June 2016

Abstract. Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control and exterminate it has not effective enough so far now. The infected areas by it have continuously spreaded out at present. Thus the introduction of spatial information technology is highly demanded. It is very effective to examine the spatial distribution characteristics that can establish timely proper strategies for control against pests by periodically figuring out the infected situations as soon as possible and by predicting the spreading ways of the infection. Now, with the UAV photography being more and more popular, it has become much cheaper and faster to get UAV images which are very suitable to be used to monitor the health of forest and detect the pest. This paper proposals a new method to effective detect forest pest in UAV aerial imagery. For an image, we segment it to many superpixels at first and then we calculate a 12-dimension statistical texture information for each superpixel which are used to train and classify the data. At last, we refine the classification results by some simple rules. The experiments show that the method is effective for the extraction of forest pest areas in UAV images.
COMPACT HYPERSPECTRAL IMAGING SYSTEM (COSI) FOR SMALL REMOTELY PILOTED AIRCRAFT SYSTEMS (RPAS) – SYSTEM OVERVIEW AND FIRST PERFORMANCE EVALUATION RESULTS
Authors: Sima A. A. , Baeck P. , Nuyts D. , Delalieux S. , Livens S. , Blommaert J. , Delauré B. , Boonen M.
Published: June 2016

Abstract. This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute for Technological Research (VITO, Belgium) and suitable for remotely piloted aircraft systems. A hyperspectral dataset captured from a multirotor platform over a strawberry field is presented and explored in order to assess spectral bands co-registration quality. Thanks to application of line based interference filters deposited directly on the detector wafer the COSI camera is compact and lightweight (total mass of 500g), and captures 72 narrow (FWHM: 5nm to 10 nm) bands in the spectral range of 600-900 nm. Covering the region of red edge (680 nm to 730 nm) allows for deriving plant chlorophyll content, biomass and hydric status indicators, making the camera suitable for agriculture purposes. Additionally to the orthorectified hypercube digital terrain model can be derived enabling various analyses requiring object height, e.g. plant height in vegetation growth monitoring. Geometric data quality assessment proves that the COSI camera and the dedicated data processing chain are capable to deliver very high resolution data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results reported in similar studies for an alternative system based on the Fabry–Pérot interferometer.
SENSORS FOR LOCATION-BASED AUGMENTED REALITY THE EXAMPLE OF GALILEO AND EGNOS
Authors: Pagani Alain , Henriques José , Stricker Didier
Published: June 2016

Abstract. Augmented Reality has long been approached from the point of view of Computer Vision and Image Analysis only. However, much more sensors can be used, in particular for location-based Augmented Reality scenarios. This paper reviews the various sensors that can be used for location-based Augmented Reality. It then presents and discusses several examples of the usage of Galileo and EGNOS in conjonction with Augmented Reality.
SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY
Authors: Suresh G. , Gehrke R. , Wiatr T. , Hovenbitzer M.
Published: June 2016

Abstract. Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.
COPERNICUS – PRACTICE OF DAILY LIFE IN A NATIONAL MAPPING AGENCY?
Authors: Wiatr T. , Suresh G. , Gehrke R. , Hovenbitzer M.
Published: June 2016

Abstract. Copernicus is an European system created for Earth observation and monitoring. It consists of a set of Earth observation satellites and in-situ sensors that provide geo-information that are used, through a set of Copernicus services, for applications related to the environment and global security. The main services of the Copernicus programme address six thematic areas: land, marine, atmosphere, climate change, emergency management and security. In Germany, there is a national service team of Copernicus service coordinators, who are responsible for the national development of the Copernicus services and for providing user-specific information about the Copernicus processes. These coordinators represent the contact points for all the programmes and services concerning their respective Copernicus theme. To publish information about Copernicus, national conferences and workshops are organised. Many people are involved in planning the continuous process of bringing the information to public authorities, research institutes and commercial companies. The Federal Agency for Cartography and Geodesy (Bundesamt für Kartographie und Geodäsie, BKG) is one such organisation, and is mainly responsible for the national land monitoring service of Copernicus. To make use of the freely available data from the Copernicus programme, the Federal Agency for Cartography and Geodesy is currently developing new applications and projects in the field of remote sensing and land monitoring. These projects can be used by other public authorities as examples on how to use the Copernicus data and services for their individual demands and requirements. Copernicus data and services are currently not very commonly used in the daily routine of the national mapping agencies, but they will soon be.
MONITORING CAPABILITIES OF A MOBILE MAPPING SYSTEM BASED ON NAVIGATION QUALITIES
Authors: Jing H. , Slatcher N. , Meng X. , Hunter G.
Published: June 2016

Abstract. Mobile mapping systems are becoming increasingly popular as they can build 3D models of the environment rapidly by using a laser scanner that is integrated with a navigation system. 3D mobile mapping has been widely used for applications such as 3D city modelling and mapping of the scanned environments. However, accurate mapping relies on not only the scanner’s performance but also on the quality of the navigation results (accuracy and robustness) . This paper discusses the potentials of using 3D mobile mapping systems for landscape change detection, that is traditionally carried out by terrestrial laser scanners that can be accurately geo-referenced at a static location to produce highly accurate dense point clouds. Yet compared to conventional surveying using terrestrial laser scanners, several advantages of mobile mapping systems can be identified. A large area can be monitored in a relatively short period, which enables high repeat frequency monitoring without having to set-up dedicated stations. However, current mobile mapping applications are limited by the quality of navigation results, especially in different environments. The change detection ability of mobile mapping systems is therefore significantly affected by the quality of the navigation results. This paper presents some data collected for the purpose of monitoring from a mobile platform. The datasets are analysed to address current potentials and difficulties. The change detection results are also presented based on the collected dataset. Results indicate the potentials of change detection using a mobile mapping system and suggestions to enhance quality and robustness.
INITIAL TESTS AND ACCURACY ASSESMENT OF A COMPACT MOBILE LASER SCANNING SYSTEM
Authors: Julge K. , Ellmann A. , Vajakas T. , Kolka R.
Published: June 2016

Abstract. Mobile laser scanning (MLS) is a faster and cost-effective alternative to static laser scanning, even though there is a slight trade-off in accuracy. This contribution describes a compact mobile laser scanning system mounted on a vehicle. The technical parameters of the used system components, i.e. a small LIDAR sensor Velodyne VLP-16 and a dual antenna GNSS/INS system Advanced Navigation Spatial Dual, are reviewed, along with the integration of these components for spatial data acquisition. Calculation principles of 3D coordinates from the real-time data of all the involved sensors are discussed. The field tests were carried out in a controlled environment of a parking lot and at different velocities. Experiments were carried out to test the ability of the GNSS/INS system to cope with difficult conditions, e.g. sudden movements due to cornering or swerving. The accuracy of the resulting MLS point cloud is evaluated with respect to high-accuracy static terrestrial laser scanning data. Problems regarding combining LIDAR, GNSS and INS sensors are outlined, as well as the initial accuracy assessments. Initial tests revealed errors related to insufficient quality of inertial data and a need for the trajectory post-processing calculations. Although this study was carried out while the system was mounted on a car, there is potential for operating the system on an unmanned aerial vehicle, all-terrain vehicle or in a backpack mode due to its relatively compact size.
AUTOMATIC ADJUSTMENT OF WIDE-BASE GOOGLE STREET VIEW PANORAMAS
Authors: Boussias-Alexakis E. , Tsironis V. , Petsa E. , Karras G.
Published: June 2016

Abstract. This paper focuses on the issue of sparse matching in cases of extremely wide-base panoramic images such as those acquired by Google Street View in narrow urban streets. In order to effectively use affine point operators for bundle adjustment, panoramas must be suitably rectified to simulate affinity. To this end, a custom piecewise planar projection (triangular prism projection) is applied. On the assumption that the image baselines run parallel to the street façades, the estimated locations of the vanishing lines of the façade plane allow effectively removing projectivity and applying the ASIFT point operator on panorama pairs. Results from comparisons with multi-panorama adjustment, based on manually measured image points, and ground truth indicate that such an approach, if further elaborated, may well provide a realistic answer to the matching problem in the case of demanding panorama configurations.
ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM
Authors: Li Y. H. , Shinohara T. , Satoh T. , Tachibana K.
Published: June 2016

Abstract. High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.