bluuebunny/crossref_metadata_2025 huggingface dataset:
LOW FREQUENCY ERROR ANALYSIS AND CALIBRATION FOR HIGH-RESOLUTION OPTICAL SATELLITE’S UNCONTROLLED GEOMETRIC POSITIONING
Authors: Wang Mi , Fang Chengcheng , Yang Bo , Cheng Yufeng
Published: August 2016

The low frequency error is a key factor which has affected uncontrolled geometry processing accuracy of the high-resolution optical image. To guarantee the geometric quality of imagery, this paper presents an on-orbit calibration method for the low frequency error based on geometric calibration field. Firstly, we introduce the overall flow of low frequency error on-orbit analysis and calibration, which includes optical axis angle variation detection of star sensor, relative calibration among star sensors, multi-star sensor information fusion, low frequency error model construction and verification. Secondly, we use optical axis angle change detection method to analyze the law of low frequency error variation. Thirdly, we respectively use the method of relative calibration and information fusion among star sensors to realize the datum unity and high precision attitude output. Finally, we realize the low frequency error model construction and optimal estimation of model parameters based on DEM/DOM of geometric calibration field. To evaluate the performance of the proposed calibration method, a certain type satellite’s real data is used. Test results demonstrate that the calibration model in this paper can well describe the law of the low frequency error variation. The uncontrolled geometric positioning accuracy of the high-resolution optical image in the WGS-84 Coordinate Systems is obviously improved after the step-wise calibration.
RESEARCH ON REEF BATHYMETRIC SURVEY OF UAV STEREOPAIR BASED ON TWO-MEDIUM PHOTOGRAMMETRY
Authors: Ye Dongmei , Liao Ming , Nan Ao , Wang Enquan , Zhou Gaowei
Published: August 2016

This paper is based on the principles of two-medium photogrammetry, with the purpose to perform a bathymetric survey of a reef in the South China Sea, using aerial imagery acquired by UAV. The first objectives are to introduce the basic principles of two-medium photogrammetry, discuss the technical requirements of this methodology to determine an accurate refractive index of sea water, and propose a new method to calculate seawater refraction and calculate corrected reef elevations. The second objective is to analyse and integrate the elevation and depth datum for both the land mass and the undersea reef. The final objective of this paper is performing stereoscopic mensuration on the UAV photography in order to transform reef elevation and depth datum. Our test shows that aerial two-medium photogrammetry is feasible in practical application, but requires relatively high aerial photography conditions.
WILD FIRE RISK MAP IN THE EASTERN STEPPE OF MONGOLIA USING SPATIAL MULTI-CRITERIA ANALYSIS
Authors: Nasanbat Elbegjargal , Lkhamjav Ochirkhuyag
Published: August 2016

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.
SIMULATION OF FULL-WAVEFORM LASER ALTIMETER ECHOWAVEFORM
Authors: Lv Y. , Tong X. H. , Liu S. J. , Xie H. , Luan K. F. , Liu J.
Published: August 2016

Change of globe surface height is an important factor to study human living environment. The Geoscience Laser Altimeter System (GLAS) on ICESat is the first laser-ranging instrument for continuous global observations of the Earth. In order to have a comprehensive understanding of full-waveform laser altimeter, this study simulated the operating mode of ICESat and modeled different terrains’ (platform terrain, slope terrain, and artificial terrain) echo waveforms based on the radar equation. By changing the characteristics of the system and the targets, numerical echo waveforms can be achieved. Hereafter, we mainly discussed the factors affecting the amplitude and size (width) of the echoes. The experimental results implied that the slope of the terrain, backscattering coefficient and reflectivity, target height, target position in the footprint and area reacted with the pulse all can affect the energy distribution of the echo waveform and the receiving time. Finally, Gaussian decomposition is utilized to decompose the echo waveform. From the experiment, it can be noted that the factors which can affect the echo waveform and by this way we can know more about large footprint full-waveform satellite laser altimeter.
ASSESSMENT OF THE GEOMETRIC QUALITY OF SENTINEL-2 DATA
Authors: Pandžic M. , Mihajlovic D. , Pandžic J. , Pfeifer N.
Published: August 2016

High resolution (10 m and 20 m) optical imagery satellite Sentinel-2 brings a new perspective to Earth observation. Its frequent revisit time enables monitoring the Earth surface with high reliability. Since Sentinel-2 data is provided free of charge by the European Space Agency, its mass use for variety of purposes is expected. Quality evaluation of Sentinel-2 data is thus necessary. Quality analysis in this experiment is based on comparison of Sentinel-2 imagery with reference data (orthophoto). From the possible set of features to compare (point features, texture lines, objects, etc.) line segments were chosen because visual analysis suggested that scale differences matter least for these features. The experiment was thus designed to compare long line segments (e.g. airstrips, roads, etc.) in both datasets as the most representative entities. Edge detection was applied to both images and corresponding edges were manually selected. The statistical parameter which describes the geometrical relation between different images (and between datasets in general) covering the same area is calculated as the distance between corresponding curves in two datasets. The experiment was conducted for two different test sites, Austria and Serbia. From 21 lines with a total length of ca. 120 km the average offset of 6.031 m (0.60 pixel of Sentinel-2) was obtained for Austria, whereas for Serbia the average offset of 12.720 m (1.27 pixel of Sentinel-2) was obtained out of 10 lines with a total length of ca. 38 km.
PRELIMINARY ANALYSES OF BEIDOU SIGNAL-IN-SPACE ANOMALY SINCE 2013
Authors: Wu Y. , Ren J. , Liu W.
Published: August 2016

As BeiDou navigation system has been operational since December 2012. There is an increasing desire to use multiple constellation to improve positioning performance. The signal-in-space (SIS) anomaly caused by the ground control and the space vehicle is one of the major threats to affect the integrity. For a young Global Navigation Satellite System, knowledge about SIS anomalies in history is very important for not only assessing the SIS integrity performance of a constellation but also providing the assumption for ARAIM (Advanced Receiver Autonomous Integrity Monitoring).

 In this paper, the broadcast ephemerides and the precise ones are pre-processed for avoiding the false anomaly identification. The SIS errors over the period of Mar. 2013-Feb. 2016 are computed by comparing the broadcast ephemerides with the precise ones. The time offsets between GPST (GPS time) and BDT (BeiDou time) are estimated and removed by an improved estimation algorithm. SIS worst-UREs are computed and a RMS criteria are investigated to identify the SIS anomalies. The results show that the probability of BeiDou SIS anomalies is in 10-3 level in last three years. Even though BeiDou SIS integrity performance currently cannot match the GPS integrity performances, the result indicates that BeiDou has a tendency to improve its integrity performance.
A SYSTEMATIC COMPARISON OF DIRECT AND IMAGE-BASED GEOREFERENCING IN CHALLENGING URBAN AREAS
Authors: Cavegn S. , Nebiker S. , Haala N.
Published: August 2016

Image-based mobile mapping systems enable an efficient acquisition of georeferenced image sequences, which can be used for geo-data capture in subsequent steps. In order to provide accurate measurements in a given reference frame while e.g. aiming at high fidelity 3D urban models, high quality georeferencing of the captured multi-view image sequences is required. Moreover, sub-pixel accurate orientations of these highly redundant image sequences are needed in order to optimally perform steps like dense multi-image matching as a prerequisite for 3D point cloud and mesh generation. While direct georeferencing of image-based mobile mapping data performs well in open areas, poor GNSS coverage in urban canyons aggravates fulfilling these high accuracy requirements, even with high-grade inertial navigation equipment. Hence, we conducted comprehensive investigations aiming at assessing the quality of directly georeferenced sensor orientations as well as the expected improvement by image-based georeferencing in a challenging urban environment. Our study repeatedly delivered mean trajectory deviations of up to 80 cm. By performing image-based georeferencing using bundle adjustment for a limited set of cameras and a limited number of ground control points, mean check point residuals could be lowered from approx. 40 cm to 4 cm. Furthermore, we showed that largely automated image-based georeferencing is capable of detecting and compensating discontinuities in directly georeferenced trajectories.
A UNIVERSAL DE-NOISING ALGORITHM FOR GROUND-BASED LIDAR SIGNAL
Authors: Ma Xin , Xiang Chengzhi , Gong Wei
Published: August 2016

Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.
A METHOD OF GENERATING PANORAMIC STREET STRIP IMAGE MAP WITH MOBILE MAPPING SYSTEM
Authors: Tianen Chen , Yamamoto Kohei , Tachibana Kikuo
Published: August 2016

This paper explores a method of generating panoramic street strip image map which is called as “Pano-Street” here and contains both sides, ground surface and overhead part of a street with a sequence of 360° panoramic images captured with Point Grey’s Ladybug3 mounted on the top of Mitsubishi MMS-X 220 at 2m intervals along the streets in urban environment. On-board GPS/IMU, speedometer and post sequence image analysis technology such as bundle adjustment provided much more accuracy level position and attitude data for these panoramic images, and laser data. The principle for generating panoramic street strip image map is similar to that of the traditional aero ortho-images. A special 3D DEM(3D-Mesh called here) was firstly generated with laser data, the depth map generated from dense image matching with the sequence of 360° panoramic images, or the existing GIS spatial data along the MMS trajectory, then all 360° panoramic images were projected and stitched on the 3D-Mesh with the position and attitude data. This makes it possible to make large scale panoramic street strip image maps for most types of cities, and provides another kind of street view way to view the 360° scene along the street by avoiding the switch of image bubbles like Google Street View and Bing Maps Streetside.
A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS
Authors: Chen Xinqu , Li Jonathan
Published: August 2016

This study aims to automatically detect pavement cracks on urban roads by employing the 3D point clouds acquired by a mobile laser scanning (MLS) system. Our method consists of four steps: ground point filtering, high-pass convolution, matched filtering, and noise removal. First, a voxel-based upward growing method is applied to construct Digital Terrain Model (DTM) of the road surface. Then, a high-pass filter convolutes the DTM to detect local elevation changes that may embed cracking information. Next, a two-step matched filter is applied to extract crack features. Lastly, a noise removal process is conducted to refine the results. Instead of using MLS intensity, this study takes advantages of the MLS elevation information to perform automated crack detection from large-volume, mixed-density, unstructured MLS point clouds. Four types of cracks including longitudinal, transvers, random, and alligator cracks are detected. Our results demonstrated that the proposed method works well with the RIEGL VMX-450 point clouds and can detect cracks in moderate-to-severe severity (13 - 25 mm) within a 200 m by 30 m urban road segment located in Kingston, Ontario, at one time. Due to the resolution capability, small cracks with slight severity remain unclear in the MLS point cloud.
POLE PHOTOGRAMMETRY WITH AN ACTION CAMERA FOR FAST AND ACCURATE SURFACE MAPPING
Authors: Gonçalves J. A. , Moutinho O. F. , Rodrigues A. C.
Published: August 2016

High resolution and high accuracy terrain mapping can provide height change detection for studies of erosion, subsidence or land slip. A UAV flying at a low altitude above the ground, with a compact camera, acquires images with resolution appropriate for these change detections. However, there may be situations where different approaches may be needed, either because higher resolution is required or the operation of a drone is not possible. Pole photogrammetry, where a camera is mounted on a pole, pointing to the ground, is an alternative. This paper describes a very simple system of this kind, created for topographic change detection, based on an action camera. These cameras have high quality and very flexible image capture. Although radial distortion is normally high, it can be treated in an auto-calibration process. The system is composed by a light aluminium pole, 4 meters long, with a 12 megapixel GoPro camera. Average ground sampling distance at the image centre is 2.3 mm. The user moves along a path, taking successive photos, with a time lapse of 0.5 or 1 second, and adjusting the speed in order to have an appropriate overlap, with enough redundancy for 3D coordinate extraction. Marked ground control points are surveyed with GNSS for precise georeferencing of the DSM and orthoimage that are created by structure from motion processing software. An average vertical accuracy of 1 cm could be achieved, which is enough for many applications, for example for soil erosion. The GNSS survey in RTK mode with permanent stations is now very fast (5 seconds per point), which results, together with the image collection, in a very fast field work. If an improved accuracy is needed, since image resolution is 1/4 cm, it can be achieved using a total station for the control point survey, although the field work time increases.
EVALUATION OF DRIVER VISIBILITY FROM MOBILE LIDAR DATA AND WEATHER CONDITIONS
Authors: González-Jorge H. , Díaz-Vilariño L. , Lorenzo H. , Arias P.
Published: August 2016

Visibility of drivers is crucial to ensure road safety. Visibility is influenced by two main factors, the geometry of the road and the weather present therein. The present work depicts an approach for automatic visibility evaluation using mobile LiDAR data and climate information provided from weather stations located in the neighbourhood of the road. The methodology is based on a ray-tracing algorithm to detect occlusions from point clouds with the purpose of identifying the visibility area from each driver position. The resulting data are normalized with the climate information to provide a polyline with an accurate area of visibility. Visibility ranges from 25 m (heavy fog) to more than 10,000 m (clean atmosphere). Values over 250 m are not taken into account for road safety purposes, since this value corresponds to the maximum braking distance of a vehicle. Two case studies are evaluated an urban road in the city of Vigo (Spain) and an inter-urban road between the city of Ourense and the village of Castro Caldelas (Spain). In both cases, data from the Galician Weather Agency (Meteogalicia) are used. The algorithm shows promising results allowing the detection of particularly dangerous areas from the viewpoint of driver visibility. The mountain road between Ourense and Castro Caldelas, with great presence of slopes and sharp curves, shows special interest for this type of application. In this case, poor visibility can especially contribute to the run over of pedestrians or cyclists traveling on the road shoulders.
AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY
Authors: Hussnain Zille , Oude Elberink Sander , Vosselman George
Published: August 2016

In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.
INITIAL TESTS AND ACCURACY ASSESMENT OF A COMPACT MOBILE LASER SCANNING SYSTEM
Authors: Julge K. , Ellmann A. , Vajakas T. , Kolka R.
Published: August 2016

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

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.
PHOTOGRAMMETRIC RECONSTRUCTION WITH BAYESIAN INFORMATION
Authors: Masiero A. , Fissore F. , Guarnieri A. , Pirotti F. , Vettore A.
Published: August 2016

Nowadays photogrammetry and laser scanning methods are the most wide spread surveying techniques. Laser scanning methods usually allow to obtain more accurate results with respect to photogrammetry, but their use have some issues, e.g. related to the high cost of the instrumentation and the typical need of high qualified personnel to acquire experimental data on the field. Differently, photogrammetric reconstruction can be achieved by means of low cost devices and by persons without specific training. Furthermore, the recent diffusion of smart devices (e.g. smartphones) embedded with imaging and positioning sensors (i.e. standard camera, GNSS receiver, inertial measurement unit) is opening the possibility of integrating more information in the photogrammetric reconstruction procedure, in order to increase its computational efficiency, its robustness and accuracy. In accordance with the above observations, this paper examines and validates new possibilities for the integration of information provided by the inertial measurement unit (IMU) into the photogrammetric reconstruction procedure, and, to be more specific, into the procedure for solving the feature matching and the bundle adjustment problems.
COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES
Authors: Miraliakbari A. , Sok S. , Ouma Y. O. , Hahn M.
Published: August 2016

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.
SYSTEMATIC CALIBRATION FOR A BACKPACKED SPHERICAL PHOTOGRAMMETRY IMAGING SYSTEM
Authors: Rau J. Y. , Su B. W. , Hsiao K. W. , Jhan J. P.
Published: August 2016

A spherical camera can observe the environment for almost 720 degrees’ field of view in one shoot, which is useful for augmented reality, environment documentation, or mobile mapping applications. This paper aims to develop a spherical photogrammetry imaging system for the purpose of 3D measurement through a backpacked mobile mapping system (MMS). The used equipment contains a Ladybug-5 spherical camera, a tactical grade positioning and orientation system (POS), i.e. SPAN-CPT, and an odometer, etc. This research aims to directly apply photogrammetric space intersection technique for 3D mapping from a spherical image stereo-pair. For this purpose, several systematic calibration procedures are required, including lens distortion calibration, relative orientation calibration, boresight calibration for direct georeferencing, and spherical image calibration. The lens distortion is serious on the ladybug-5 camera’s original 6 images. Meanwhile, for spherical image mosaicking from these original 6 images, we propose the use of their relative orientation and correct their lens distortion at the same time. However, the constructed spherical image still contains systematic error, which will reduce the 3D measurement accuracy. Later for direct georeferencing purpose, we need to establish a ground control field for boresight/lever-arm calibration. Then, we can apply the calibrated parameters to obtain the exterior orientation parameters (EOPs) of all spherical images. In the end, the 3D positioning accuracy after space intersection will be evaluated, including EOPs obtained by structure from motion method.
3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS
Authors: Zou Xiaoliang , Zhao Guihua , Li Jonathan , Yang Yuanxi , Fang Yong
Published: August 2016

Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.
RESEARCH ON THE KEY TECHNOLOGY OF LARGE SCALE MAPPING FROM LOW ALTITUDE PHOTOGRAMMETRY
Authors: Bo-Yi Wu , Ning Zhang , Guo-Zhong Su
Published: August 2016

Based on the theoretic analysis of the accuracy in large scale photogrammetric mapping, some defects in traditional procedure were discussed. A set of key technologies dedicate to accuracy improvement in low altitude photogrammetry were analyzed in detail, namely the utilization of wide angle camera and low altitude flight, enhancement in image matching, predesigned layout of Ground Control Points (GCPs) in field survey, optimization of adjustment model and improvement in map processing. Besides, a low altitude aerial unmanned airship system was established. Finally, successful implementation in 1:500 topographic mapping project in built-up areas of 30 counties in Shanxi Province proves the practicability and effectiveness of the proposed approaches.
EXPERIENCES OF UAV SURVEYS APPLIED TO ENVIRONMENTAL RISK MANAGEMENT
Authors: Caprioli M. , Trizzino R. , Mazzone F. , Scarano M.
Published: August 2016

In this paper the results of some surveys carried out in an area of Apulian territory affected by serious environmental hazard are presented. Unmanned Aerial Vehicles (UAV) are emerging as a key engineering tool for future environmental survey tasks. UAVs are increasingly seen as an attractive low-cost alternative or supplement to aerial and terrestrial photogrammetry due to their low cost, flexibility, availability and readiness for duty. In addition, UAVs can be operated in hazardous or temporarily inaccessible locations, that makes them very suitable for the assessment and management of environmental risk conditions. In order to verify the reliability of these technologies an UAV survey and A LIDAR survey have been carried outalong about 1 km of coast in the Salento peninsula, near the towns of San Foca, Torre dellOrso and SantAndrea( Lecce, Southern Italy). This area is affected by serious environmental risks due to the presence of dangerous rocky cliffs named falesie. The UAV platform was equipped with a photogrammetric measurement system that allowed us to obtain a mobile mapping of the fractured fronts of dangerous rocky cliffs. UAV-images data have been processed using dedicated software (AgisoftPhotoscan). The point clouds obtained from both the UAV and LIDAR surveys have been processed using Cloud Compare software, with the aim of testing the UAV results with respect to the LIDAR ones. The total error obtained was of centimeter-order that is a very satisfactory result. The environmental information has been arranged in an ArcGIS platform in order to assess the risk levels. The possibility to repeat the survey at time intervals more or less close together depending on the measured levels of risk and to compare the output allows following the trend of the dangerous phenomena. In conclusion, for inaccessible locations of dangerous rocky bodies the UAV survey coupled with GIS methodology proved to be a key engineering tool for the management of environmental risks.
INVESTIGATIONS ON THE POSSIBILITIES OF MONITORING COASTAL CHANGES INCLUDING SHALLOW UNDER WATER AREAS WITH UAS PHOTO BATHMETRY
Authors: Grenzdörffer G. J. , Naumann M.
Published: August 2016

UAS become a very valuable tool for coastal morphology. Not only for mapping but also for change detection and a better understanding of processes along and across the shore. This contribution investigates the possibilities of UAS to determine the water depth in clear shallow waters by means of the so called "photo bathymetry". From the results of several test flights it became clear that three factors influence the ability and the accuracy of bathymetric sea floor measurements. Firstly, weather conditions. Sunny weather is not always good. Due to the high image resolution the sunlight gets focussed even in very small waves causing moving patterns on shallow grounds with high reflection properties, such as sand. This effect invisible under overcast weather conditions. Waves, may also introduce problems and mismatches. Secondly the quality and the accuracy of the georeferencing with SFM algorithms. As multi image key point matching will not work over water, the proposed approach will only work for projects closely to the coastline with enough control on the land. Thirdly the software used and the intensity of post processing and filtering. Refraction correction and the final interpolation of the point cloud into a DTM are the last steps. If everything is done appropriately, accuracies in the bathymetry in the range of 10 – 50 cm, depending on the water depth are possible.
CHOSEN ASPECTS OF THE PRODUCTION OF THE BASIC MAP USING UAV IMAGERY
Authors: Kedzierski M. , Fryskowska A. , Wierzbicki D. , Nerc P.
Published: August 2016

For several years there has been an increasing interest in the use of unmanned aerial vehicles in acquiring image data from a low altitude. Considering the cost-effectiveness of the flight time of UAVs vs. conventional airplanes, the use of the former is advantageous when generating large scale accurate ortophotos. Through the development of UAV imagery, we can update large-scale basic maps. These maps are cartographic products which are used for registration, economic, and strategic planning. On the basis of these maps other cartographic maps are produced, for example maps used building planning. The article presents an assessesment of the usefulness of orthophotos based on UAV imagery to upgrade the basic map. In the research a compact, non-metric camera, mounted on a fixed wing powered by an electric motor was used. The tested area covered flat, agricultural and woodland terrains. The processing and analysis of orthorectification were carried out with the INPHO UASMaster programme. Due to the effect of UAV instability on low-altitude imagery, the use of non-metric digital cameras and the low-accuracy GPS-INS sensors, the geometry of images is visibly lower were compared to conventional digital aerial photos (large values of phi and kappa angles). Therefore, typically, low-altitude images require large along- and across-track direction overlap – usually above 70 %. As a result of the research orthoimages were obtained with a resolution of 0.06 meters and a horizontal accuracy of 0.10m. Digitized basic maps were used as the reference data. The accuracy of orthoimages vs. basic maps was estimated based on the study and on the available reference sources. As a result, it was found that the geometric accuracy and interpretative advantages of the final orthoimages allow the updating of basic maps. It is estimated that such an update of basic maps based on UAV imagery reduces processing time by approx. 40%.
ROBUST MOSAICKING OF UAV IMAGES WITH NARROW OVERLAPS
Authors: Kim J. , Kim T. , Shin D. , Kim S. H.
Published: August 2016

This paper considers fast and robust mosaicking of UAV images under a circumstance that each UAV images have very narrow overlaps in-between. Image transformation for image mosaicking consists of two estimations: relative transformations and global transformations. For estimating relative transformations between adjacent images, projective transformation is widely considered. For estimating global transformations, panoramic constraint is widely used. While perspective transformation is a general transformation model in 2D-2D transformation, this may not be optimal with weak stereo geometry such as images with narrow overlaps. While panoramic constraint works for reliable conversion of global transformation for panoramic image generation, this constraint is not applicable to UAV images in linear motions. For these reasons, a robust approach is investigated to generate a high quality mosaicked image from narrowly overlapped UAV images. For relative transformations, several transformation models were considered to ensure robust estimation of relative transformation relationship. Among them were perspective transformation, affine transformation, coplanar relative orientation, and relative orientation with reduced adjustment parameters. Performance evaluation for each transformation model was carried out. The experiment results showed that affine transformation and adjusted coplanar relative orientation were superior to others in terms of stability and accuracy. For global transformation, we set initial approximation by converting each relative transformation to a common transformation with respect to a reference image. In future work, we will investigate constrained relative orientation for enhancing geometric accuracy of image mosaicking and bundle adjustments of each relative transformation model for optimal global transformation.
Augmenting ViSP’s 3D Model-Based Tracker with RGB-D SLAM for 3D Pose Estimation in Indoor Environments
Authors: Li-Chee-Ming J. , Armenakis C.
Published: August 2016

This paper presents a novel application of the Visual Servoing Platform’s (ViSP) for pose estimation in indoor and GPS-denied outdoor environments. Our proposed solution integrates the trajectory solution from RGBD-SLAM into ViSP’s pose estimation process. Li-Chee-Ming and Armenakis (2015) explored the application of ViSP in mapping large outdoor environments, and tracking larger objects (i.e., building models). Their experiments revealed that tracking was often lost due to a lack of model features in the camera’s field of view, and also because of rapid camera motion. Further, the pose estimate was often biased due to incorrect feature matches. This work proposes a solution to improve ViSP’s pose estimation performance, aiming specifically to reduce the frequency of tracking losses and reduce the biases present in the pose estimate. This paper explores the integration of ViSP with RGB-D SLAM. We discuss the performance of the combined tracker in mapping indoor environments and tracking 3D wireframe indoor building models, and present preliminary results from our experiments.
GEOMETRIC QUALITY ASSESSMENT OF LIDAR DATA BASED ON SWATH OVERLAP
Authors: Sampath A. , Heidemann H. K. , Stensaas G. L.
Published: August 2016

This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the inter-swath errors, including the presence of systematic errors in lidar data. Hence they pose a risk to the user in terms of data acceptance (i.e. a higher potential for Type II error indicating risk of accepting potentially unsuitable data). For example, if the overlap area is too small or if the sampled locations are close to the center of overlap, or if the errors are sampled in flat regions when there are residual pitch errors in the data, the resultant Root Mean Square Differences (RMSD) can still be small. To avoid this, the following are suggested to be used as criteria for defining the inter-swath quality of data:

 a) Median Discrepancy Angle

 b) Mean and RMSD of Horizontal Errors using DQM measured on sloping surfaces

 c) RMSD for sampled locations from flat areas (defined as areas with less than 5 degrees of slope)

 It is suggested that 4000-5000 points are uniformly sampled in the overlapping regions of the point cloud, and depending on the surface roughness, to measure the discrepancy between swaths. Care must be taken to sample only areas of single return points only. Point-to-Plane distance based data quality measures are determined for each sample point. These measurements are used to determine the above mentioned parameters. This paper details the measurements and analysis of measurements required to determine these metrics, i.e. Discrepancy Angle, Mean and RMSD of errors in flat regions and horizontal errors obtained using measurements extracted from sloping regions (slope greater than 10 degrees). The research is a result of an ad-hoc joint working group of the US Geological Survey and the American Society for Photogrammetry and Remote Sensing (ASPRS) Airborne Lidar Committee.
TOWARD REAL TIME UAVS’ IMAGE MOSAICKING
Authors: Mehrdad S. , Satari M. , Safdary M. , Moallem P.
Published: August 2016

Anyone knows that sudden catastrophes can instantly do great damage. Fast and accurate acquisition of catastrophe information is an essential task for minimize life and property damage. Compared with other ways of catastrophe data acquisition, UAV based platforms can optimize time, cost and accuracy of the data acquisition, as a result UAVs’ data has become the first choice in such condition. In this paper, a novel and fast strategy is proposed for registering and mosaicking of UAVs’ image data. Firstly, imprecise image positions are used to find adjoining frames. Then matching process is done by a novel matching method. With keeping Sift in mind, this fast matching method is introduced, which uses images exposure time geometry, SIFT point detector and rBRIEF descriptor vector in order to match points efficiency, and by efficiency we mean not only time efficiency but also elimination of mismatch points. This method uses each image sequence imprecise attitude in order to use Epipolar geometry to both restricting search space of matching and eliminating mismatch points. In consideration of reaching to images imprecise attitude and positions we calibrated the UAV’s sensors. After matching process, RANSAC is used to eliminate mismatched tie points. In order to obtain final mosaic, image histograms are equalized and a weighted average method is used to image composition in overlapping areas. The total RMSE over all matching points is 1.72 m.
CLASSICAL PHOTOGRAMMETRY AND UAV – SELECTED ASCPECTS
Authors: Mikrut S.
Published: August 2016

The UAV technology seems to be highly future-oriented due to its low costs as compared to traditional aerial images taken from classical photogrammetry aircrafts. The AGH University of Science and Technology in Cracow - Department of Geoinformation, Photogrammetry and Environmental Remote Sensing focuses mainly on geometry and radiometry of recorded images. Various scientific research centres all over the world have been conducting the relevant research for years. The paper presents selected aspects of processing digital images made with the UAV technology. It provides on a practical example a comparison between a digital image taken from an airborne (classical) height, and the one made from an UAV level. In his research the author of the paper is trying to find an answer to the question: to what extent does the UAV technology diverge today from classical photogrammetry, and what are the advantages and disadvantages of both methods? The flight plan was made over the Tokarnia Village Museum (more than 0.5 km2) for two separate flights: the first was made by an UAV - System FT-03A built by FlyTech Solution Ltd. The second was made with the use of a classical photogrammetric Cesna aircraft furnished with an airborne photogrammetric camera (Ultra Cam Eagle). Both sets of photographs were taken with pixel size of about 3 cm, in order to have reliable data allowing for both systems to be compared. The project has made aerotriangulation independently for the two flights. The DTM was generated automatically, and the last step was the generation of an orthophoto. The geometry of images was checked under the process of aerotriangulation. To compare the accuracy of these two flights, control and check points were used. RMSE were calculated. The radiometry was checked by a visual method and using the author's own algorithm for feature extraction (to define edges with subpixel accuracy). After initial pre-processing of data, the images were put together, and shown side by side. Buildings and strips on the road were selected from whole data for the comparison of edges and details. The details on UAV images were not worse than those on classical photogrammetric ones. One might suppose that geometrically they also were correct. The results of aerotriangulation prove these facts, too. Final results from aerotriangulation were on the level of RMS = 1 pixel (about 3 cm). In general it can be said that photographs from UAVs are not worse than classic ones. In the author's opinion, geometric and radiometric qualities are at a similar level for this kind of area (a small village). This is a very significant result as regards mapping. It means that UAV data can be used in mapping production.