bluuebunny/crossref_metadata_2025 huggingface dataset:
A SIMULATION-AS-A-SERVICE FRAMEWORK FACILITATING WEBGIS BASED INSTALLATION PLANNING
Authors: Zheng Z. , Chang Z. Y. , Fei Y. F.
Published: September 2017

Abstract. Installation Planning is constrained by both natural and social conditions, especially for spatially sparse but functionally connected facilities. Simulation is important for proper deploy in space and configuration in function of facilities to make them a cohesive and supportive system to meet users’ operation needs. Based on requirement analysis, we propose a framework to combine GIS and Agent simulation to overcome the shortness in temporal analysis and task simulation of traditional GIS. In this framework, Agent based simulation runs as a service on the server, exposes basic simulation functions, such as scenario configuration, simulation control, and simulation data retrieval to installation planners. At the same time, the simulation service is able to utilize various kinds of geoprocessing services in Agents’ process logic to make sophisticated spatial inferences and analysis. This simulation-as-a-service framework has many potential benefits, such as easy-to-use, on-demand, shared understanding, and boosted performances. At the end, we present a preliminary implement of this concept using ArcGIS javascript api 4.0 and ArcGIS for server, showing how trip planning and driving can be carried out by agents.
3D SCANNING OF LIVE PIGS SYSTEM AND ITS APPLICATION IN BODY
MEASUREMENTS
Authors: Guo H. , Wang K. , Su W. , Zhu D. H. , Liu W. L. , Xing Ch. , Chen Z. R.
Published: September 2017

Abstract. The shape of a live pig is an important indicator of its health and value, whether for breeding or for carcass quality. This paper implements a prototype system for live single pig body surface 3d scanning based on two consumer depth cameras, utilizing the 3d point clouds data. These cameras are calibrated in advance to have a common coordinate system. The live 3D point clouds stream of moving single pig is obtained by two Xtion Pro Live sensors from different viewpoints simultaneously. A novel detection method is proposed and applied to automatically detect the frames containing pigs with the correct posture from the point clouds stream, according to the geometric characteristics of pig’s shape. The proposed method is incorporated in a hybrid scheme, that serves as the preprocessing step in a body measurements framework for pigs. Experimental results show the portability of our scanning system and effectiveness of our detection method. Furthermore, an updated this point cloud preprocessing software for livestock body measurements can be downloaded freely from https://github.com/LiveStockShapeAnalysis to livestock industry, research community and can be used for monitoring livestock growth status.
APPLICATIONS OF 3D-EDGE DETECTION FOR ALS POINT CLOUD
Authors: Ni H. , Lin X. G. , Zhang J. X.
Published: September 2017

Abstract. Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point’s neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.
CSF BASED NON-GROUND POINTS EXTRACTION FROM LIDAR DATA
Authors: Shen A. , Zhang W. , Shi H.
Published: September 2017

Abstract. Region growing is a classical method of point cloud segmentation. Based on the idea of collecting the pixels with similar properties to form regions, region growing is widely used in many fields such as medicine, forestry and remote sensing. In this algorithm, there are two core problems. One is the selection of seed points, the other is the setting of the growth constraints, in which the selection of the seed points is the foundation. In this paper, we propose a CSF (Cloth Simulation Filtering) based method to extract the non-ground seed points effectively. The experiments have shown that this method can obtain a group of seed spots compared with the traditional methods. It is a new attempt to extract seed points
POINT CLOUD SEGMENTATION OF GULLY BASED ON CHARACTERISTIC
DIFFERENCE USING AIRBORNE LIDAR DATA
Authors: Xu Y. , Yue D. , He P.
Published: September 2017

Abstract. The point cloud segmentation of gullies with high accuracy lays the groundwork for the gully parameter extraction and developing models. A point cloud segmentation method of gullies based on characteristic difference from airborne LIDAR is proposed. Firstly, point cloud characteristics of gullies are discussed, and then differences in surface features are obtained based on different scales after preprocessing of the point cloud. Initial gullies are segmented from point clouds combined with a curvature threshold. Finally, real gully point clouds are obtained based on the clustering analysis. The experimental results demonstrate that gullies can be detected accurately with airborne LIDAR point clouds, and this method provides a new idea for quantitative evaluation of gullies.
CARBON SEQUESTRATION ESTIMATION OF STREET TREES BASED ON POINT CLOUD FROM VEHICLE-BORNE LASER SCANNING SYSTEM
Authors: Zhao Y. , Hu Q.
Published: September 2017

Abstract. Continuous development of urban road traffic system requests higher standards of road ecological environment. Ecological benefits of street trees are getting more attention. Carbon sequestration of street trees refers to the carbon stocks of street trees, which can be a measurement for ecological benefits of street trees. Estimating carbon sequestration in a traditional way is costly and inefficient. In order to solve above problems, a carbon sequestration estimation approach for street trees based on 3D point cloud from vehicle-borne laser scanning system is proposed in this paper. The method can measure the geometric parameters of a street tree, including tree height, crown width, diameter at breast height (DBH), by processing and analyzing point cloud data of an individual tree. Four Chinese scholartree trees and four camphor trees are selected for experiment. The root mean square error (RMSE) of tree height is 0.11m for Chinese scholartree and 0.02m for camphor. Crown widths in X direction and Y direction, as well as the average crown width are calculated. And the RMSE of average crown width is 0.22m for Chinese scholartree and 0.10m for camphor. The last calculated parameter is DBH, the RMSE of DBH is 0.5cm for both Chinese scholartree and camphor. Combining the measured geometric parameters and an appropriate carbon sequestration calculation model, the individual tree’s carbon sequestration will be estimated. The proposed method can help enlarge application range of vehicle-borne laser point cloud data, improve the efficiency of estimating carbon sequestration, construct urban ecological environment and manage landscape.
AUTOMATIC MONITORING OF TUNNEL DEFORMATION BASED ON HIGH DENSITY POINT CLOUDS DATA
Authors: Du L. , Zhong R. , Sun H. , Wu Q.
Published: September 2017

Abstract. An automated method for tunnel deformation monitoring using high density point clouds data is presented. Firstly, the 3D point clouds data are converted to two-dimensional surface by projection on the XOY plane, the projection point set of central axis on XOY plane named Uxoy is calculated by combining the Alpha Shape algorithm with RANSAC (Random Sampling Consistency) algorithm, and then the projection point set of central axis on YOZ plane named Uyoz is obtained by highest and lowest points which are extracted by intersecting straight lines that through each point of Uxoy and perpendicular to the two -dimensional surface with the tunnel point clouds, Uxoy and Uyoz together form the 3D center axis finally. Secondly, the buffer of each cross section is calculated by K-Nearest neighbor algorithm, and the initial cross-sectional point set is quickly constructed by projection method. Finally, the cross sections are denoised and the section lines are fitted using the method of iterative ellipse fitting. In order to improve the accuracy of the cross section, a fine adjustment method is proposed to rotate the initial sectional plane around the intercept point in the horizontal and vertical direction within the buffer. The proposed method is used in Shanghai subway tunnel, and the deformation of each section in the direction of 0 to 360 degrees is calculated. The result shows that the cross sections becomes flat circles from regular circles due to the great pressure at the top of the tunnel
DATA QUALITY IN REMOTE SENSING
Authors: Batini C. , Blaschke T. , Lang S. , Albrecht F. , Abdulmutalib H. M. , Barsi Á. , Szabó G. , Kugler Zs.
Published: September 2017

Abstract. The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb “DQ” identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.
DEVELOPMENT OF GEOSPATIAL MAP BASED PORTAL FOR NEW DELHI MUNICIPAL COUNCIL
Authors: Kumar Chandra Gupta A. , Kumar P. , Kumar Sharma P.
Published: September 2017

Abstract. The Geospatial Delhi Limited (GSDL), a Govt. of NCT of Delhi Company formed in order to provide the geospatial information of National Capital Territory of Delhi (NCTD) to the Government of National Capital Territory of Delhi (GNCTD) and its organs such as DDA, MCD, DJB, State Election Department, DMRC etc., for the benefit of all citizens of Government of National Capital Territory of Delhi (GNCTD). This paper describes the development of Geospatial Map based Portal (GMP) for New Delhi Municipal Council (NDMC) of NCT of Delhi. The GMP has been developed as a map based spatial decision support system (SDSS) for planning and development of NDMC area to the NDMC department and It’s heaving the inbuilt information searching tools (identifying of location, nearest utilities locations, distance measurement etc.) for the citizens of NCTD. The GMP is based on Client-Server architecture model. It has been developed using Arc GIS Server 10.0 with .NET (pronounced dot net) technology. The GMP is scalable to enterprise SDSS with enterprise Geo Database & Virtual Private Network (VPN) connectivity. Spatial data to GMP includes Circle, Division, Sub-division boundaries of department pertaining to New Delhi Municipal Council, Parcels of residential, commercial, and government buildings, basic amenities (Police Stations, Hospitals, Schools, Banks, ATMs and Fire Stations etc.), Over-ground and Underground utility network lines, Roads, Railway features. GMP could help achieve not only the desired transparency and easiness in planning process but also facilitates through efficient & effective tools for development and management of MCD area. It enables a faster response to the changing ground realities in the development planning, owing to its in-built scientific approach and open-ended design.
TWO METHODS FOR REMOTE ESTIMATION OF COMPLETE URBAN SURFACE
TEMPERATURE
Authors: Jiang L. , Zhan W. , Zou Z.
Published: September 2017

Abstract. Complete urban surface temperature (TC) is a key parameter for evaluating the energy exchange between the urban surface and atmosphere. At the present stage, the estimation of TC still needs detailed 3D structure information of the urban surface, however, it is often difficult to obtain the geometric structure and composition of the corresponding temperature of urban surface, so that there is still lack of concise and efficient method for estimating the TC by remote sensing. Based on the four typical urban surface scale models, combined with the Envi-met model, thermal radiant directionality forward modeling and kernel model, we analyzed a complete day and night cycle hourly component temperature and radiation temperature in each direction of two seasons of summer and winter, and calculated hemispherical integral temperature and TC. The conclusion is obtained by examining the relationship of directional radiation temperature, hemispherical integral temperature and TC: (1) There is an optimal angle of radiation temperature approaching the TC in a single observation direction when viewing zenith angle is 45–60°, the viewing azimuth near the vertical surface of the sun main plane, the average absolute difference is about 1.1 K in the daytime. (2) There are several (3–5 times) directional temperatures of different view angle, under the situation of using the thermal radiation directionality kernel model can more accurately calculate the hemispherical integral temperature close to TC, the mean absolute error is about 1.0 K in the daytime. This study proposed simple and effective strategies for estimating TC by remote sensing, which are expected to improve the quantitative level of remote sensing of urban thermal environment.
DEVELOPMENT OF GEOSPATIAL MAP BASED PORTAL FOR DELIMITATION OF MCD WARDS
Authors: Kumar Chandra Gupta A. , Kumar P. , Kumar Sharma P.
Published: September 2017

Abstract. The Geospatial Delhi Limited (GSDL), a Govt. of NCT of Delhi Company formed in order to provide the geospatial information of National Capital Territory of Delhi (NCTD) to the Government of National Capital Territory of Delhi (GNCTD) and its organs such as DDA, MCD, DJB, State Election Department, DMRC etc., for the benefit of all citizens of Government of National Capital Territory of Delhi (GNCTD). This paper describes the development of Geospatial Map based Portal for Delimitation of MCD Wards (GMPDW) and election of 3 Municipal Corporations of NCT of Delhi. The portal has been developed as a map based spatial decision support system (SDSS) for delimitation of MCD Wards and draw of peripheral wards boundaries to planning and management of MCD Election process of State Election Commission, and as an MCD election related information searching tools (Polling Station, MCD Wards and Assembly constituency etc.,) for the citizens of NCTD. The GMPDW is based on Client-Server architecture model. It has been developed using Arc GIS Server 10.0 with .NET (pronounced dot net) technology. The GMPDW is scalable to enterprise SDSS with enterprise Geo Database & Virtual Private Network (VPN) connectivity. Spatial data to GMPDW includes Enumeration Block (EB) and Enumeration Blocks Group (EBG) boundaries of Citizens of Delhi, Assembly Constituency, Parliamentary Constituency, Election District, Landmark locations of Polling Stations & basic amenities (Police Stations, Hospitals, Schools and Fire Stations etc.). GMPDW could help achieve not only the desired transparency and easiness in planning process but also facilitates through efficient & effective tools for management of MCD election. It enables a faster response to the changing ground realities in the development planning, owing to its in-built scientific approach and open-ended design.
OSM POI ANALYZER: A PLATFORM FOR ASSESSING POSITION OF POIs IN OPENSTREETMAP
Authors: Kashian A. , Rajabifard A. , Chen Y. , Richter K. F.
Published: September 2017

Abstract. In recent years, more and increased participation in Volunteered Geographical Information (VGI) projects provides enough data coverage for most places around the world for ordinary mapping and navigation purposes, however, the positional credibility of contributed data becomes more and more important to bring a long-term trust in VGI data. Today, it is hard to draw a definite traditional boundary between the authoritative map producers and the public map consumers and we observe that more and more volunteers are joining crowdsourcing activities for collecting geodata, which might result in higher rates of man-made mistakes in open map projects such as OpenStreetMap. While there are some methods for monitoring the accuracy and consistency of the created data, there is still a lack of advanced systems to automatically discover misplaced objects on the map. One feature type which is contributed daily to OSM is Point of Interest (POI). In order to understand how likely it is that a newly added POI represents a genuine real-world feature scientific means to calculate a probability of such a POI existing at that specific position is needed. This paper reports on a new analytic tool which dives into OSM data and finds co-existence patterns between one specific POI and its surrounding objects such as roads, parks and buildings. The platform uses a distance-based classification technique to find relationships among objects and tries to identify the high-frequency association patterns among each category of objects. Using such method, for each newly added POI, a probabilistic score would be generated, and the low scored POIs can be highlighted for editors for a manual check. The same scoring method can be used for existing registered POIs to check if they are located correctly. For a sample study, this paper reports on the evaluation of 800 pre-registered ATMs in Paris with associated scores to understand how outliers and fake entries could be detected automatically.
DOES QUALITY CONTROL MATTER? A REVISIT OF SURFACE URBAN HEAT ISLAND INTENSITY ESTIMATED BY SATELLITE-DERIVED LAND SURFACE TEMPERATURE PRODUCTS
Authors: Lai J. , Zhan W. , Huang F.
Published: September 2017

Abstract. Temporally regular and spatially continuous monitoring of surface urban heat island (SUHI) has been extremely difficult until the advent of spaceborne land surface temperature (LST) products. The higher errors of these LST products compared with in-situ measurements, nevertheless, have resulted in a comparatively inaccuracy and may distort the interpretation of SUHI. Although reports have shown that LST quality matters to the SUHI interpretation, a systematic investigation on how the SUHI indicators are responsive to the LST quality across cities within dissimilar bioclimates remains rare. With regard to this issue, our study chose eighty-six major cities across the mainland China and analyzed the SUHI intensity (SUHII) discrepancies (referred to as ΔSUHII) between using and not using quality control (QC) flags from Moderate Resolution Imaging Spectroradiometer data. Our major findings include: (1) the SUHII can be significantly impacted by the MODIS QC flags, and the associated seasonal ΔSUHIIs generally account for 25.5 % (29.6 %) of the total intensity in the day (night). (2) The ΔSUHIIs differ season-by-season and significant discrepancies also appear among northern and southern cities, with northern ones often possessing a higher annual mean ΔSUHII. (3) The internal ΔSUHIIs within an individual city are also heterogeneous, with the variations exceeding 5.0 K (3.0 K) in northern (southern) cities. (4) The ΔSUHII is significantly negatively related to the SUHII and cloud cover percentage mostly in transitional seasons. Our findings highlight that one needs to be very careful when using the LST-product-based SUHII to interpret the SUHI.
COMPREHENSIVE COMPARISON OF TWO IMAGE-BASED POINT CLOUDS
FROM AERIAL PHOTOS WITH AIRBORNE LIDAR FOR LARGE-SCALE MAPPING
Authors: Widyaningrum E. , Gorte B. G. H.
Published: September 2017

Abstract. The integration of computer vision and photogrammetry to generate three-dimensional (3D) information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM) and by Structure from Motion (SfM). In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.
SHADOWDETECTION FROM VHR AERIAL IMAGES IN URBAN AREA BY USING 3D
CITY MODELS AND A DECISION FUSION APPROACH
Authors: Zhou K. L. , Gorte B. G. H.
Published: September 2017

Abstract. In VHR(very high resolution) aerial images, shadows indicating height information are valuable for validating or detecting changes on an existing 3D city model. In the paper, we propose a novel and full automatic approach for shadow detection from VHR images. Instead of automatic thresholding, the supervised machine learning approach is expected with better performance on shadow detection, but it requires to obtain training samples manually. The shadow image reconstructed from an existing 3D city model can provide free training samples with large variety. However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training samples are mislabeled. The erosion morphology is provided to remove boundary pixels which have high mislabeling possibility from the reconstructed image. Moreover, the quadratic discriminant analysis (QDA) which is resistant to the mislabeling is chosen. Further, two feature domains, RGB and ratio of the hue over the intensity, are analyzed to have complementary effects on better detecting different objects. Finally, a decision fusion approach is proposed to combine the results wisely from preliminary classifications from two feature domains. The fuzzy membership is a confidence measurement and determines the way of making decision, in the meanwhile the memberships are weighted by an entropy measurements to indicate their certainties. The experimental results on two cities in the Netherlands demonstrate that the proposed approach outperforms the two separate classifiers and two stacked-vector fusion approaches.
DEFORMATION MEASUREMENT USING SENTINEL-1A/B IMAGERY
Authors: Crosetto M. , Monserrat O. , Devanthéry N. , Cuevas-González M. , Barra A. , Crippa B.
Published: September 2017

Abstract. Land deformation monitoring based on C-band Synthetic Aperture Radar Sentinel-1A/B imagery is the main focus of this paper. This type of data is exploited using a Persistent Scatterer Interferometry technique. The paper describes a deformation monitoring strategy, which is related to a specific monitoring scenario: a relatively small deformation area of interest surrounded by a stable area. In the case study considered in this work, the scenario corresponds to an area of potential subsidence induced by underground water pumping. In this specific case, the deformation area of interest has a radius of approximately 1 km. The proposed monitoring strategy takes advantage of the specific scenario at hand, and, in particular, of the availability of stable areas close to the area that potentially is affected by deformation. In this paper we briefly describe the proposed data analysis strategy. The key component of the strategy, i.e. the estimation of the atmospheric component, is illustrated in detail. Some examples of the two main products of the procedure, i.e. the deformation velocity map and the deformation time series, are discussed.
A NOVEL AND FAST CORNER DETECTION METHOD FOR SAR IMAGERY
Authors: Jiao N. , Kang W. , Xiang Y. , You H.
Published: September 2017

Abstract. Corners play an important role on image processing, while it is difficult to detect reliable and repeatable corners in SAR images due to the complex property of SAR sensors. In this paper, we propose a fast and novel corner detection method for SAR imagery. First, a local processing window is constructed for each point. We use the local mean of a 3 x 3 mask to represent a single point, which is weighted by a Gaussian template. Then the candidate point is compared with 16 surrounding points in the processing window. Considering the multiplicative property of speckle noise, the similarity measure between the center point and the surrounding points is calculated by the ratio of their local means. If there exist more than M continuous points are different from the center point, then the candidate point is labelled as a corner point. Finally, a selection strategy is implemented by ranking the corner score and employing the non-maxima suppression method. Extreme situations such as isolated bright points are also removed. Experimental results on both simulated and real-world SAR images show that the proposed detector has a high repeatability and a low localization error, compared with other state-of-the-art detectors.
GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION
Authors: Shi X. , Zhao Q. H.
Published: September 2017

Abstract. For the image segmentation method based on Gaussian Mixture Model (GMM), there are some problems: 1) The number of component was usually a fixed number, i.e., fixed class and 2) GMM is sensitive to image noise. This paper proposed a RS image segmentation method that combining GMM with reversible jump Markov Chain Monte Carlo (RJMCMC). In proposed algorithm, GMM was designed to model the distribution of pixel intensity in RS image. Assume that the number of component was a random variable. Respectively build the prior distribution of each parameter. In order to improve noise resistance, used Gibbs function to model the prior distribution of GMM weight coefficient. According to Bayes' theorem, build posterior distribution. RJMCMC was used to simulate the posterior distribution and estimate its parameters. Finally, an optimal segmentation is obtained on RS image. Experimental results show that the proposed algorithm can converge to the optimal number of class and get an ideal segmentation results.
QUANTITATIVE EVALUATION METHOD OF ELEMENTS PRIORITY OF CARTOGRAPHIC GENERALIZATION BASED ON TAXI TRAJECTORY DATA
Authors: Long Z. , Yang N. , Huang Y. , Chao Y. , Wan L.
Published: September 2017

Abstract. Considering the lack of quantitative criteria for the selection of elements in cartographic generalization, this study divided the hotspot areas of passengers into parts at three levels, gave them different weights, and then classified the elements from the different hotspots. On this basis, a method was proposed to quantify the priority of elements selection. Subsequently, the quantitative priority of different cartographic elements was summarized based on this method. In cartographic generalization, the method can be preferred to select the significant elements and discard those that are relatively non-significant.
THE MONITORING CASE OF GROUND-BASED SYNTHETIC APERTURE RADAR WITH FREQUENCY MODULATED CONTINUOUS WAVE SYSTEM
Authors: Zhang H. Y. , Zhai Q. P. , Chen L. , Liu Y. J. , Zhou K. Q. , Wang Y. S. , Dou Y. D.
Published: September 2017

Abstract. The features of the landslide geological disaster are wide distribution, variety, high frequency, high intensity, destructive and so on. It has become a natural disaster with harmful and wide range of influence. The technology of ground-based synthetic aperture radar is a novel deformation monitoring technology developed in recent years. The features of the technology are large monitoring area, high accuracy, long distance without contact and so on. In this paper, fast ground-based synthetic aperture radar (Fast-GBSAR) based on frequency modulated continuous wave (FMCW) system is used to collect the data of Ma Liuzui landslide in Chongqing. The device can reduce the atmospheric errors caused by rapidly changing environment. The landslide deformation can be monitored in severe weather conditions (for example, fog) by Fast-GBSAR with acquisition speed up to 5 seconds per time. The data of Ma Liuzui landslide in Chongqing are analyzed in this paper. The result verifies that the device can monitor landslide deformation under severe weather conditions.
BUILDINGS CHANGE DETECTION BASED ON SHAPE MATCHING FOR MULTI-RESOLUTION REMOTE SENSING IMAGERY
Authors: Abdessetar M. , Zhong Y.
Published: September 2017

Abstract. Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object’s shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).
THE IMAGE REGISTRATION OF FOURIER-MELLIN BASED ON THE COMBINATION OF PROJECTION AND GRADIENT PREPROCESSING
Authors: Gao D. , Zhao X. , Pan X.
Published: September 2017

Abstract. Image registration is one of the most important applications in the field of image processing. The method of Fourier Merlin transform, which has the advantages of high precision and good robustness to change in light and shade, partial blocking, noise influence and so on, is widely used. However, not only this method can’t obtain the unique mutual power pulse function for non-parallel image pairs, even part of image pairs also can’t get the mutual power function pulse. In this paper, an image registration method based on Fourier-Mellin transformation in the view of projection-gradient preprocessing is proposed. According to the projection conformational equation, the method calculates the matrix of image projection transformation to correct the tilt image; then, gradient preprocessing and Fourier-Mellin transformation are performed on the corrected image to obtain the registration parameters. Eventually, the experiment results show that the method makes the image registration of Fourier-Mellin transformation not only applicable to the registration of the parallel image pairs, but also to the registration of non-parallel image pairs. What’s more, the better registration effect can be obtained
MULTI-BASELINE INSAR ELEVATION INVERSION METHOD BASED ON THREE-DIMENSIONAL RECONSTRUCTION MODEL
Authors: Hua F. , Peng G. , Lu L. , Wang Z.
Published: September 2017

Abstract. The existing multi-baseline methods have some problems of low accuracy and intensive calculation. In order to solve the problems, a new multi-baseline InSAR elevation inversion method based on a rigorous geometric model instead of a simplified model is proposed in the letter. This method introduces the three-dimensional reconstruction model based on rigorous geometric model and the unknown full cycles of interferometric phase as a parameter to iteratively solve the 3-D coordinates of the target. With adopting the 3-D coordinate information of targets to connect different interferometric data, the new method obviously weakens the effects of system errors on solving the integer cycle and is more reliable than conventional multi-baseline InSAR methods. The experimental results show that the speed and accuracy of the new method are better than the existing methods.
A FAST SEGMENTATION ALGORITHM FOR C-V MODEL BASED ON EXPONENTIAL IMAGE SEQUENCE GENERATION
Authors: Hu J. , Lu L. , Xu J. , Zhang J.
Published: September 2017

Abstract. For the island coastline segmentation, a fast segmentation algorithm for C-V model method based on exponential image sequence generation is proposed in this paper. The exponential multi-scale C-V model with level set inheritance and boundary inheritance is developed. The main research contributions are as follows: 1) the problems of the "holes" and "gaps" are solved when extraction coastline through the small scale shrinkage, low-pass filtering and area sorting of region. 2) the initial value of SDF (Signal Distance Function) and the level set are given by Otsu segmentation based on the difference of reflection SAR on land and sea, which are finely close to the coastline. 3) the computational complexity of continuous transition are successfully reduced between the different scales by the SDF and of level set inheritance. Experiment results show that the method accelerates the acquisition of initial level set formation, shortens the time of the extraction of coastline, at the same time, removes the non-coastline body part and improves the identification precision of the main body coastline, which automates the process of coastline segmentation.
AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER SCANNING DATA
Authors: Ma H. , Pei Z. , Wei Z. , Zhong R.
Published: September 2017

Abstract. Road markings as critical feature in high-defination maps, which are Advanced Driver Assistance System (ADAS) and self-driving technology required, have important functions in providing guidance and information to moving cars. Mobile laser scanning (MLS) system is an effective way to obtain the 3D information of the road surface, including road markings, at highway speeds and at less than traditional survey costs. This paper presents a novel method to automatically extract road markings from MLS point clouds. Ground points are first filtered from raw input point clouds using neighborhood elevation consistency method. The basic assumption of the method is that the road surface is smooth. Points with small elevation-difference between neighborhood are considered to be ground points. Then ground points are partitioned into a set of profiles according to trajectory data. The intensity histogram of points in each profile is generated to find intensity jumps in certain threshold which inversely to laser distance. The separated points are used as seed points to region grow based on intensity so as to obtain road mark of integrity. We use the point cloud template-matching method to refine the road marking candidates via removing the noise clusters with low correlation coefficient. During experiment with a MLS point set of about 2 kilometres in a city center, our method provides a promising solution to the road markings extraction from MLS data.
MULTIPLE-PRIMITIVES-BASED HIERARCHICAL CLASSIFICATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREAS
Authors: Ni H. , Lin X. G. , Zhang J. X.
Published: September 2017

Abstract. A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.
A MULTI-CORE PARALLEL MOSAIC ALORITHM FOR MULTI-VIEW UAV IMAGES
Authors: Pan X. , Zhao X. , Gao D. , Li X.
Published: September 2017

Abstract. As the spread of the error and accumulation often lead to distortion or failure of image mosaic during the multi-view UAV (Unmanned Aerial Vehicle) images stitching. In this paper, to solve the problem we propose a mosaic strategy to construct a mosaic ring and multi-level grouping parallel acceleration as an auxiliary. First, the input images will be divided into several groups, each group in the ring way to stitch. Then, use SIFT for matching, RANSAC to remove the wrong matching points. And then, calculate the perspective transformation matrix. Finally weaken the error by using the adjustment equation. All these steps run between different groups at the same time. By using real UAV images, the experiment results show that this method can effectively reduce the influence of accumulative error, improve the precision of mosaic and reduce the mosaic time by 60 %. The proposed method can be used as one of the effective ways to minimize the accumulative error.
USING COMBINATION OF PLANAR AND HEIGHT FEATURES FOR DETECTING BUILT-UP AREAS FROM HIGH-RESOLUTION STEREO IMAGERY
Authors: Peng F. , Cai X. , Tan W.
Published: September 2017

Abstract. Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM). Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3) can be used as data source of identifying built-up areas. A new method of identifying high-accuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM) and digital orthophoto map (DOM) are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings) are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data). The proposed method is suitable for spaceborne and airborne stereo pairs and triplets.
DIGITAL ELEVATION MODEL INTERPOLATION BY FUSION OF MORPHOLOGICAL RECONSTRUCTION AND DISTANCE TRANSFORMATION
Authors: Shen J. , Han W. L. , Ge J. , Zhang L. B. , Tan H.
Published: September 2017

Abstract. Interpolation methods have significant impacts on the accuracy of the digital elevation model (DEM) from contours which are one of frequently employed data sources. In this paper, an interpolation method is presented to build DEM from contour lines by fusion/integration of morphological reconstruction and distance transformation with obstacles. Particularly, morphological reconstruction is used to get the elevation values of the higher contour lines and the lower contour lines of any a spatial point between two contour lines, and distance transformation with obstacles is used to get the geodesic distances of the spatial point to the higher contour lines and the lower contour lines respectively. At last, linear interpolation along water flow line is used to get the elevation values of the pixels to be interpolated. The experiment demonstrates that feasibility of our proposed method.
OPEN SOURCE WEB-BASED SOLUTIONS FOR DISSEMINATING AND ANALYZING FLOOD HAZARD INFORMATION AT THE COMMUNITY LEVEL
Authors: M.-M.-Santillan M. , Santillan J. R. , Morales E. M. O.
Published: September 2017

Abstract. We discuss in this paper the development, including the features and functionalities, of an open source web-based flood hazard information dissemination and analytical system called “Flood EViDEns”. Flood EViDEns is short for “Flood Event Visualization and Damage Estimations”, an application that was developed by the Caraga State University to address the needs of local disaster managers in the Caraga Region in Mindanao, Philippines in accessing timely and relevant flood hazard information before, during and after the occurrence of flood disasters at the community (i.e., barangay and household) level. The web application made use of various free/open source web mapping and visualization technologies (GeoServer, GeoDjango, OpenLayers, Bootstrap), various geospatial datasets including LiDAR-derived elevation and information products, hydro-meteorological data, and flood simulation models to visualize various scenarios of flooding and its associated damages to infrastructures. The Flood EViDEns application facilitates the release and utilization of this flood-related information through a user-friendly front end interface consisting of web map and tables. A public version of the application can be accessed at http://121.97.192.11:8082/. The application is currently expanded to cover additional sites in Mindanao, Philippines through the “Geo-informatics for the Systematic Assessment of Flood Effects and Risks for a Resilient Mindanao” or the “Geo-SAFER Mindanao” Program.
USING COUPLED NONNEGATIVE MATRIX FACTORIZATION (CNMF) UN-MIXING
FOR HIGH SPECTRAL AND SPATIAL RESOLUTION DATA FUSION TO ESTIMATE
URBAN IMPERVIOUS SURFACE AND URBAN ECOLOGICAL ENVIRONMENT
Authors: Wang T. , Zhang H. , Lin H.
Published: September 2017

Abstract. surfaces has increasingly roused widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, studies on the impervious surface using multi-spectral imageries is insufficient and inaccurate due to the complexity of urban infrastructures base on the need to further recognize these impervious surface materials in a finer scale. Hyperspectral imageries have been proved to be sensitive to subtle spectral differences thus capable to exquisitely discriminate these similar materials while limited to the low spatial resolution. Coupled nonnegative matrix factorization (CNMF) unmixing method is one of the most physically straightforward and easily complemented hyperspectral pan-sharpening methods that could produce fused data with both high spectral and spatial resolution. This paper aimed to exploit the latent capacity and tentative validation of CNMF on the killer application of mapping urban impervious surfaces in complexed metropolitan environments like Hong Kong. Experiments showed that the fusion of high spectral and spatial resolution image could provide more accurate and comprehensive information on urban impervious surface estimation.
AN IMPROVED PROGRESSIVE TRIANGULATION ALGORITHM FOR VEHICLE-BORNE LASER POINT CLOUD
Authors: Wei Z. , Ma H. , Chen X. , Liu L.
Published: September 2017

Abstract. The application of classical progressive triangulation filter algorithm for airborne point cloud is very successful, however, there is a big difference between airborne point cloud and vehicle-borne laser point cloud in spatial distribution, density and other aspects. In this paper, a lot of experiments are carried out to improve the filter algorithm for vehicle-borne laser point cloud, which includes as follows: (1) Establish grid index, such as 0.1 meters, only retain the lowest points, which can greatly reduce the number of suspected ground points, and the filtering efficiency is improved significantly; (2) According to the vehicle-borne height and track line, the road face points can be roughly determined. Then the convolution operation is used to ensure the real road points, which are also the ground points. This method cannot have to relax the filter parameters (which will lead to more non-ground points) and ensure the integrity of the road boundary; (3) A method named as "get more and remove some" is proposed for solving the filtering faults at the tail of every points segment caused by the incline scanning face. After the three steps, the filtering is improved obviously on qualification and processing speed.
“SLICE-ITERATION”, A METHOD FOR AUTOMATIC EXTRATION OF VEHICLE LASER POINT CLOUD
Authors: Wei Z. , Chen X. , Ma H. , Zhong R.
Published: September 2017

Abstract. In this paper a method “slicing-iteration” is proposed to realize automatic extraction of vehicle-borne laser point cloud, the main steps are as follows: (1) Confirm ground reference surface; (2) Dividing points: taking points at certain height range above reference surface, divide them by certain cell size (such as 0.1 m) and record points number of every cell; (3) Forming polyline: based on the number, for every cell, axially cluster is made, then find a neighbor with most similar number and form the two-point-line, further forming a polyline; (4) Iteration: dividing points and forming polyline can be conduct at different height range and by x/y axis, so as many features aspossible can be extracted. The method can also be used in half-automatic boundary extraction. Experiments show that the method in thepaper is easy to use, highly adaptive, with good extraction result.