bluuebunny/crossref_metadata_2025 huggingface dataset:
EFFICIENT WIDE BASELINE STRUCTURE FROM MOTION
Authors: Michelini Mario , Mayer Helmut
Published: August 2016

This paper presents a Structure from Motion approach for complex unorganized image sets. To achieve high accuracy and robustness, image triplets are employed and (an approximate) camera calibration is assumed to be known. The focus lies on a complete linking of images even in case of large image distortions, e.g., caused by wide baselines, as well as weak baselines. A method for embedding image descriptors into Hamming space is proposed for fast image similarity ranking. The later is employed to limit the number of pairs to be matched by a wide baseline method. An iterative graph-based approach is proposed formulating image linking as the search for a terminal Steiner minimum tree in a line graph. Finally, additional links are determined and employed to improve the accuracy of the pose estimation. By this means, loops in long image sequences are implicitly closed. The potential of the proposed approach is demonstrated by results for several complex image sets also in comparison with VisualSFM.
LASER-BASED SLAM WITH EFFICIENT OCCUPANCY LIKELIHOOD MAP LEARNING FOR DYNAMIC INDOOR SCENES
Authors: Li Li , Yao Jian , Xie Renping , Tu Jinge , Feng Chen
Published: August 2016

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.
FIRST STEPS TOWARDS AN INTEGRATED CITYGML-BASED 3D MODEL OF VIENNA
Authors: Agugiaro G.
Published: August 2016

This paper presents and discusses the results regarding the initial steps (selection, analysis, preparation and eventual integration of a number of datasets) for the creation of an integrated, semantic, three-dimensional, and CityGML-based virtual model of the city of Vienna. CityGML is an international standard conceived specifically as information and data model for semantic city models at urban and territorial scale. It is being adopted by more and more cities all over the world.

 The work described in this paper is embedded within the European Marie-Curie ITN project “Ci-nergy, Smart cities with sustainable energy systems”, which aims, among the rest, at developing urban decision making and operational optimisation software tools to minimise non-renewable energy use in cities. Given the scope and scale of the project, it is therefore vital to set up a common, unique and spatio-semantically coherent urban model to be used as information hub for all applications being developed. This paper reports about the experiences done so far, it describes the test area and the available data sources, it shows and exemplifies the data integration issues, the strategies developed to solve them in order to obtain the integrated 3D city model. The first results as well as some comments about their quality and limitations are presented, together with the discussion regarding the next steps and some planned improvements.
VALIDATION OF "AW3D" GLOBAL DSM GENERATED FROM ALOS PRISM
Authors: Takaku Junichi , Tadono Takeo , Tsutsui Ken , Ichikawa Mayumi
Published: August 2016

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), one of onboard sensors carried by Advanced Land Observing Satellite (ALOS), was designed to generate worldwide topographic data with its optical stereoscopic observation. It has an exclusive ability to perform a triplet stereo observation which views forward, nadir, and backward along the satellite track in 2.5 m ground resolution, and collected its derived images all over the world during the mission life of the satellite from 2006 through 2011. A new project, which generates global elevation datasets with the image archives, was started in 2014. The data is processed in unprecedented 5 m grid spacing utilizing the original triplet stereo images in 2.5 m resolution. As the number of processed data is growing steadily so that the global land areas are almost covered, a trend of global data qualities became apparent. This paper reports on up-to-date results of the validations for the accuracy of data products as well as the status of data coverage in global areas. The accuracies and error characteristics of datasets are analyzed by the comparison with existing global datasets such as Ice, Cloud, and land Elevation Satellite (ICESat) data, as well as ground control points (GCPs) and the reference Digital Elevation Model (DEM) derived from the airborne Light Detection and Ranging (LiDAR).
VISUAL ANALYSIS BASED ON THE DATA OF CHINESE SURVEYING AND MAPPING JOURNALS
Authors: Li Jing , Liu Haiyan , Guo Wenyue , Yu Anzhu
Published: August 2016

Taking four influential Chinese surveying and mapping journals as the data source, 5863 papers published during the period of 2003–2013 were obtained. Using the method of bibliometrics and visual analysis, summarizing the surveying and mapping papers in the past ten years (2003-2013), research themes, authors, and geographical distribution were analyzed. In the study, the papers of geodesy, cartography and GIS are 59.9%, more than half of all the papers. We also determine that the core author group has 131 authors, mainly of whom are from big cities. 90% of top ten cities on the number of publishing papers are capital cities or municipalities directly under the central government.In conclusion, we found that the research focus was different every year, and the research content was richness, the content of geodesy, cartography and GIS were widely researched, and the development of surveying and mapping is imbalanced in China.
PREFACE
Authors: Remondino Fabio
Published: August 2016

No abstract available.
REVIEW OF ADVANCES IN COBB ANGLE CALCULATION AND IMAGE-BASED MODELLING TECHNIQUES FOR SPINAL DEFORMITIES
Authors: Giannoglou V. , Stylianidis E.
Published: August 2016

Scoliosis is a 3D deformity of the human spinal column that is caused from the bending of the latter, causing pain, aesthetic and respiratory problems. This internal deformation is reflected in the outer shape of the human back. The golden standard for diagnosis and monitoring of scoliosis is the Cobb angle, which refers to the internal curvature of the trunk. This work is the first part of a post-doctoral research, presenting the most important researches that have been done in the field of scoliosis, concerning its digital visualisation, in order to provide a more precise and robust identification and monitoring of scoliosis. The research is divided in four fields, namely, the X-ray processing, the automatic Cobb angle(s) calculation, the 3D modelling of the spine that provides a more accurate representation of the trunk and the reduction of X-ray radiation exposure throughout the monitoring of scoliosis. Despite the fact that many researchers have been working on the field for the last decade at least, there is no reliable and universal tool to automatically calculate the Cobb angle(s) and successfully perform proper 3D modelling of the spinal column that would assist a more accurate detection and monitoring of scoliosis.
SEE-THROUGH IMAGING OF LASER-SCANNED 3D CULTURAL HERITAGE OBJECTS BASED ON STOCHASTIC RENDERING OF LARGE-SCALE POINT CLOUDS
Authors: Tanaka S. , Hasegawa K. , Okamoto N. , Umegaki R. , Wang S. , Uemura M. , Okamoto A. , Koyamada K.
Published: August 2016

We propose a method for the precise 3D see-through imaging, or transparent visualization, of the large-scale and complex point clouds acquired via the laser scanning of 3D cultural heritage objects. Our method is based on a stochastic algorithm and directly uses the 3D points, which are acquired using a laser scanner, as the rendering primitives. This method achieves the correct depth feel without requiring depth sorting of the rendering primitives along the line of sight. Eliminating this need allows us to avoid long computation times when creating natural and precise 3D see-through views of laser-scanned cultural heritage objects. The opacity of each laser-scanned object is also flexibly controllable. For a laser-scanned point cloud consisting of more than 107 or 108 3D points, the pre-processing requires only a few minutes, and the rendering can be executed at interactive frame rates. Our method enables the creation of cumulative 3D see-through images of time-series laser-scanned data. It also offers the possibility of fused visualization for observing a laser-scanned object behind a transparent high-quality photographic image placed in the 3D scene. We demonstrate the effectiveness of our method by applying it to festival floats of high cultural value. These festival floats have complex outer and inner 3D structures and are suitable for see-through imaging.
MOTORIZED PANORAMIC CAMERA MOUNT – CALIBRATION AND IMAGE CAPTURE
Authors: Kauhanen H. , Rönnholm P. , Lehtola V. V.
Published: August 2016

interesting applications. Among the large variation of panoramic camera systems, we have focused on concentric panoramic imaging with a frame camera. In order to establish the concentric image acquisition, the camera mount must be calibrated so that the projection centre of the camera is located at the rotation centre of the mount. For this purpose, we developed a novel mount calibration method, which allows an accurate recovery of the rotation centre in two image acquisition steps. In addition, we have built a motorized camera mount that can self-calibrate the camera position within the mount, given the previously solved rotation centre, and then be used to automatically capture panoramic images. Hence, we have streamlined the previously laborious manual phase of iterative position calibration, but also automated the capturing of panoramic images. For validation purposes, reference results from a conventional manual mount are provided. In the case of non-motorized mount, the average distance between the projection centre of the camera and the rotation centre of the mount was 0.253 mm and the standard deviation was 0.161 mm. For the motorized mount, the corresponding average distance and standard deviation were 0.549 mm and 0.404 mm, respectively.
CLOSE-RANGE SENSING TECHNIQUES IN ALPINE TERRAIN
Authors: Rutzinger M. , Höfle B. , Lindenbergh R. , Oude Elberink S. , Pirotti F. , Sailer R. , Scaioni M. , Stötter J. , Wujanz D.
Published: August 2016

Early career researchers such as PhD students are a main driving force of scientific research and are for a large part responsible for research innovation. They work on specialized topics within focused research groups that have a limited number of members, but might also have limited capacity in terms of lab equipment. This poses a serious challenge for educating such students as it is difficult to group a sufficient number of them to enable efficient knowledge transfer. To overcome this problem, the Innsbruck Summer School of Alpine Research 2015 on close-range sensing techniques in Alpine terrain was organized in Obergurgl, Austria, by an international team from several universities and research centres. Of the applicants a group of 40 early career researchers were selected with interest in about ten types of specialized surveying tools, i.e. laser scanners, a remotely piloted aircraft system, a thermal camera, a backpack mobile mapping system and different grade photogrammetric equipment. During the one-week summer school, students were grouped according to their personal preference to work with one such type of equipment under guidance of an expert lecturer. All students were required to capture and process field data on a mountain-related theme like landslides or rock glaciers. The work on the assignments lasted the whole week but was interspersed with lectures on selected topics by invited experts. The final task of the summer school participants was to present and defend their results to their peers, lecturers and other colleagues in a symposium-like setting. Here we present the framework and content of this summer school which brought together scientists from close-range sensing and environmental and geosciences.
ENGLISH FOR SPECIFIC PURPOSES: TEACHING ENGLISH FOR SCIENCE AND TECHNOLOGY
Authors: Musikhin I. A.
Published: August 2016

In the era of globalization, English communication for scientists and engineers whose native language is not English has become as important as their major related abilities. The paper describes the results of a four-year experience in the development of English for specific purpose manuals in the field of photogrammetry, interferometry, and GNSS technologies, as well as key teaching methods and didactic approaches used in class and out-of-class activities. The focus of the present study is to provide a detailed description of the development and systematic updating of a relevant manual, aimed at professional language training of learners. The findings of the study reflect the importance of an ESP course for scientists and engineers: conducting a needs analysis for carrying out a specific search of relevant and reliable authentic materials, defining proper teaching methods, software and didactic approaches used in the educational process to develop the language skills necessary to be active and contributive players in the competitive world.
A COGNITIVE APPROACH TO TEACHING A GRADUATE-LEVEL GEOBIA COURSE
Authors: Bianchetti Raechel A.
Published: August 2016

Remote sensing image analysis training occurs both in the classroom and the research lab. Education in the classroom for traditional pixel-based image analysis has been standardized across college curriculums. However, with the increasing interest in Geographic Object-Based Image Analysis (GEOBIA), there is a need to develop classroom instruction for this method of image analysis. While traditional remote sensing courses emphasize the expansion of skills and knowledge related to the use of computer-based analysis, GEOBIA courses should examine the cognitive factors underlying visual interpretation. This current paper provides an initial analysis of the development, implementation, and outcomes of a GEOBIA course that considers not only the computational methods of GEOBIA, but also the cognitive factors of expertise, that such software attempts to replicate. Finally, a reflection on the first instantiation of this course is presented, in addition to plans for development of an open-source repository for course materials.
MONITORING OF THE UNDERMINED TERRITORIES OF KARAGANDA COAL BASIN ON THE BASIS OF SATELLITE RADAR INTERFEROMETRY
Authors: Ozhigina S. B. , Mozer D. V. , Ozhigin D. S , Ozhigin S. G. , Bessimbayeva O. G. , Khmyrova E. N.
Published: August 2016

In the Karaganda coal basin, mines are located in close proximity to each other and to the city of Karaganda and ongoing mining operations are accompanied by a dangerous process of settling the earth's surface and monitoring are essential for the region's econ-omy. Underground mining leads to the formation of voids in the rock mass, which cause displacement of the earth surface. This paper demonstrates an innovative use of the integrated approach for monitoring on the example of Karaganda coal basin, which includes estimation of the rock mass displacement using leveling profile lines and satellite radar interferometry. It is proved that satellite radar interferometry provides reliable results of surface subsidence measurements in mining areas and can be used for con-sidered sort of monitoring.
STANDALONE TERRESTRIAL LASER SCANNING FOR EFFICIENTLY CAPTURING AEC BUILDINGS FOR AS-BUILT BIM
Authors: Bassier M. , Vergauwen M. , Van Genechten B.
Published: August 2016

With the increasing popularity of as-built building models for the architectural, engineering and construction (AEC) industry, the demand for highly accurate and dense point cloud data is rising. The current data acquisition methods are labour intensive and time consuming. In order to compete with indoor mobile mapping systems (IMMS), surveyors are now opting to use terrestrial laser scanning as a standalone solution. However, there is uncertainty about the accuracy of this approach. The emphasis of this paper is to determine the scope for which terrestrial laser scanners can be used without additional control. Multiple real life test cases are evaluated in order to identify the boundaries of this technique. Furthermore, this research presents a mathematical prediction model that provides an indication of the data accuracy given the project dimensions. This will enable surveyors to make informed discussions about the employability of terrestrial laser scanning without additional control in mid to large-scale projects.
A NOVEL REMOVAL METHOD FOR DENSE STRIPES IN REMOTE SENSING IMAGES
Authors: Liu Xinxin , Shen Huanfeng , Yuan Qiangqiang , Zhang Liangpei , Cheng Qing
Published: August 2016

In remote sensing images, the common existing stripe noise always severely affects the imaging quality and limits the related subsequent application, especially when it is with high density. To well process the dense striped data and ensure a reliable solution, we construct a statistical property based constraint in our proposed model and use it to control the whole destriping process. The alternating direction method of multipliers (ADMM) is applied in this work to solve and accelerate the model optimization. Experimental results on real data with different kinds of dense stripe noise demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative perspectives.
RESEARCH INTO THE COLLIMATION AND HORIZONTAL AXIS ERRORS INFLUENCE ON THE Z+F LASER SCANNER ACCURACY OF VERTICALITY MEASUREMENT
Authors: Sawicki J. , Kowalczyk M.
Published: August 2016

Aim of this study was to appoint values of collimation and horizontal axis errors of the laser scanner ZF 5006h owned by Department of Geodesy and Cartography, Warsaw University of Technology, and then to determine the effect of those errors on the results of measurements. An experiment has been performed, involving measurement of the test field , founded in the Main Hall of the Main Building of the Warsaw University of Technology, during which values of instrumental errors of interest were determined. Then, an universal computer program that automates the proposed algorithm and capable of applying corrections to measured target coordinates or even entire point clouds from individual stations, has been developed.
A NEW METHOD FOR DETERMINING THE DEFORMATION MONITORABLE INDICATOR OF POINT CLOUD
Authors: Xuan W. , Hua X. H. , Qiu W. N. , Zou J. G. , Chen X. J.
Published: August 2016

With the continuous development of the terrestrial laser scanning (TLS) technique, the precision of the laser scanning has been improved which makes it possible that TLS could be used for high-precision deformation monitoring. A deformation monitorable indicator (DMI) should be determined to distinguish the deformation from the error of point cloud and plays an important role in the deformation monitoring using TLS. After the DMI determined, a scheme of the deformation monitoring case could be planned to choose a suitable instrument, set up a suitable distance and sampling interval. In this paper, the point error space and the point cloud error space are modelled firstly based on the point error ellipsoid. Secondly, the actual point error is derived by the relationship between the actual point cloud error space and the point error space. Then, the DMI is determined using the actual point error. Finally, two sets of experiments is carried out and the feasibility of the DMI is proved.
MAPPING URBAN TREE CANOPY COVER USING FUSED AIRBORNE LIDAR AND SATELLITE IMAGERY DATA
Authors: Parmehr Ebadat G. , Amati Marco , Fraser Clive S.
Published: August 2016

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.
ALTERNATIVE TO FOUR-COMPONENT DECOMPOSITION FOR POLARIMETRIC SAR
Authors: Zhang J. X. , Huang G. M. , Wei J. J. , Zhao Z.
Published: August 2016

There are more unknowns than equations to solve for previous four-component decomposition methods. In this case, the nonnegative power of each scattering mechanism has to be determined with some assumptions and physical power constraints. This paper presents a new decomposition scheme, which models the measured matrix after polarimetric orientation angle (POA) compensation as a linear sum of five scattering mechanisms (i.e., odd-bounce scattering, double-bounce scattering, diffuse scattering, volume scattering, and helix scattering). And the volume scattering power is calculated by a slight modified NNED method, owing to this method considering the external volume scattering model from oblique dihedral structure. After the helix and volume scattering powers have been determined sequentially, the other three scattering powers are estimated by combining the generalized similarity parameter (GSP) and the eigenvalue decomposition. Among them, due to POA compensation, the diffuse scattering induced from a dihedral with a relative orientation of 45º has negligible scattering power. Thus, the new method can be reduced as four-component decomposition automatically. And then the ALOS-2 PolSAR data covering Guiyang City, Guizhou Province, China were used to evaluate the performance of the new method in comparison with some classical decomposition methods (i.e. Y4R, S4R and G4U).
FOREST AREA DERIVATION FROM SENTINEL-1 DATA
Authors: Dostálová Alena , Hollaus Markus , Milenković Milutin , Wagner Wolfgang
Published: August 2016

The recently launched Sentinel-1A provides the high resolution Synthetic Aperture Radar (SAR) data with very high temporal coverage over large parts of European continent. Short revisit time and dual polarization availability supports its usability for forestry applications. The following study presents an analysis of the potential of the multi-temporal dual-polarization Sentinel-1A data for the forest area derivation using the standard methods based on Otsu thresholding and K-means clustering. Sentinel-1 data collected in winter season 2014-2015 over a test area in eastern Austria were used to derive forest area mask with spatial resolution of 10m and minimum mapping unit of 500 m2. The validation with reference forest mask derived from airborne full-waveform laser scanning data revealed overall accuracy of 92 % and kappa statistics of 0.81. Even better results can be achieved when using external mask for urban areas, which might be misclassified as forests when using the introduced approach based on SAR data only. The Sentinel-1 data and the described methods are well suited for forest change detection between consecutive years.
TOWARDS A SEMANTIC INTERPRETATION OF URBAN AREAS WITH AIRBORNE SYNTHETIC APERTURE RADAR TOMOGRAPHY
Authors: D'Hondt O. , Guillaso S. , Hellwich O.
Published: August 2016

In this paper, we introduce a method to detect and reconstruct building parts from tomographic Synthetic Aperture Radar (SAR) airborne data. Our approach extends recent works in two ways: first, the radiometric information is used to guide the extraction of geometric primitives. Second, building facades and roofs are extracted thanks to geometric classification rules. We demonstrate our method on a 3 image L-Band airborne dataset over the city of Dresden, Germany. Experiments show how our technique allows to use the complementarity between the radiometric image and the tomographic point cloud to extract buildings parts in challenging situations.
TASK-DEPENDENT BAND-SELECTION OF HYPERSPECTRAL IMAGES BY PROJECTION-BASED RANDOM FORESTS
Authors: Hänsch R. , Hellwich O.
Published: August 2016

The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the large amount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequent classifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is found by band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests to hyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automatically select spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classification task. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectral bands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1 % accuracy if roughly 13 % of all available bands are used.
BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL
Authors: Matsuoka M. , Takagi M. , Akatsuka S. , Honda R. , Nonomura A. , Moriya H. , Yoshioka H.
Published: August 2016

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.
OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE
Authors: Alberto R. T. , Serrano S. C. , Damian G. B. , Camaso E. E. , Celestino A. B. , Hernando P. J. C. , Isip M. F. , Orge K. M. , Quinto M. J. C. , Tagaca R. C.
Published: August 2016

Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.
POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING
Authors: Babic Z. , Pilipovic R. , Risojevic V. , Mirjanic G.
Published: August 2016

Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.
A COMPARISON OF SUB-PIXEL MAPPING METHODS FOR COASTAL AREAS
Authors: Liu Qingxiang , Trinder John , Turner Ian
Published: August 2016

This paper presents the comparisons of three soft classification methods and three sub-pixel mapping methods for the classification of coastal areas at sub-pixel level. Specifically, SPOT-7 multispectral images covering the coastal area of Perth are selected as the experiment dataset. For the soft classification, linear spectral unmixing model, supervised fully-fuzzy classification method and the support vector machine are applied to generate the fraction map. Then for the sub-pixel mapping, the sub-pixel/pixel attraction model, pixel swapping and wavelets method are compared. Besides, the influence of the correct fraction constraint is explored. Moreover, a post-processing step is implemented according to the known spatial knowledge of coastal areas. The accuracy assessment of the fraction values indicates that support vector machine generates the most accurate fraction result. For sub-pixel mapping, wavelets method outperforms the other two methods with overall classification accuracy of 91.79% and Kappa coefficient of 0.875 after the post-processing step and it also performs best for waterline extraction with mean distance of 0.71m to the reference waterline. In this experiment, the use of correct fraction constraint decreases the classification accuracy of sub-pixel mapping methods and waterline extraction. Finally, the post-processing step improves the accuracy of sub-pixel mapping methods, especially for those with correct coefficient constraint. The most significant improvement of overall accuracy is as much as 4% for the sub-pixel/pixel attraction model with correct coefficient constraint.
DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS
Authors: Roscher Ribana , Behmann Jan , Mahlein Anne-Katrin , Dupuis Jan , Kuhlmann Heiner , Plümer Lutz
Published: August 2016

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.
MAPPING AND CHANGE ANALYSIS IN MANGROVE FOREST BY USING LANDSAT IMAGERY
Authors: Dan T. T. , Chen C. F. , Chiang S. H. , Ogawa S.
Published: August 2016

Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km2 of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.
THE EFFECT OF WIND ON TREE STEM PARAMETER ESTIMATION USING TERRESTRIAL LASER SCANNING
Authors: Vaaja M. T. , Virtanen J.-P. , Kurkela M. , Lehtola V. , Hyyppä J. , Hyyppä H.
Published: August 2016

The 3D measurement technique of terrestrial laser scanning (TLS) in forest inventories has shown great potential for improving the accuracy and efficiency of both individual tree and plot level data collection. However, the effect of wind has been poorly estimated in the error analysis of TLS tree measurements although it causes varying deformations to the trees. In this paper, we evaluated the effect of wind on tree stem parameter estimation at different heights using TLS. The data consists of one measured Scots pine captured from three different scanning directions with two different scanning resolutions, 6.3 mm and 3.1 mm at 10 m. The measurements were conducted under two different wind speeds, approximately 3 m/s and 9 m/s, as recorded by a nearby weather station of the Finnish Meteorological Institute. Our results show that the wind may cause both the underestimation and overestimation of tree diameter when using TLS. The duration of the scanning is found to have an impact for the measured shape of the tree stem under 9 m/s wind conditions. The results also indicate that a 9 m/s wind does not have a significant effect on the stem parameters of the lower part of a tree (<28% of the tree height). However, as the results imply, the wind conditions should be taken into account more comprehensively in analysis of TLS tree measurements, especially if multiple scans from different positions are registered together. In addition, TLS could potentially be applied to indirectly measure wind speed by observing the tree stem movement.
OVERLAND FLOW ANALYSIS USING TIME SERIES OF SUAS-DERIVED ELEVATION MODELS
Authors: Jeziorska J. , Mitasova H. , Petrasova A. , Petras V. , Divakaran D. , Zajkowski T.
Published: August 2016

With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (Mw = 7.5) OF 25 APRIL 2015
Authors: Akhoondzadeh M.
Published: August 2016

Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth = 15.0 km) a powerful earthquake of Mw = 7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.
ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR
Authors: Ni Made Pertiwi Jaya , Miura Fusanori , Rimba A. Besse
Published: August 2016

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km2. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.
NEED FOR RELIABLE SENSOR CALIBRATION FROM THE PERSPECTIVE OF A NATIONAL MAPPING AGENCY
Authors: Baltrusch S.
Published: August 2016

The sensor calibration is one of the basic elements for getting effective and efficient production workflows out of airborne photogrammetry. Digital images and their orientations (interior and exterior) are the key to get the resulting products into the workflow of a national mapping agency (NMCA). Not only the geometric calibration is required meanwhile the radiometric calibration as well is used, for example in raster-based classification processes. In the paper the requirements are shown and examples are presented. At least open aspects and the outstanding debts are given.
ORIENTATION AND CALIBRATION REQUIREMENTS FOR HYPERPECTRAL IMAGING USING UAVs: A CASE STUDY
Authors: Tommaselli A. M. G. , Berveglieri A. , Oliveira R. A. , Nagai L. Y. , Honkavaara E.
Published: August 2016

Flexible tools for photogrammetry and remote sensing using unmanned airborne vehicles (UAVs) have been attractive topics of research and development. The lightweight hyperspectral camera based on a Fabry-Pérot interferometer (FPI) is one of the highly interesting tools for UAV based remote sensing for environmental and agricultural applications. The camera used in this study acquires images from different wavelengths by changing the FPI gap and using two CMOS sensors. Due to the acquisition principle of this camera, the interior orientation parameters (IOP) of the spectral bands can vary for each band and sensor and changing the configuration also would change these sets of parameters posing an operational problem when several bands configurations are being used. The objective of this study is to assess the impact of use IOPs estimated for some bands in one configuration for other bands of different configuration the FPI camera, considering different IOP and EOP constraints. The experiments were performed with two FPI-hyperspectral camera data sets: the first were collected 3D terrestrial close-range calibration field and the second onboard of an UAV in a parking area in the interior of São Paulo State.
EXTERIOR ORIENTATION OF HYPERSPECTRAL FRAME IMAGES COLLECTED WITH UAV FOR FOREST APPLICATIONS
Authors: Berveglieri A. , Tommaselli A. M. G.
Published: August 2016

This paper describes a preliminary study on the image orientation acquired by a hyperspectral frame camera for applications in small tropical forest areas with dense vegetation. Since access to the interior of forests is complicated and Ground Control Points (GCPs) are not available, this study conducts an assessment of the altimetry accuracy provided by control targets installed on one border of an image block, simulating it outside a forest. A lightweight Unmanned Aerial Vehicle (UAV) was equipped with a hyperspectral camera and a dual-frequency GNSS receiver to collect images at two flying strips covering a vegetation area. The assessment experiments were based on Bundle Block Adjustment (BBA) with images of two spectral bands (from two sensors) using several weighted constraints in the camera position. Trials with GCPs (presignalized targets) positioned only on one side of the image block were compared with trials using GCPs in the corners. Analyses were performed on altimetry discrepancies obtained from altimetry checkpoints. The results showed a discrepancy in Z coordinate of approximately 40 cm using the proposed technique, which is sufficient for applications in forests.
TRAJECTORY ADJUSTMENT OF MOBILE LASER SCAN DATA IN GPS DENIED ENVIRONMENTS
Authors: Schaer P. , Vallet J.
Published: August 2016

Mobile mapping often occurs in environments with poor or no GNSS signal reception (tunnels, urban canons, canopy …). Most mobile scanning systems are equipped with tactical grade inertial systems, GNSS outages longer than 30-60 seconds may lead to a rapid decrease in absolute positioning accuracy that might be below the client's expectations. In order to guarantee sufficient positioning accuracy even in such environments the data must be corrected. This can either happen by readjusting the final point cloud using control points (3D translations) or by directly correcting the trajectory by adding external position updates. The approach presented in this paper consists in detecting targets (either existing or placed before measurement) that can easily be identified in the point cloud and that have been measured independently. By identifying the 3D point closest to the target's center, computing the coordinate difference to the corresponding GCP and retrieving its GNSS-timestamp and the internal scanner coordinates, an external position update at a given time can be computed. This procedure has proven its efficiency in several projects including scanning tunnels up to 5km length, where the positional error (in 3D) of the resulting point cloud could be reduced from 5m (in the middle of the tunnel) down to better than 5cm using GCPS only every 400m.