bluuebunny/crossref_metadata_2025 huggingface dataset:
QUALITY ASSESSMENT OF BUILDING TEXTURES EXTRACTED FROM OBLIQUE AIRBORNE THERMAL IMAGERY
Authors: Iwaszczuk D. , Stilla U.
Published: August 2016

Thermal properties of the building hull became an important topic of the last decade. Combining the thermal data with building models makes it possible to analyze thermal data in a 3D scene. In this paper we combine thermal images with 3D building models by texture mapping. We present a method for texture extraction from oblique airborne thermal infrared images. We put emphasis on quality assessment of these textures and evaluation of their usability for thermal inspections. The quality measures used for assessment are divided to resolution, occlusion and matching quality.
APPROACH FOR IMPROVING THE INTEGRATED SENSOR ORIENTATION
Authors: Mitishita E. , Ercolin Filho L. , Graça N. , Centeno J.
Published: August 2016

The direct determination of exterior orientation parameters (EOP) of aerial images via integration of the Inertial Measurement Unit (IMU) and GPS is often used in photogrammetric mapping nowadays. The accuracies of the EOP depend on the accurate parameters related to sensors mounting when the job is performed (offsets of the IMU relative to the projection centre and the angles of boresigth misalignment between the IMU and the photogrammetric coordinate system). In principle, when the EOP values do not achieve the required accuracies for the photogrammetric application, the approach, known as Integrated Sensor Orientation (ISO), is used to refine the direct EOP. ISO approach requires accurate Interior Orientation Parameters (IOP) and standard deviation of the EOP under flight condition. This paper investigates the feasibility of use the in situ camera calibration to obtain these requirements. The camera calibration uses a small sub block of images, extracted from the entire block. A digital Vexcel UltraCam XP camera connected to APPLANIX POS AVTM system was used to get two small blocks of images that were use in this study. The blocks have different flight heights and opposite flight directions. The proposed methodology improved significantly the vertical and horizontal accuracies of the 3D point intersection. Using a minimum set of control points, the horizontal and vertical accuracies achieved nearly one image pixel of resolution on the ground (GSD). The experimental results are shown and discussed.
A NEW ALGORITHM FOR VOID FILLING IN A DSM FROM STEREO SATELLITE IMAGES IN URBAN AREAS
Authors: Gharib Bafghi Z. , Tian J. , d'Angelo P. , Reinartz P.
Published: August 2016

Digital Surface Models (DSM) derived from stereo-pair satellite images are the main sources for many Geo-Informatics applications like 3D change detection, object classification and recognition. However since occlusion especially in urban scenes result in some deficiencies in the stereo matching phase, these DSMs contain some voids. In order to fill the voids a range of algorithms have been proposed, mainly including interpolation alone or along with auxiliary DSM. In this paper an algorithm for void filling in DSM from stereo satellite images has been developed. Unlike common previous approaches we didn’t use any external DSM to fill the voids. Our proposed algorithm uses only the original images and the unfilled DSM itself. First a neighborhood around every void in the unfilled DSM and its corresponding area in multispectral image is defined. Then it is analysed to extract both spectral and geometric texture and accordingly to assign labels to each cell in the voids. This step contains three phases comprising shadow detection, height thresholding and image segmentation. Thus every cell in void has a label and is filled by the median value of its co-labelled neighbors. The results for datasets from WorldView-2 and IKONOS are shown and discussed.
DIRECT IMAGE-TO-GEOMETRY REGISTRATION USING MOBILE SENSOR DATA
Authors: Kehl C. , Buckley S. J. , Gawthorpe R. L. , Viola I. , Howell J. A.
Published: August 2016

Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm’s accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.
Authors: Moyo T. , Musakwa W.
Published: August 2016

The study of commuters’ origins and destinations (O_D) promises to assist transportation planners with prediction models to inform decision making. Conventionally O_D surveys are undertaken through travel surveys and traffic counts, however data collection for these surveys has historically proven to be time consuming and having a strain on human resources, thus a need for an alternative data source arises. This study combines the use social media data and geographic information systems in the creation of a model for origin and destination surveys. The model tests the potential of using big data from Echo echo software which contains Twitter and Facebook data obtained from social media users in Gauteng. This data contains geo-location and it is used to determine origin and destination as well as concentration levels of Gautrain commuters. A kriging analysis was performed on the data to determine the O-D and concentration levels of Gautrain users. The results reveal the concentration of Gautrain commuters at various points of interest that is where they work, live or socialise. The results from the study highlight which nodes attract the most commuters and also possible locations for the expansion for Gautrain. Lastly, the study also highlights some weakness of crowdsourced data for informing transportation planning.
BIG BICYCLE DATA PROCESSING: FROM PERSONAL DATA TO URBAN APPLICATIONS
Authors: Pettit C. J. , Lieske S. N. , Leao S. Z.
Published: August 2016

Understanding the flows of people moving through the built environment is a vital source of information for the planners and policy makers who shape our cities. Smart phone applications enable people to trace themselves through the city and these data can potentially be then aggregated and visualised to show hot spots and trajectories of macro urban movement. In this paper our aim is to develop procedures for cleaning, aggregating and visualising human movement data and translating this into policy relevant information. In conducting this research we explore using bicycle data collected from a smart phone application known as RiderLog. We focus on the RiderLog application initially in the context of Sydney, Australia and discuss the procedures and challenges in processing and cleaning this data before any analysis can be made. We then present some preliminary map results using the CartoDB online mapping platform where data are aggregated and visualised to show hot spots and trajectories of macro urban movement. We conclude the paper by highlighting some of the key challenges in working with such data and outline some next steps in processing the data and conducting higher volume and more extensive analysis.
TRACKING THE SPATIAL EVOLUTION OF URBAN HEAT ISLANDS
Authors: Zhu Rui , Guilbert Eric , Wong Man Sing
Published: August 2016

The urban heat island (UHI) phenomenon occurring in the urban areas or city-clusters is increasingly becoming a severe problem in the urbanization process. Previous research mainly rely on grid analysis techniques to study temperature data from images recorded at fixed time instants. The evolutionary process of UHI in both time and space has not been investigated yet. This research designs an object-oriented spatiotemporal model to reconstruct the evolution of UHI and provide a qualitative interpretation. Each UHI is modeled as a spatiotemporal field object with it own life cycle. Dynamic behavior of an UHI is defined by sequences of spatial changes (e.g. contraction or expansion) and topological transformations (e.g. merge or split). The model is implemented in an object-relational database and applied to air temperature data collected from weather stations every hour over three days. UHIs with their behavior were extracted from the data. Results suggest that the model can effectively track and provide a qualitative description of the UHI evolution.
A MEDIAN-BASED DEPTHMAP FUSION STRATEGY FOR THE GENERATION OF ORIENTED POINTS
Authors: Rothermel M. , Haala N. , Fritsch D.
Published: August 2016

Due to good scalability, systems for image-based dense surface reconstruction often employ stereo or multi-baseline stereo methods. These types of algorithms represent the scene by a set of depth or disparity maps which eventually have to be fused to extract a consistent, non-redundant surface representation. Generally the single depth observations across the maps possess variances in quality. Within the fusion process not only preservation of precision and detail but also density and robustness with respect to outliers are desirable. Being prune to outliers, in this article we propose a local median-based algorithm for the fusion of depth maps eventually representing the scene as a set of oriented points. Paying respect to scalability, points induced by each of the available depth maps are streamed to cubic tiles which then can be filtered in parallel. Arguing that the triangulation uncertainty is larger in the direction of image rays we define these rays as the main filter direction. Within an additional strategy we define the surface normals as the principle direction for median filtering/integration. The presented approach is straight-forward to implement since employing standard oc- and kd-tree structures enhanced by nearest neighbor queries optimized for cylindrical neighborhoods. We show that the presented method in combination with the MVS (Rothermel et al., 2012) produces surfaces comparable to the results of the Middlebury MVS benchmark and favorably compares to an state-of-the-art algorithm employing the Fountain dataset (Strecha et al., 2008). Moreover, we demonstrate its capability of depth map fusion for city scale reconstructions derived from large frame airborne imagery.
DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS
Authors: Aijazi A. K. , Malaterre L. , Tazir M. L. , Trassoudaine L. , Checchin P.
Published: August 2016

This work presents a new method that automatically detects and analyzes surface defects such as corrosion spots of different shapes and sizes, on large ship hulls. In the proposed method several scans from different positions and viewing angles around the ship are registered together to form a complete 3D point cloud. The R, G, B values associated with each scan, obtained with the help of an integrated camera are converted into HSV space to separate out the illumination invariant color component from the intensity. Using this color component, different surface defects such as corrosion spots of different shapes and sizes are automatically detected, within a selected zone, using two different methods depending upon the level of corrosion/defects. The first method relies on a histogram based distribution whereas the second on adaptive thresholds. The detected corrosion spots are then analyzed and quantified to help better plan and estimate the cost of repair and maintenance. Results are evaluated on real data using different standard evaluation metrics to demonstrate the efficacy as well as the technical strength of the proposed method.
ROBUST LOW-ALTITUDE IMAGE MATCHING BASED ON LOCAL REGION CONSTRAINT AND FEATURE SIMILARITY CONFIDENCE
Authors: Chen Min , Zhu Qing , Huang Shengzhi , Hu Han , Wang Jingxue
Published: August 2016

Improving the matching reliability of low-altitude images is one of the most challenging issues in recent years, particularly for images with large viewpoint variation. In this study, an approach for low-altitude remote sensing image matching that is robust to the geometric transformation caused by viewpoint change is proposed. First, multiresolution local regions are extracted from the images and each local region is normalized to a circular area based on a transformation. Second, interest points are detected and clustered into local regions. The feature area of each interest point is determined under the constraint of the local region which the point belongs to. Then, a descriptor is computed for each interest point by using the classical scale invariant feature transform (SIFT). Finally, a feature matching strategy is proposed on the basis of feature similarity confidence to obtain reliable matches. Experimental results show that the proposed method provides significant improvements in the number of correct matches compared with other traditional methods.
OBJECT CLASSIFICATION VIA PLANAR ABSTRACTION
Authors: Oesau Sven , Lafarge Florent , Alliez Pierre
Published: August 2016

We present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar shapes provides a means to both reduce the computational complexity and improve robustness to defects inherent to the acquisition process. Measuring statistical properties and relationships between planar shapes offers invariance to scale and orientation. A random forest is then used for solving the multiclass classification problem. We demonstrate the potential of our approach on a set of indoor objects from the Princeton shape benchmark and on objects acquired from indoor scenes and compare the performance of our method with other point-based shape descriptors.
DETECTING ANOMALY REGIONS IN SATELLITE IMAGE TIME SERIES BASED ON SESAONAL AUTOCORRELATION ANALYSIS
Authors: Zhou Z.-G. , Tang P. , Zhou M.
Published: August 2016

Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.
FAST AND RESISTANT PROCRUSTEAN BUNDLE ADJUSTMENT
Authors: Fusiello Andrea , Crosilla Fabio
Published: August 2016

In a recent paper (Fusiello and Crosilla, 2015) a Procrustean formulation of the bundle block adjustment has been presented, with a solution based on alternating least squares. This paper improves on it in two respects: it introduces a faster iterative scheme that minimizes the same cost function, thereby achieving the same accuracy, and makes the method resistant to rogue measures through iteratively reweighted least-squares. Empirical results confirm the effectiveness of these enhancements.
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: August 2016

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).
AN IHS-BASED ENHANCEMENT METHOD WITH IMPROVED SCALE/SHIFT PARAMETER OF LINEAR MODEL
Authors: Bingxian Zhang
Published: August 2016

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: August 2016

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.
PALM TREE DETECTION USING CIRCULAR AUTOCORRELATION OF POLAR SHAPE MATRIX
Authors: Manandhar A. , Hoegner L. , Stilla U.
Published: August 2016

Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape matrix representation of an image, as the shape feature, and the linear support vector machine to standardize and reduce dimensions of the feature. Finally, the study uses local maximum detection algorithm on the spatial distribution of standardized feature to detect palm trees. The method was applied to 8 images chosen from different tough scenarios and it performed on average with an accuracy of 84% and 76.1%, despite being subjected to different challenging conditions in the chosen test images.
SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS
Authors: Marmanis D. , Wegner J. D. , Galliani S. , Schindler K. , Datcu M. , Stilla U.
Published: August 2016

This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.
TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM
Authors: Hu Han , Chen Chongtai , Wu Bo , Yang Xiaoxia , Zhu Qing , Ding Yulin
Published: August 2016

Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.
A EUCLIDEAN FORMULATION OF INTERIOR ORIENTATION COSTRAINTS IMPOSED BY THE FUNDAMENTAL MATRIX
Authors: Kalisperakis I. , Karras G. , Petsa E.
Published: August 2016

Epipolar geometry of a stereopair can be expressed either in 3D, as the relative orientation (i.e. translation and rotation) of two bundles of optical rays in case of calibrated cameras or, in case of unclalibrated cameras, in 2D as the position of the epipoles on the image planes and a projective transformation that maps points in one image to corresponding epipolar lines on the other. The typical coplanarity equation describes the first case; the Fundamental matrix describes the second. It has also been proven in the Computer Vision literature that 2D epipolar geometry imposes two independent constraints on the parameters of camera interior orientation. In this contribution these constraints are expressed directly in 3D Euclidean space by imposing the equality of the dihedral angle of epipolar planes defined by the optical axes of the two cameras or by suitably chosen corresponding epipolar lines. By means of these constraints, new closed form algorithms are proposed for the estimation of a variable or common camera constant value given the fundamental matrix and the principal point position of a stereopair.
NONZONAL EXPRESSIONS OF GAUSS–KRÜGER PROJECTION IN POLAR REGIONS
Authors: Li Zhongmei , Bian Shaofeng , Liu Qiang , Li Houpu , Chen Cheng , Hu Yanfeng
Published: August 2016

With conformal colatitude introduced, based on the mathematical relationship between exponential and logarithmic functions by complex numbers, strict equation of complex conformal colatitude is derived, and then theoretically strict nonzonal expressions of Gauss projection in polar regions are carried out. By means of the computer algebra system, correctness of these expressions is verified, and sketches of Gauss-krüger projection without bandwidth restriction in polar regions are charted. In the Arctic or Antarctic region, graticule of nonzonal Gauss projection complies with people’s reading habit and reflects real ground-object distribution. Achievements in this paper could perfect mathematical basis of Gauss projection and provide reference frame for polar surveying and photogrammetry.
DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI)
Authors: Bhattacharya D. , Painho M.
Published: August 2016

Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. It is about research to harness the sensed environment by utilizing domain specific sensor data to create a generalized sensor webframework. The challenges being semantic enablement for Spatial Data Infrastructures, and connecting the interfaces of SDI with interfaces of Sensor Web. The proposed research plan is to Identify sensor data sources, Setup an open source SDI, Match the APIs and functions between Sensor Web and SDI, and Case studies like hazard applications, urban applications etc. We take up co-operative development of SDI best practices to enable a new realm of a location enabled and semantically enriched World Wide Web - the "Geospatial Web" or "Geosemantic Web" by setting up one to one correspondence between WMS, WFS, WCS, Metadata and 'Sensor Observation Service' (SOS); 'Sensor Planning Service' (SPS); 'Sensor Alert Service' (SAS); a service that facilitates asynchronous message interchange between users and services, and between two OGC-SWE services, called the 'Web Notification Service' (WNS). Hence in conclusion, it is of importance to geospatial studies to integrate SDI with Sensor Web. The integration can be done through merging the common OGC interfaces of SDI and Sensor Web. Multi-usability studies to validate integration has to be undertaken as future research.
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: August 2016

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.
A SIMULATION TOOL ASSISTING THE DESIGN OF A CLOSE RANGE PHOTOGRAMMETRY SYSTEM FOR THE SARDINIA RADIO TELESCOPE
Authors: Buffa F. , Pinna A. , Sanna G.
Published: August 2016

The Sardinia Radio Telescope (SRT) is a 64 m diameter antenna, whose primary mirror is equipped with an active surface capable to correct its deformations by means of a thick network of actuators. Close range photogrammetry (CRP) was used to measure the self-load deformations of the SRT primary reflector from its optimal shape, which are requested to be minimized for the radio telescope to operate at full efficiency. In the attempt to achieve such performance, we conceived a near real-time CRP system which requires the cameras to be installed in fixed positions and at the same time to avoid any interference with the antenna operativeness. The design of such system is not a trivial task, and to assist our decision we therefore developed a simulation pipeline to realistically reproduce and evaluate photogrammetric surveys of large structures. The described simulation environment consists of (i) a detailed description of the SRT model, included the measurement points and the camera parameters, (ii) a tool capable of generating realistic images accordingly to the above model, and (iii) a self-calibrating bundle adjustment to evaluate the performance in terms of RMSE of the camera configurations.
3D CENTRAL LINE EXTRACTION OF FOSSIL OYSTER SHELLS
Authors: Djuricic A. , Puttonen E. , Harzhauser M. , Mandic O. , Székely B. , Pfeifer N.
Published: August 2016

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: August 2016

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.
STRUCTURAL 3D MONITORING USING A NEW SINUSOIDAL FITTING ADJUSTMENT
Authors: Detchev I. , Habib A. , Lichti D. , El-Badry M.
Published: August 2016

Digital photogrammetric systems combined with image processing techniques have been used for structural monitoring purposes for more than a decade. For applications requiring sub-millimetre level precision, the use of off-the-shelf DSLR cameras is a suitable choice, especially when the low cost of the involved sensors is a priority. The disadvantage in the use of entry level DSLRs is that there is a trade-off between frame rate and burst rate – a high frame rate is either not available or it cannot be sustained long enough. This problem must be overcome when monitoring a structural element undergoing a dynamic test, where a range of loads are cycled through multiple times a second. In order to estimate deflections during such a scenario, this paper proposes a new least-squares adjustment for sinusoidal fitting. The new technique is capable of processing multiple back-to-back bursts of data within the same adjustment, which synthetically increases the de-facto temporal resolution of the system. The paper describes a beam deformation test done in a structures laboratory. The experimental results were assessed in terms of both their precision and accuracy. The new method increased the effective sampling frequency three-fold, which improved the standard deviations of the estimated parameters with up to two orders of magnitude. A residual RMSE as low as 30 μm was attained, and likewise the RMSE of the computed amplitudes between the photogrammetric system and the control laser transducers was as small as 34 μm.
DEFORMATION MONITORING OF MATERIALS UNDER STRESS IN LABORATORY EXPERIMENTS
Authors: Skarlatos D. , Yiatros S.
Published: August 2016

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.
BIM FROM LASER SCANS… NOT JUST FOR BUILDINGS: NURBS-BASED PARAMETRIC MODELING OF A MEDIEVAL BRIDGE
Authors: Barazzetti L. , Banfi F. , Brumana R. , Previtali M. , Roncoroni F.
Published: August 2016

Building Information Modelling is not limited to buildings. BIM technology includes civil infrastructures such as roads, dams, bridges, communications networks, water and wastewater networks and tunnels. This paper describes a novel methodology for the generation of a detailed BIM of a complex medieval bridge. The use of laser scans and images coupled with the development of algorithms able to handle irregular shapes allowed the creation of advanced parametric objects, which were assembled to obtain an accurate BIM. The lack of existing object libraries required the development of specific families for the different structural elements of the bridge. Finally, some applications aimed at assessing the stability and safety of the bridge are illustrated and discussed. The BIM of the bridge can incorporate this information towards a new “BIMonitoring” concept to preserve the geometric complexity provided by point clouds, obtaining a detailed BIM with object relationships and attributes.
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: August 2016

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.
A RADICAL COLLABORATIVE APPROACH: DEVELOPING A MODEL FOR LEARNING THEORY, HUMAN-BASED COMPUTATION AND PARTICIPANT MOTIVATION IN A ROCK-ART HERITAGE APPLICATION
Authors: Haubt R.
Published: August 2016

This paper explores a Radical Collaborative Approach in the global and centralized Rock-Art Database project to find new ways to look at rock-art by making information more accessible and more visible through public contributions. It looks at rock-art through the Key Performance Indicator (KPI), identified with the latest Australian State of the Environment Reports to help develop a better understanding of rock-art within a broader Cultural and Indigenous Heritage context. Using a practice-led approach the project develops a conceptual collaborative model that is deployed within the RADB Management System. Exploring learning theory, human-based computation and participant motivation the paper develops a procedure for deploying collaborative functions within the interface design of the RADB Management System. The paper presents the results of the collaborative model implementation and discusses considerations for the next iteration of the RADB Universe within an Agile Development Approach.
FILTERING TECHNIQUES ON ANALYSIS OF ARCHEOLOGY AREAS USING RADARSAT IMAGES: CASE STUDY OF LEMBAH BUJANG, MALAYSIA
Authors: Norzailawati M. N. , Akma R. S. , Alias A. , Zuraini M. A.
Published: August 2016

Speckle noise present in radar imagery caused by interaction of out –of-phase waves with a target, the objective of this paper is attempt to test filtering techniques consist of Lee, Frost and Gamma Map to identify a potential shrines area in Lembah Bujang using RADARSAT imageries. The multi-temporal images of RADARSAT for years 2003 and 2014 have been used filtering techniques in identifying potential shrines consist of have been used and tested to selected study areas with using processing software of ENVI 4.8 and ArcGIS 10.2. Based on mathematical morphology, the speckles in these images were reduced, once the reduction is achieved, the enhancement of archaeological sites is accomplished. The finding shows that Local Adaptive Filtering on GAMMA Map filter is the best techniques in identifying potential shrines areas at once as guidance to pursuing an area as official gazette historical site in Malaysia context.
CHANGE DETECTION WITH MULTI-SOURCE DEFECTIVE REMOTE SENSING IMAGES BASED ON EVIDENTIAL FUSION
Authors: Chen Xi , Li Jing , Zhang Yunfei , Tao Liangliang
Published: August 2016

Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the proposed method, post-classification comparison process is firstly performed on multi-source defective images. Then, the classification results of all the images, together with their corresponding confusion matrixes are used to calculate the Basic Belief Assignment (BBA) of each pixel. Further, based on the principle of Dempster-Shafer evidence theory, a BBA redistribution process is introduced to deal with the defective parts of multi-source data. At last, evidential fusion and decision making rules are applied on the pixel level, and the final map of change detection can be derived. The proposed method can finish change detection with data fusion and image completion in one integrated process, which makes use of the complementary and redundant information from the input images. The method is applied to a case study of landslide barrier lake formed in Aug. 3rd, 2014, with a series of multispectral images from different sensors of GF-1 satellite. Result shows that the proposed method can not only complete the defective parts of the input images, but also provide better change detection accuracy than post-classification comparison method with single pair of pre- and post-change images. Subsequent analysis indicates that high conflict degree between evidences is the main source of errors in the result. Finally, some possible reasons that result in evidence conflict on the pixel level are analysed.