bluuebunny/crossref_metadata_2025 huggingface dataset:
SHAPE FUNCTION-BASED ESTIMATION OF DEFORMATION WITH MOVING CAMERAS ATTACHED TO THE DEFORMING BODY
Authors: Jokinen O. , Ranta I. , Haggrén H. , Rönnholm P.
Published: August 2016

The paper presents a novel method to measure 3-D deformation of a large metallic frame structure of a crane under loading from one to several images, when the cameras need to be attached to the self deforming body, the structure sways during loading, and the imaging geometry is not optimal due to physical limitations. The solution is based on modeling the deformation with adequate shape functions and taking into account that the cameras move depending on the frame deformation. It is shown that the deformation can be estimated even from a single image of targeted points if the 3-D coordinates of the points are known or have been measured before loading using multiple cameras or some other measuring technique. The precision of the method is evaluated to be 1 mm at best, corresponding to 1:11400 of the average distance to the target.
AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS
Authors: Protopapadakis E. , Stentoumis C. , Doulamis N. , Doulamis A. , Loupos K. , Makantasis K. , Kopsiaftis G. , Amditis A.
Published: August 2016

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: August 2016

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.
RESEARCH AND TECHNOLOGY DEVELOPMENT FOR CONSTRUCTION OF 3D VIDEO SCENES
Authors: Khlebnikova Tatyana A.
Published: August 2016

For the last two decades surface information in the form of conventional digital and analogue topographic maps has been being supplemented by new digital geospatial products, also known as 3D models of real objects. It is shown that currently there are no defined standards for 3D scenes construction technologies that could be used by Russian surveying and cartographic enterprises. The issues regarding source data requirements, their capture and transferring to create 3D scenes have not been defined yet. The accuracy issues for 3D video scenes used for measuring purposes can hardly ever be found in publications. Practicability of development, research and implementation of technology for construction of 3D video scenes is substantiated by 3D video scene capability to expand the field of data analysis application for environmental monitoring, urban planning, and managerial decision problems. The technology for construction of 3D video scenes with regard to the specified metric requirements is offered. Technique and methodological background are recommended for this technology used to construct 3D video scenes based on DTM, which were created by satellite and aerial survey data. The results of accuracy estimation of 3D video scenes are presented.
PRODUCT ACCURACY EFFECT OF OBLIQUE AND VERTICAL NON-METRIC DIGITAL CAMERA UTILIZATION IN UAV-PHOTOGRAMMETRY TO DETERMINE FAULT PLANE
Authors: Amrullah C. , Suwardhi D. , Meilano I.
Published: August 2016

This study aims to see the effect of non-metric oblique and vertical camera combination along with the configuration of the ground control points to improve the precision and accuracy in UAV-Photogrammetry project. The field observation method is used for data acquisition with aerial photographs and ground control points. All data are processed by digital photogrammetric process with some scenarios in camera combination and ground control point configuration. The model indicates that the value of precision and accuracy increases with the combination of oblique and vertical camera at all control point configuration. The best products of the UAV-Photogrammetry model are produced in the form of Digital Elevation Model (DEM) compared to the LiDAR DEM. Furthermore, DEM from UAV-Photogrammetry and LiDAR are used to define the fault plane by using cross-section on the model and interpretation to determine the point at the extreme height of terrain changes. The result of the defined fault planes indicate that two models do not show any significant difference.
ROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER
Authors: Srestasathiern P. , Lawawirojwong S. , Suwantong R. , Phuthong P
Published: August 2016

This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.
A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION
Authors: Wang Ping , Wei Zheng , Cui Weihong , Lin Zhiyong
Published: August 2016

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.
BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES
Authors: Tian J. , Qin R. , Cerra D. , Reinartz P.
Published: August 2016

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.
MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA
Authors: Zaki N. A. M. , Latif Z. A. , Suratman M. N. , Zainal M. Z.
Published: August 2016

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.
SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES
Authors: Le Bris A. , Chehata N. , Briottet X. , Paparoditis N.
Published: August 2016

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) to material classification was also shown.
CROP TYPE MAPPING FROM A SEQUENCE OF TERRASAR-X IMAGES WITH DYNAMIC CONDITIONAL RANDOM FIELDS
Authors: Kenduiywo B. K. , Bargiel D. , Soergel U.
Published: August 2016

Crop phenology is dynamic as it changes with times of the year. Such biophysical processes also look spectrally different to remote sensing satellites. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, conventional approaches that select only images from phenological stages where crops are distinguishable for classification, have low discrimination. In contrast, stacking images within a cropping season limits discrimination to a single feature space that can suffer from overlapping classes. Since crop backscatter varies with time, it can aid discrimination. Therefore, our main objective is to develop a crop sequence classification method using multitemporal TerraSAR-X images. We adopt first order markov assumption in undirected temporal graph sequence. This property is exploited to implement Dynamic Conditional Random Fields (DCRFs). Our DCRFs model has a repeated structure of temporally connected Conditional Random Fields (CRFs). Each node in the sequence is connected to its predecessor via conditional probability matrix. The matrix is computed using posterior class probabilities from association potential. This way, there is a mutual temporal exchange of phenological information observed in TerraSAR-X images. When compared to independent epoch classification, the designed DCRF model improved crop discrimination at each epoch in the sequence. However, government, insurers, agricultural market traders and other stakeholders are interested in the quantity of a certain crop in a season. Therefore, we further develop a DCRF ensemble classifier. The ensemble produces an optimal crop map by maximizing over posterior class probabilities selected from the sequence based on maximum F1-score and weighted by correctness. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using Maximum Likelihood Classifier (MLC) and standard CRFs. It outperforms MLC and CRFs by 7.70% and 6.42% in overall accuracy, respectively.
BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA
Authors: Papadomanolaki M. , Vakalopoulou M. , Zagoruyko S. , Karantzalos K.
Published: August 2016

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.
INVESTIGATION OF LATENT TRACES USING INFRARED REFLECTANCE HYPERSPECTRAL IMAGING
Authors: Schubert Till , Wenzel Susanne , Roscher Ribana , Stachniss Cyrill
Published: August 2016

The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.
RAPID RISK EVALUATION (ER2) USING MS EXCEL SPREADSHEET: A CASE STUDY OF FREDERICTON (NEW BRUNSWICK, CANADA)
Authors: McGrath H. , Stefanakis E. , Nastev M.
Published: August 2016

Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools and guidance to adequately undertake flood hazard risk assessment in order to estimate respective damages and social and economic losses. While many complex computer models have been developed for flood risk assessment, they require highly trained personnel to prepare the necessary input (hazard, inventory of the built environment, and vulnerabilities) and analyze model outputs. As such, tools which utilize open-source software or are built within popular desktop software programs are appealing alternatives. The recently developed Rapid Risk Evaluation (ER2) application runs scenario based loss assessment analyses in a Microsoft Excel spreadsheet. User input is limited to a handful of intuitive drop-down menus utilized to describe the building type, age, occupancy and the expected water level. In anticipation of local depth damage curves and other needed vulnerability parameters, those from the U.S. FEMA’s Hazus-Flood software have been imported and temporarily accessed in conjunction with user input to display exposure and estimated economic losses related to the structure and the content of the building. Building types and occupancies representative of those most exposed to flooding in Fredericton (New Brunswick) were introduced and test flood scenarios were run. The algorithm was successfully validated against results from the Hazus-Flood model for the same building types and flood depths.
AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL
Authors: Tu Jihui , Sui Haigang , Feng Wenqing , Song Zhina
Published: August 2016

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.
REMOTELY-SENSED GLACIER CHANGE ESTIMATION: A CASE STUDY AT LINDBLAD COVE, ANTARCTIC PENINSULA
Authors: Fieber K. D. , Mills J. P. , Miller P. E. , Fox A. J.
Published: August 2016

This study builds on existing literature of glacier change estimation in polar regions and is a continuation of efforts aimed at unlocking the information encapsulated in archival aerial photography of Antarctic Peninsula glaciers. Historical aerial imagery acquired in 1957 over three marine-terminating glaciers at Lindblad Cove on the West Coast of Trinity Peninsula is processed to extract digital elevation models (DEMs) which are subsequently compared to DEMs generated from present day (2014) WorldView-2 satellite stereo-imagery. The new WorldView-2 images offer unprecedented sub-metre resolution of the Antarctic Peninsula and are explored here to facilitate improved registration and higher accuracy analysis of glacier changes. Unlike many studies, which focus on glacier fronts or only restricted regions of glaciers, this paper presents a complete coverage of elevation changes across the glacier surfaces for two of the studied glaciers. The study utilises a robust least squares matching technique to ensure precise registration of the archival and modern DEMs, which is applied due to lack of existing ground control in this remote region. This case study reveals that, while many glaciers in polar regions are reported as experiencing significant mass loss, some glaciers are stable or even demonstrate mass gain. All three glaciers reported here demonstrated overall mean increases in surface elevation, indicative of positive mass balance ranging from 0.6 to 5.8 metre water equivalent between 1957 and 2014.
EMPIRICAL RELATIONSHIP BETWEEN LEAF BIOMASS OF RED PINE FORESTS AND ENHANCED VEGETATION INDEX IN SOUTH KOREA USING LANDSAT-5 TM
Authors: Gusso A. , Lee J. , Son Y. , Son Y. M.
Published: August 2016

Research on forest carbon (C) dynamics has been undertaken due to the importance of forest ecosystems in national C inventories. Currently, the C sequestration of South Korean forests surpasses that of other countries. In South Korea, Pinus densiflora (red pine) is the most abundant tree species. Thus, understanding the growth rate and biomass evolution of red pine forest in South Korea is important for estimating the forest C dynamics. In this paper, we derived empirical relationship between foliage biomass and the no blue band enhanced vegetation index (EVI-2) profile using both field work and multi-temporal Landsat-5 TM remote sensing data to estimate the productivity of forest biomass in South Korea. Our analysis combined a set of 84 Landsat-5 TM images from 28 different dates between 1986 and 2008 to study red pine forest development over time. Field data were collected from 30 plots (0.04 ha) that were irregularly distributed over South Korea. Individual trees were harvested by destructive sampling, and the age of trees were determined by the number of tree rings. The results are realistic (R2&thinsp=&thinsp0.81, p < 0.01) and suggest that the EVI-2 index is able to adequately represent the development profile of foliage biomass in red pine forest growth.
ODOMETRY AND LOW-COST SENSOR FUSION IN TMM DATASET
Authors: Manzino A. M. , Taglioretti C.
Published: August 2016

The aim of this study is to identify the most powerful motion model and filtering technique to represent an urban terrestrial mobile mapping (TMM) survey and ultimately to obtain the best representation of the car trajectory. The authors want to test how far a motion model and a more or less refined filtering technique could bring benefits in the determination of the car trajectory.

 To achieve the necessary data for the application of the motion models and the filtering techniques described in the article, the authors realized a TMM survey in the urban centre of Turin by equipping a vehicle with various instruments: a low-cost action-cam also able to record the GPS trace of the vehicle even in the presence of obstructions, an inertial measurement system and an odometer.

 The results of analysis show in the article indicate that the Unscented Kalman Filter (UKF) technique provides good results in the determination of the vehicle trajectory, especially if the motion model considers more states (such as the positions, the tangential velocity, the angular velocity, the heading, the acceleration). The authors also compared the results obtained with a motion model characterized by four, five and six states.

 A natural corollary to this work would be the introduction to the UKF of the photogrammetric information obtained by the same camera placed on board the vehicle. These data would permit to establish how photogrammetric measurements can improve the quality of TMM solutions, especially in the absence of GPS signals (like urban canyons).
ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY
Authors: Kerner S. , Kaufman I. , Raizman Y.
Published: August 2016

Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching.

 In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage.

 We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.
STUDY OF LEVER-ARM EFFECT USING EMBEDDED PHOTOGRAMMETRY AND ON-BOARD GPS RECEIVER ON UAV FOR METROLOGICAL MAPPING PURPOSE AND PROPOSAL OF A FREE GROUND MEASUREMENTS CALIBRATION PROCEDURE
Authors: Daakir M. , Pierrot-Deseilligny M. , Bosser P. , Pichard F. , Thom C. , Rabot Y.
Published: August 2016

Nowadays, Unmanned Aerial Vehicle (UAV) on-board photogrammetry knows a significant growth due to the democratization of using drones in the civilian sector. Also, due to changes in regulations laws governing the rules of inclusion of a UAV in the airspace which become suitable for the development of professional activities. Fields of application of photogrammetry are diverse, for instance: architecture, geology, archaeology, mapping, industrial metrology, etc. Our research concerns the latter area. Vinci-Construction- Terrassement is a private company specialized in public earthworks that uses UAVs for metrology applications. This article deals with maximum accuracy one can achieve with a coupled camera and GPS receiver system for direct-georeferencing of Digital Surface Models (DSMs) without relying on Ground Control Points (GCPs) measurements. This article focuses specially on the lever-arm calibration part. This proposed calibration method is based on two steps: a first step involves the proper calibration for each sensor, i.e. to determine the position of the optical center of the camera and the GPS antenna phase center in a local coordinate system relative to the sensor. A second step concerns a 3d modeling of the UAV with embedded sensors through a photogrammetric acquisition. Processing this acquisition allows to determine the value of the lever-arm offset without using GCPs.
PRECISION ANALYSIS OF POINT-AND-SCALE PHOTOGRAMMETRIC MEASUREMENTS FOR CORRIDOR MAPPING: PRELIMINARY RESULTS
Authors: Molina P. , Blázquez M. , Sastre J. , Colomina I.
Published: August 2016

This paper addresses the key aspects of the sensor orientation and calibration approach within the mapKITE concept for corridor mapping, focusing on the contribution analysis of point-and-scale measurements of kinematic ground control points. MapKITE is a new mobile, simultaneous terrestrial and aerial, geodata acquisition and post-processing method. On one hand, the acquisition system is a tandem composed of a terrestrial mobile mapping system and an unmanned aerial system, the latter equipped with a remote sensing payload, and linked through a 'virtual tether', that is, a real-time waypoint supply from the terrestrial vehicle to the unmanned aircraft. On the other hand, mapKITE entails a method for geodata post-processing (specifically, sensor orientation and calibration) based on the described acquisition paradigm, focusing on few key aspects: the particular geometric relationship of a mapKITE network – the aerial vehicle always observes the terrestrial one as they both move –, precise air and ground trajectory determination – the terrestrial vehicle is regarded as a kinematic ground control point – and new photogrammetric measurements – pointing on and measuring the scale of an optical target on the roof of the terrestrial vehicle – are exploited.

 In this paper, we analyze the performance of aerial image orientation and calibration in mapKITE for corridor mapping, which is the natural application niche of mapKITE, based on the principles and procedures of integrated sensor orientation with the addition of point-and-scale photogrammetric measurements of the kinematic ground control points. To do so, traditional (static ground control points, photogrammetric tie points, aerial control) and new (pointing-and-scaling of kinematic ground control points) measurements have been simulated for mapKITE corridor mapping missions, consisting on takeoff and calibration pattern, single-pass corridor operation potentially performing calibration patterns, and landing and calibration pattern. Our preliminary results show that the exterior orientation, interior orientation and tie points precision estimates are better when using kinematic control with few static ground control, and even with excluding the latter. We conclude then that mapKITE can be a breakthrough on the UAS-based corridor mapping field, as precision requirements can be achieved for single-pass operation with no need for traditional static ground control points.
EXPLORING THE POTENTIAL OF AERIAL PHOTOGRAMMETRY FOR 3D MODELLING OF HIGH-ALPINE ENVIRONMENTS
Authors: Legat K. , Moe K. , Poli D. , Bollmannb E.
Published: August 2016

High-alpine areas are subject to rapid topographic changes, mainly caused by natural processes like glacial retreat and other geomorphological processes, and also due to anthropogenic interventions like construction of slopes and infrastructure in skiing resorts. Consequently, the demand for highly accurate digital terrain models (DTMs) in alpine environments has arisen. Public administrations often have dedicated resources for the regular monitoring of glaciers and natural hazard processes. In case of glaciers, traditional monitoring encompasses in-situ measurements of area and length and the estimation of volume and mass changes. Next to field measurements, data for such monitoring programs can be derived from DTMs and digital ortho photos (DOPs). Skiing resorts, on the other hand, require DTMs as input for planning and – more recently – for RTK-GNSS supported ski-slope grooming. Although different in scope, the demand of both user groups is similar: high-quality and up-to-date terrain data for extended areas often characterised by difficult accessibility and large elevation ranges.

Over the last two decades, airborne laser scanning (ALS) has replaced photogrammetric approaches as state-of-the-art technology for the acquisition of high-resolution DTMs also in alpine environments. Reasons include the higher productivity compared to (manual) stereo-photogrammetric measurements, canopy-penetration capability, and limitations of photo measurements on sparsely textured surfaces like snow or ice. Nevertheless, the last few years have shown strong technological advances in the field of aerial camera technology, image processing and photogrammetric software which led to new possibilities for image-based DTM generation even in alpine terrain. At Vermessung AVT, an Austrian-based surveying company, and its subsidiary Terra Messflug, very promising results have been achieved for various projects in high-alpine environments, using images acquired by large-format digital cameras of Microsoft’s UltraCam series and the in-house processing chain centred on the Dense-Image-Matching (DIM) software SURE by nFrames.

 This paper reports the work carried out at AVT for the surface- and terrain modelling of several high-alpine areas using DIM- and ALS-based approaches. A special focus is dedicated to the influence of terrain morphology, flight planning, GNSS/IMU measurements, and ground-control distribution in the georeferencing process on the data quality. Based on the very promising results, some general recommendations for aerial photogrammetry processing in high-alpine areas are made to achieve best possible accuracy of the final 3D-, 2.5D- and 2D products.
NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION
Authors: Sánchez N. , Lopez-Sanchez J. M. , Arias-Pérez B. , Valcarce-Diñeiro R. , Martínez-Fernández J. , Calvo-Heras J. M. , Camps A. , González-Zamora A. , Vicente-Guijalba F.
Published: August 2016

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.
INTEGRATION OF A GENERALISED BUILDING MODEL INTO THE POSE ESTIMATION OF UAS IMAGES
Authors: Unger J. , Rottensteiner F. , Heipke C.
Published: August 2016

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.
METHODOLOGY FOR DETERMINING OPTIMAL EXPOSURE PARAMETERS OF A HYPERSPECTRAL SCANNING SENSOR
Authors: Walczykowski P. , Siok K. , Jenerowicz A.
Published: August 2016

The purpose of the presented research was to establish a methodology that would allow the registration of hyperspectral images with a defined spatial resolution on a horizontal plane. The results obtained within this research could then be used to establish the optimum sensor and flight parameters for collecting aerial imagery data using an UAV or other aerial system. The methodology is based on an user-selected optimal camera exposure parameters (i.e. time, gain value) and flight parameters (i.e. altitude, velocity). A push-broom hyperspectral imager- the Headwall MicroHyperspec A-series VNIR was used to conduct this research. The measurement station consisted of the following equipment: a hyperspectral camera MicroHyperspec A-series VNIR, a personal computer with HyperSpec III software, a slider system which guaranteed the stable motion of the sensor system, a white reference panel and a Siemens star, which was used to evaluate the spatial resolution.

 Hyperspectral images were recorded at different distances between the sensor and the target- from 5m to 100m. During the registration process of each acquired image, many exposure parameters were changed, such as: the aperture value, exposure time and speed of the camera’s movement on the slider. Based on all of the registered hyperspectral images, some dependencies between chosen parameters had been developed:

 - the Ground Sampling Distance – GSD and the distance between the sensor and the target,

 - the speed of the camera and the distance between the sensor and the target,

 - the exposure time and the gain value,

 - the Density Number and the gain value.

 The developed methodology allowed us to determine the speed and the altitude of an unmanned aerial vehicle on which the sensor would be mounted, ensuring that the registered hyperspectral images have the required spatial resolution.
QUALITY ANALYSIS ON 3D BUIDLING MODELS RECONSTRUCTED FROM UAV IMAGERY
Authors: Jarzabek-Rychard M. , Karpina M.
Published: August 2016

Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.
UAV SURVEYING FOR A COMPLETE MAPPING AND DOCUMENTATION OF ARCHAEOLOGICAL FINDINGS. THE EARLY NEOLITHIC SITE OF PORTONOVO
Authors: Malinverni E. S. , Conati Barbaro C. , Pierdicca R. , Bozzi C. A. , Tassetti A. N.
Published: August 2016

The huge potential of 3D digital acquisition techniques for the documentation of archaeological sites, as well as the related findings, is almost well established. In spite of the variety of available techniques, a sole documentation pipeline cannot be defined a priori because of the diversity of archaeological settings. Stratigraphic archaeological excavations, for example, require a systematic, quick and low cost 3D single-surface documentation because the nature of stratigraphic archaeology compels providing documentary evidence of any excavation phase. Only within a destructive process each single excavation cannot be identified, documented and interpreted and this implies the necessity of a re- examination of the work on field. In this context, this paper describes the methodology, carried out during the last years, to 3D document the Early Neolithic site of Portonovo (Ancona, Italy) and, in particular, its latest step consisting in a photogrammetric aerial survey by means of UAV platform. It completes the previous research delivered in the same site by means of terrestrial laser scanning and close range techniques and sets out different options for further reflection in terms of site coverage, resolution and campaign cost. With the support of a topographic network and a unique reference system, the full documentation of the site is managed in order to detail each excavation phase; besides, the final output proves how the 3D digital methodology can be completely integrated with reasonable costs during the excavation and used to interpret the archaeological context. Further contribution of this work is the comparison between several acquisition techniques (i.e. terrestrial and aerial), which could be useful as decision support system for different archaeological scenarios. The main objectives of the comparison are: i) the evaluation of 3D mapping accuracy from different data sources, ii) the definition of a standard pipeline for different archaeological needs and iii) the provision of different level of detail according to the user needs.
A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA
Authors: Tang Dejin , Zhou Xiaoming , Jiang Jie , Li Caiping
Published: August 2016

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.
MULTI-CRITERIA GIS ANALYSES WITH THE USE OF UAVS FOR THE NEEDS OF SPATIAL PLANNING
Authors: Zawieska D. , Markiewicz J. , Turek A. , Bakuła K. , Kowalczyk M. , Kurczyński Z. , Ostrowski W. , Podlasiak P.
Published: August 2016

Utilization of Unmanned Aerial Systems (UAVs) in agriculture, forestry, or other environmental contexts has recently become common. However, in the case of spatial planning, the role of UAVs still seems to be underestimated. At present, sections of municipal development use UAVs mainly for promotional purposes (films, folders, brochures, etc.). The use of UAVs for spatial management provides results, first of all, in the form of savings in human resources and time; however, more frequently, it is also connected with financial savings (given the decreasing cost of UAVs and photogrammetric software). The performed research presented here relates to the possibilities of using UAVs to update planning documents, and, in particular, to update the study of conditions and directions of spatial management and preparation of local plans for physical management. Based on acquired photographs with a resolution of 3 cm, a cloud of points is generated, as well as 3D models and the true orthophotomap. These data allow multi-criteria spatial analyses. Additionally, directions of development and changes in physical management are analysed for the given area.
HORIZONTAL POSITION OPTIMAL SOLUTION DETERMINATION FOR THE SATELLITE LASER RANGING SLOPE MODEL
Authors: Wang Yu , Ai Yu , Hu Yu , Wang RenLi
Published: August 2016

According to the Gaussian-fit laser echo model and the terrain slope model, the regular mean value theorem and the asymptotic principle of the median point of the double integral mean value theorem are used to derive the optimal solution for the horizontal position of a single-mode laser echo. Through simulation experiments, the horizontal position results of the echo signal peak from various terrain slopes are analyzed. When ignoring the effect of the atmosphere and the surface roughness of the target, considering the geometric position of the Gaussian single-mode echo signal peak to be the center of the laser spot is highly accurate. However, as the accuracy significantly decreases when the slope is greater than 26°, making the range of the peak value of the single-mode echo data (for a slope of less than 26°) to be the range of the geometrical center of the laser spot can obtain a higher degree of accuracy.