bluuebunny/crossref_metadata_2025 huggingface dataset:
ANALYSIS OF HUMAN ACTIVITIES IN NATURE RESERVES BASED ON NIGHTTIME LIGHT REMOTE SENSING AND MICROBLOGGING DATA –ILLUSTRATED BY THE CASE OF NATIONAL NATURE RESERVES IN JIANGXI PROVINCE
Authors: Shi F. , Li X. , Xu H.
Published: September 2017

Abstract. The study used the mainstream social media in china - Sina microblogging data combined with nighttime light remote sensing and various geographical data to reveal the pattern of human activities and light pollution of the Jiangxi Provincial National Nature Reserves. Firstly, we performed statistical analysis based on both functional areas and km-grid from the perspective of space and time, and selected the key areas for in-depth study. Secondly, the relationship between microblogging data and nighttime light remote sensing, population, GDP, road coverage, road distance and road type in nature reserves was analyzed by Spearman correlation coefficient method, so the distribution pattern and influencing factors of the microblogging data were explored. Thirdly, a region where the luminance value was greater than 0.2 was defined as a light region. We evaluated the management status by analyzing the distribution of microblogging data in both light area and non-light area. Final results showed that in all nature reserves, the top three were the Lushan Nature Reserve, the Jinggangshan Nature Reserve, the Taohongling National Nature Reserve of Sikas both on the total number and density of microblogging ; microblogging had a significant correlation with nighttime light remote sensing , the GDP, population, road and other factors; the distribution of microblogging near roads in protected area followed power laws; luminous radiance of Lushan Nature Reserve was the highest, with 43 percent of region was light at night; analysis combining nighttime light remote sensing with microblogging data reflected the status of management of nature reserves.
ANALYSIS OF BIG DATA FROM SPACE
Authors: Tan J. , Osborne B.
Published: September 2017

Abstract. Massive data have been collected through various space mission. To maximize the investment, the data need to be exploited to the fullest. In this paper, we address key topics on big data from space about the status and future development using the system engineering method. First, we summarized space data including operation data and mission data, on their sources, access way, characteristics of 5Vs and application models based on the concept of big data, as well as the challenges they faced in application. Second, we gave proposals on platform design and architecture to meet the demand and challenges on space data application. It has taken into account of features of space data and their application models. It emphasizes high scalability and flexibility in the aspects of storage, computing and data mining. Thirdly, we suggested typical and promising practices for space data application, that showed valuable methodologies for improving intelligence on space application, engineering, and science. Our work will give an interdisciplinary knowledge to space engineers and information engineers.
A MODELING METHOD OF FLUTTERING LEAVES BASED ON POINT CLOUD
Authors: Tang J. , Wang Y. , Zhao Y. , Hao W. , Ning X. , Lv K. , Shi Z. , Zhao M.
Published: September 2017

Abstract. Leaves falling gently or fluttering are common phenomenon in nature scenes. The authenticity of leaves falling plays an important part in the dynamic modeling of natural scenes. The leaves falling model has a widely applications in the field of animation and virtual reality. We propose a novel modeling method of fluttering leaves based on point cloud in this paper. According to the shape, the weight of leaves and the wind speed, three basic trajectories of leaves falling are defined, which are the rotation falling, the roll falling and the screw roll falling. At the same time, a parallel algorithm based on OpenMP is implemented to satisfy the needs of real-time in practical applications. Experimental results demonstrate that the proposed method is amenable to the incorporation of a variety of desirable effects.
CHANGE ANALYSIS ON SOIL EROSION OF FUJIAN PROVINCE FROM 1990 TO 2015
Authors: Wang X. Q. , Zeng S. J. , Chen X. G. , Lin J. L. , Chen S. M.
Published: September 2017

Abstract. Soil erosion is one of major environment problems in the world, and China is one of the most serious soil erosion country. In this paper, Fujian province was used as a study area for its typical red soil region. Based on USLE model, the soil erosion modulus in 1990 and 2015 were calculated and turned to soil erosion intensity. The soil erosion distribution trend in Fujian province was decrease from south-east coastal zone to north-west inland region. In soil erosion areas, the main erosion type was light level with about 80 %, and the soil erosion levels above serious type were mainly sporadic distribution with less than 10 %. The soil erosion improved for the past 25 years. The areas of different erosion types all decreased, and the total erosion area reduced by 26.59 %. The improvement area mainly located in north-east, south and west region. The aggravation area mainly located in the north and some middle hilly regions. The impact of human activities is more significant for erosion control.
SPATIAL-TEMPORAL ANALYSIS OF OPENSTREETMAP DATA AFTER NATURAL
DISASTERS: A CASE STUDY OF HAITI UNDER HURRICANE MATTHEW
Authors: Xu J. , Li L. , Zhou Q.
Published: September 2017

Abstract. Volunteered geographic information (VGI) has been widely adopted as an alternative for authoritative geographic information in disaster management considering its up-to-date data. OpenStreetMap, in particular, is now aiming at crisis mapping for humanitarian purpose. This paper illustrated that natural disaster played an essential role in updating OpenStreetMap data after Haiti was hit by Hurricane Matthew in October, 2016. Spatial-temporal analysis of updated OSM data was conducted in this paper. Correlation of features was also studied to figure out whether updates of data were coincidence or the results of the hurricane. Spatial pattern matched the damaged areas and temporal changes fitted the time when disaster occurred. High level of correlation values of features were recorded when hurricane occurred, suggesting that updates in data were led by the hurricane.
COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN
IN PEARL RIVER DELTA
Authors: Zeng Y.
Published: September 2017

Abstract. Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.
THE RELATIVE IMPORTANCE OF THREE SPECIFIC CLIMATIC FACTORS ON NORTH AMERICAN BREEDING BIRD SPECIES RICHNESS
Authors: Zhang M. , Lin Y.
Published: September 2017

Abstract. Understanding of the relationships between bird species and environment facilitates protecting avian biodiversity and maintaining nature sustaining. However, the effects of many climatic factors on bird richness have not been fully grasped. To fill this gap, this study investigated the relationships between the richness of three typical North American breeding bird species and three climatic factors at the monthly scale. Based on the North American Breeding Bird Survey (BBS) data during 1967–2014, the relationships between the numbers of Carolina wren, Cerulean warbler, and Red-bellied woodpecker and the three climatic factors of precipitation, vapor pressure, and potential evapotranspiration were examined using the method of Pearson linear regression analysis. The results indicated that the three climatic factors have correlations with the richness of the breeding bird species but in different modes, e.g., strong correlations for the non-migratory species but weak correlations for the migratory species.
A CASE STUDY: EXPLORING INDUSTRIAL AGGLOMERATION OF MANUFACTURING INDUSTRIES IN SHANGHAI USING DURANTON AND OVERMAN’S K-DENSITY FUNCTION
Authors: Tian S. , Wang J. , Gui Z. , Wu H. , Wang Y.
Published: September 2017

Abstract. There has wide academic and policy attention on the issue of scale economy and industrial agglomeration, with most of the attention paid to industrial geography concentration. This paper adopted a scale-independent and distance-based measurement method, K-density function or known as Duranton and Overman (DO) index, to study the manufacturing industries localization in Shanghai, which is the most representative economic development zone in China and East Asia. The result indicates the industry has a growing tendency of localization, and various spatial distribution patterns in different distances. Furthermore, the class of industry also show significant influence on the concentration pattern. Besides, the method has been coded and published on GeoCommerce, a visualization and analysis portal for industrial big data, to provide geoprocessing and spatial decision support.
PRECISION AND DEVIATION COMPARISON BETWEEN ICESAT AND ENVISAT IN TYPICAL ICE GAINING AND LOSING REGIONS OF ANTARCTICA
Authors: Du W. , Chen L. , Xie H. , Hai G. , Zhang S. , Tong X.
Published: September 2017

Abstract. This paper analyzes the precision and deviation of elevations acquired from Envisat and The Ice, Cloud and Land Elevation Satellite (ICESat) over typical ice gaining and losing regions, i.e. Lambert-Amery System (LAS) in east Antarctica, and Amundsen Sea Sector (ASS) in west Antarctica, during the same period from 2003 to 2008. We used GLA12 dataset of ICESat and Level 2 data of Envisat. Data preprocessing includes data filtering, projection transformation and track classification. Meanwhile, the slope correction is applied to Envisat data and saturation correction for ICESat data. Then the crossover analysis was used to obtain the crossing points of the ICESat tracks, Envisat tracks and ICESat-Envisat tracks separately. The two tracks we chose for cross-over analysis should be in the same campaign for ICESat (within 33 days) or the same cycle for Envisat (within 35 days).The standard deviation of a set of elevation residuals at time-coincident crossovers is calculated as the precision of each satellite while the mean value is calculated as the deviation of ICESat-Envisat. Generally, the ICESat laser altimeter gets a better precision than the Envisat radar altimeter. For Amundsen Sea Sector, the ICESat precision is found to vary from 8.9 cm to 17 cm and the Envisat precision varies from 0.81 m to 1.57 m. For LAS area, the ICESat precision is found to vary from 6.7 cm to 14.3 cm and the Envisat precision varies from 0.46 m to 0.81 m. Comparison result between Envisat and ICESat elevations shows a mean difference of 0.43 ±7.14 m for Amundsen Sea Sector and 0.53 ± 1.23 m over LAS.
2D EVOLUTION PATTERN AND 3D RECONSTRUCTION OF TWO
LARGE RIFTS IN FILCHNER ICE SHELF FROM SATELLITE IMAGES
Authors: Xiao H. , Liu S. , Li R. , Tong X.
Published: September 2017

Abstract. The floating ice shelves surrounding the Antarctic Ice Sheet is important for the stability of the grounded ice-sheet. Fracturing and calving event of an ice shelf can damage the structural integrity and contributes to an increase in ice discharge to the ocean. In this study, we focus on two large newly-developed traverse rifts in the frontal region of Filchner Ice Shelf which may lead to next major calving in the region. Two-dimensional evolution of the two rifts were investigated in detail using multisource remote sensing data, Two abrupt extensions in 2006 and 2011 were found with a rate of more than 1 km d-1. Moreover, a stereo pair of ZY-3 images was utilized for the first time to reconstruct the 3D rift model where an average rift depth of 48 m and ice mélange rise of ~10 m were measured.
ICE WATER CLASSIFICATION USING STATISTICAL DISTRIBUTION BASED CONDITIONAL RANDOM FIELDS IN RADARSAT-2 DUAL POLARIZATION IMAGERY
Authors: Zhang Y. , Li F. , Zhang S. , Hao W. , Zhu T. , Yuan L. , Xiao F.
Published: September 2017

Abstract. In this paper, Statistical Distribution based Conditional Random Fields (STA-CRF) algorithm is exploited for improving marginal ice-water classification. Pixel level ice concentration is presented as the comparison of methods based on CRF. Furthermore, in order to explore the effective statistical distribution model to be integrated into STA-CRF, five statistical distribution models are investigated. The STA-CRF methods are tested on 2 scenes around Prydz Bay and Adélie Depression, where contain a variety of ice types during melt season. Experimental results indicate that the proposed method can resolve sea ice edge well in Marginal Ice Zone (MIZ) and show a robust distinction of ice and water.
MEASUREMENT ERROR WITH DIFFERENT COMPUTER VISION TECHNIQUES
Authors: Icasio-Hernández O. , Curiel-Razo Y. I. , Almaraz-Cabral C. C. , Rojas-Ramirez S. R. , González-Barbosa J. J.
Published: September 2017

Abstract. The goal of this work is to offer a comparative of measurement error for different computer vision techniques for 3D reconstruction and allow a metrological discrimination based on our evaluation results. The present work implements four 3D reconstruction techniques: passive stereoscopy, active stereoscopy, shape from contour and fringe profilometry to find the measurement error and its uncertainty using different gauges. We measured several dimensional and geometric known standards. We compared the results for the techniques, average errors, standard deviations, and uncertainties obtaining a guide to identify the tolerances that each technique can achieve and choose the best.
AN INDOOR SLAM METHOD BASED ON KINECT AND MULTI-FEATURE
EXTENDED INFORMATION FILTER
Authors: Chang M. , Kang Z.
Published: September 2017

Abstract. Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.
INDOOR MODELLING FROM SLAM-BASED LASER SCANNER: DOOR DETECTION TO ENVELOPE RECONSTRUCTION
Authors: Díaz-Vilariño L. , Verbree E. , Zlatanova S. , Diakité A.
Published: September 2017

Abstract. Updated and detailed indoor models are being increasingly demanded for various applications such as emergency management or navigational assistance. The consolidation of new portable and mobile acquisition systems has led to a higher availability of 3D point cloud data from indoors. In this work, we explore the combined use of point clouds and trajectories from SLAM-based laser scanner to automate the reconstruction of building indoors. The methodology starts by door detection, since doors represent transitions from one indoor space to other, which constitutes an initial approach about the global configuration of the point cloud into building rooms. For this purpose, the trajectory is used to create a vertical point cloud profile in which doors are detected as local minimum of vertical distances. As point cloud and trajectory are related by time stamp, this feature is used to subdivide the point cloud into subspaces according to the location of the doors. The correspondence between subspaces and building rooms is not unambiguous. One subspace always corresponds to one room, but one room is not necessarily depicted by just one subspace, for example, in case of a room containing several doors and in which the acquisition is performed in a discontinue way. The labelling problem is formulated as combinatorial approach solved as a minimum energy optimization. Once the point cloud is subdivided into building rooms, envelop (conformed by walls, ceilings and floors) is reconstructed for each space. The connectivity between spaces is included by adding the previously detected doors to the reconstructed model. The methodology is tested in a real case study.
THE ISPRS BENCHMARK ON INDOOR MODELLING
Authors: Khoshelham K. , Díaz Vilariño L. , Peter M. , Kang Z. , Acharya D.
Published: September 2017

Abstract. Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at: http://www2.isprs.org/commissions/comm4/wg5/benchmark-on-indoor-modelling.html.
TOWARDS INNOVATIVE GEOSPATIAL TOOLS FOR FIT-FOR-PURPOSE LAND RIGHTS MAPPING
Authors: Koeva M. , Bennett R. , Gerke M. , Crommelinck S. , Stöcker C. , Crompvoets J. , Ho S. , Schwering A. , Chipofya M. , Schultz C. , Zein T. , Biraro M. , Alemie B. , Wayumba R. , Kundert K.
Published: September 2017

Abstract. In large parts of sub Saharan Africa it remains an ongoing challenging to map millions of unrecognized land rights. Existing approaches for recognizing these rights have proven inappropriate in many cases. A new generation of tools needs to be developed to support faster, cheaper, easier, and more responsible land rights mapping. This is the main goal of its4land, an European Commission Horizon 2020 project that aims to develop innovative tools inspired by the continuum of land rights, fit-for-purpose land administration, and cadastral intelligence. its4land is using strategic collaboration between the EU and East Africa to deliver innovative, scalable, and transferrable ICT solutions. The innovation process incorporates a broad range of stakeholders and emergent geospatial technologies, including smart sketchmaps, UAVs, automated feature extraction, as well as geocloud services. The aim is to combine innovative technologies, capture the specific needs, market opportunities and readiness of end-users in the domain of land tenure information recording in Eastern Africa. The project consists of a four year work plan, € 3.9M funding, and eight consortium partners collaborating with stakeholders from six case study locations in Ethiopia, Kenya, and Rwanda. The major tasks include tool development, prototyping, and demonstration for local, national, regional, and international interest groups. The case locations cover different land uses such as: urban, peri-urban, rural smallholder, and (former) pastoralist. This paper describes the project’s activities within the first 18 months and covers barriers discovered, lessons learned and results achieved.
RESEARCH ON KNOWLEDGE-BASED OPTIMIZATION METHOD OF INDOOR LOCATION BASED ON LOW ENERGY BLUETOOTH
Authors: Li C. , Li G. , Deng Y. , Wang T. , Kang Z.
Published: September 2017

Abstract. With the rapid development of LBS (Location-based Service), the demand for commercialization of indoor location has been increasing, but its technology is not perfect. Currently, the accuracy of indoor location, the complexity of the algorithm, and the cost of positioning are hard to be simultaneously considered and it is still restricting the determination and application of mainstream positioning technology. Therefore, this paper proposes a method of knowledge-based optimization of indoor location based on low energy Bluetooth. The main steps include: 1) The establishment and application of a priori and posterior knowledge base. 2) Primary selection of signal source. 3) Elimination of positioning gross error. 4) Accumulation of positioning knowledge. The experimental results show that the proposed algorithm can eliminate the signal source of outliers and improve the accuracy of single point positioning in the simulation data. The proposed scheme is a dynamic knowledge accumulation rather than a single positioning process. The scheme adopts cheap equipment and provides a new idea for the theory and method of indoor positioning. Moreover, the performance of the high accuracy positioning results in the simulation data shows that the scheme has a certain application value in the commercial promotion.
PHYSICAL SEISMIC VULNERABILITY ASSESSMENT OF TEHRAN USING THE
INTEGRATION OF GRANULAR COMPUTING AND INTERVAL DEMPSTER- SHAFER
Authors: Delavar M. R. , Bahrami M. , Zare M.
Published: September 2017

Abstract. Several faults exist in the vicinity of Tehran, the capital of Iran such as North Tehran, Ray, Mosha and Kahrizak. One way to assist reducing the damage caused by the earthquake is the production of a seismic vulnerability map. The study area in this research is Tehran, based on the assumption of the activation of North Tehran fault. Degree of Physical seismic vulnerability caused by the earthquake depends on a number of criteria. In this study the intensity of the earthquake, land slope, numbers of buildings’ floors as well as their materials are considered as the effective parameters. Hence, the production of the seismic vulnerability map is a multi criteria issue. In this problem, the main source of uncertainty is related to the experts’ opinions regarding the seismic vulnerability of Tehran statistical units. The main objectives of this study are to exploit opinions of the experts, undertaking interval computation and interval Dempster-Shafer combination rule to reduce the uncertainty in the opinions of the experts and customizing granular computing to extract the rules and to produce Tehran physical seismic vulnerability map with a higher confidence. Among 3174 statistical units of Tehran, 150 units were randomly selected and using interval computation, their physical vulnerabilities were determined by the experts in earthquake-related fields. After the fusion of the experts’ opinions using interval Dempster-Shafer, the information table is prepared as the input to granular computing and then rules are extracted with minimum inconsistency. Finally, the seismic physical vulnerability map of Tehran was produced with % 72 accuracy.
TOPOLOGICAL RELATIONS-BASED DETECTION OF SPATIAL INCONSISTENCY IN GLOBELAND30
Authors: Kang S. , Chen J. , Peng S.
Published: September 2017

Abstract. Land cover is one of the fundamental data sets on environment assessment, land management and biodiversity protection, etc. Hence, data quality control of land cover is extremely critical for geospatial analysis and decision making. Due to the similar remote-sensing reflectance for some land cover types, omission and commission errors occurred in preliminary classification could result to spatial inconsistency between land cover types. In the progress of post-classification, this error checking mainly depends on manual labour to assure data quality, by which it is time-consuming and labour intensive. So a method required for automatic detection in post-classification is still an open issue. From logical inconsistency point of view, an inconsistency detection method is designed. This method consist of a grids extended 4-intersection model (GE4IM) for topological representation in single-valued space, by which three different kinds of topological relations including disjoint, touch, contain or contained-by are described, and an algorithm of region overlay for the computation of spatial inconsistency. The rules are derived from universal law in nature between water body and wetland, cultivated land and artificial surface. Through experiment conducted in Shandong Linqu County, data inconsistency can be pointed out within 6 minutes through calculation of topological inconsistency between cultivated land and artificial surface, water body and wetland. The efficiency evaluation of the presented algorithm is demonstrated by Google Earth images. Through comparative analysis, the algorithm is proved to be promising for inconsistency detection in land cover data.
ANALYZING THE SPATIOTEMPORAL DISTRIBUTION OF DIFFERENT INDUSTRIES IN WUHAN CITY USING ENTERPRISE REGISTRATION DATA
Authors: Cui Z. , Xie G. , Gui Z. , Wu H.
Published: September 2017

Abstract. Enterprises are the basis of urban economic development and an essential factor that affects urban structure. It is of great significance to study the spatiotemporal distribution of enterprises to benefit cities planning and development. Based on the enterprises registration data of Administrator for Industry and Commerce of Wuhan from 1996 to 2007, in this paper, we analysed the spatiotemporal distribution of enterprises in Wuhan city. We divided Wuhan into 2,356 square grids with a length of 2 km and counted the number of enterprises of different industry categories in each grid. Then we calculated the aggregation intensity index of enterprises in each grid. With the method of global spatial autocorrelation analysis, we discovered the change of the degree of enterprise agglomeration for different industry categories in different periods. We also used local autocorrelation analysis to find the hotspots of enterprise distribution areas furtherly and discover the changes of the location of the hotspots during each period. Based on the two analyses and context information of Wuhan city, we can know the features of spatiotemporal distribution of enterprises. It can be concluded that the spatial distribution of enterprises could be reflected directly by spatial autocorrelation analysis. Moreover, the enterprises registration data with fine-grained enterprises spatiotemporal information can support the research of spatial distribution and evolution of enterprises. This study can show how different industry categories developed and provide reference to urban geography to help cities plan the distribution of enterprises and help enterprises decide its location.
VISUALIZING THE QUALITY OF VECTUR FEATURES – A PROPOSAL FOR
CADASTRAL MAPS
Authors: Navratil G. , Leopoldseder V.
Published: September 2017

Abstract. A well-known problem of geographical information is the communication of the quality level. It can be either done verbally / numerically or it can be done graphically. The graphical form is especially useful if the quality has a spatial variation because the spatial distribution is visualized as well. The problem of spatial variation of quality is an issue for cadastral maps. Non-experts cannot determine the quality at a specific location. Therefore a visual representation was tested for the Austrian cadastre. A map sheet was redesigned to give some indication of cadastral quality and presented to both experts and non-experts. The paper presents the result of the interviews.
THE STUDY OF SPECTRUM RECONSTRUCTION BASED ON FUZZY SET FULL CONSTRAINT AND MULTIENDMEMBER DECOMPOSITION
Authors: Sun Y. , Lin Y. , Hu X. , Zhao S. , Liu S. , Tong Q. , Helder D. , Yan L.
Published: September 2017

Abstract. Hyperspectral imaging system can obtain spectral and spatial information simultaneously with bandwidth to the level of 10 nm or even less. Therefore, hyperspectral remote sensing has the ability to detect some kinds of objects which can not be detected in wide-band remote sensing, making it becoming one of the hottest spots in remote sensing. In this study, under conditions with a fuzzy set of full constraints, Normalized Multi-Endmember Decomposition Method (NMEDM) for vegetation, water, and soil was proposed to reconstruct hyperspectral data using a large number of high-quality multispectral data and auxiliary spectral library data. This study considered spatial and temporal variation and decreased the calculation time required to reconstruct the hyper-spectral data. The results of spectral reconstruction based on NMEDM showed that the reconstructed data has good qualities and certain applications, which makes it possible to carry out spectral features identification. This method also extends the application of depth and breadth of remote sensing data, helping to explore the law between multispectral and hyperspectral data.
DATA PROCESSING AND ANALYSIS TOOLS BASED ON GROUND-BASED SYNTHETIC APERTURE RADAR IMAGERY
Authors: Crosetto M. , Monserrat O. , Luzi G. , Devanthéry N. , Cuevas-González M. , Barra A.
Published: September 2017

Abstract. The Ground-Based SAR (GBSAR) is a terrestrial remote sensing technique used to measure and monitor deformation. In this paper we describe two complementary approaches to derive deformation measurements using GBSAR data. The first approach is based on radar interferometry, while the second one exploits the GBSAR amplitude. In this paper we consider the so-called discontinuous GBSAR acquisition mode. The interferometric process is not always straightforward: it requires appropriate data processing and analysis tools. One of the main critical steps is phase unwrapping, which can critically affect the deformation measurements. In this paper we describe the procedure used at the CTTC to process and analyse discontinuous GBSAR data. In the second part of the paper we describe the approach based on GBSAR amplitude images and an image-matching method.
INVESTIGATION OF LITHOSPHERIC STRUCTURE IN MONGOLIA: INSIGHTS FROM INSAR OBSERVATIONS AND MODELLING
Authors: Jing Z. , Bihong F. , Pilong S. , Qiang G.
Published: September 2017

Abstract. The western Mongolia is a seismically active intracontinental region, with ongoing tectonic deformation and widespread seismicity related to the far-field effects of India-Eurasia collision. During the 20th century, four earthquakes with the magnitude larger than 8 occurred in the western Mongolia and its surrounding regions, providing a unique opportunity to study the geodynamics of intracontinental tectonic deformations. The 1957 magnitude 8.3 Gobi-Altai earthquake is one of the largest seismic events. The deformation pattern of rupture zone associated with this earthquake is complex, involving left-lateral strike-slip and reverse dip-slip faulting on several distinct geological structures in a 264 × 40 km wide zone. To understand the relationship between the observed postseismic surface deformation and the rheological structure of the upper lithosphere, Interferometric Synthetic Aperture Radar (InSAR) data are used to study the 1957 earthquake. Then we developed a postseismic model in a spherical, radially layered elastic-viscoelastic Earth based on InSAR results, and further analysed the dominant contribution to the surface deformation. This work is important for understanding not only the regional tectonics, but also the structure and dynamics of the lithosphere. SAR data were acquired from the ERS1/2 and Envisat from 1996 to 2010. Using the Repeat Orbit Interferometry Package (ROI_PAC), 124 postseismic interferograms are produced on four adjacent tracks. By stacking these interferograms, the maximum InSAR line-of-sight deformation rate along the Gobi-Altai fault zone is obtained. The main results are as follows: (1) The maximum InSAR line-of-sight deformation velocity along this large fault zone is about 6 mm/yr; (2) The modelled surface deformation suggests that the viscoelastic relaxation is the most reasonable mechanism to explain the observed surface motion; (3) The optimal model cover the Gobi-Altai seismogenic thickness is 10 km; (4) The lower bound of Maxwell viscosity of lower crust and upper mantle is approximately 9 × 1019 Pa s, and the Maxwell relaxation time corresponding to this viscosity is 95.13 years.
THE RESEARCH ON ACCURACY EVALUATION METHOD OF THE TOMOGRAPHIC SAR THREE-DIMENSIONAL RECONSTRUCTION OF URBAN BUILDING BASED ON TERRESTRIAL LIDAR POINT CLOUD
Authors: Li L. , Pang L. , Zhang X. D. , Liu H.
Published: September 2017

Abstract. Muti-baseLine SAR tomography can be used on 3D reconstruction of urban building based on SAR images acquired. In the near future, it is expected to become an important technical tool for urban multi-dimensional precision monitoring. For the moment，There is no effective method to verify the accuracy of tomographic SAR 3D point cloud of urban buildings. In this paper, a new method based on terrestrial Lidar 3D point cloud data to verify the accuracy of the tomographic SAR 3D point cloud data is proposed, 3D point cloud of two can be segmented into different facadeds. Then facet boundary extraction is carried out one by one, to evaluate the accuracy of tomographic SAR 3D point cloud of urban buildings. The experience select data of Pangu Plaza to analyze and compare, the result of experience show that the proposed method that evaluating the accuracy of tomographic SAR 3D point clou of urban building based on lidar 3D point cloud is validity and applicability
A SAR IMAGE REGISTRATION METHOD BASED ON SIFT ALGORITHM
Authors: Lu W. , Yue X. , Zhao Y. , Han C.
Published: September 2017

Abstract. In order to improve the stability and rapidity of synthetic aperture radar (SAR) images matching, an effective method was presented. Firstly, the adaptive smoothing filtering was employed for image denoising in image processing based on Wallis filtering to avoid the follow-up noise is amplified. Secondly, feature points were extracted by a simplified SIFT algorithm. Finally, the exact matching of the images was achieved with these points. Compared with the existing methods, it not only maintains the richness of features, but a-lso reduces the noise of the image. The simulation results show that the proposed algorithm can achieve better matching effect.
URBAN MODELLING PERFORMANCE OF NEXT GENERATION SAR MISSIONS
Authors: Sefercik U. G. , Yastikli N. , Atalay C.
Published: September 2017

Abstract. In synthetic aperture radar (SAR) technology, urban mapping and modelling have become possible with revolutionary missions TerraSAR-X (TSX) and Cosmo-SkyMed (CSK) since 2007. These satellites offer 1m spatial resolution in high-resolution spotlight imaging mode and capable for high quality digital surface model (DSM) acquisition for urban areas utilizing interferometric SAR (InSAR) technology. With the advantage of independent generation from seasonal weather conditions, TSX and CSK DSMs are much in demand by scientific users. The performance of SAR DSMs is influenced by the distortions such as layover, foreshortening, shadow and double-bounce depend up on imaging geometry. In this study, the potential of DSMs derived from convenient 1m high-resolution spotlight (HS) InSAR pairs of CSK and TSX is validated by model-to-model absolute and relative accuracy estimations in an urban area. For the verification, an airborne laser scanning (ALS) DSM of the study area was used as the reference model. Results demonstrated that TSX and CSK urban DSMs are compatible in open, built-up and forest land forms with the absolute accuracy of 8–10 m. The relative accuracies based on the coherence of neighbouring pixels are superior to absolute accuracies both for CSK and TSX.
MINING LAND SUBSIDENCE MONITORING USING SENTINEL-1 SAR DATA
Authors: Yuan W. , Wang Q. , Fan J. , Li H.
Published: September 2017

Abstract. In this paper, DInSAR technique was used to monitor land subsidence in mining area. The study area was selected in the coal mine area located in Yuanbaoshan District, Chifeng City, and Sentinel-1 data were used to carry out DInSAR techniqu. We analyzed the interferometric results by Sentinel-1 data from December 2015 to May 2016. Through the comparison of the results of DInSAR technique and the location of the mine on the optical images, it is shown that DInSAR technique can be used to effectively monitor the land subsidence caused by underground mining, and it is an effective tool for law enforcement of over-mining.
MEASURING THE COSEISMIC DISPLACEMENTS OF 2010 Ms7.1 Yushu
EARTHQUAKE BY USING SAR AND HIGH RESOLUTION OPTICAL SATELLITE
IMAGES
Authors: Zhang L. , Wu J. , Shi F.
Published: September 2017

Abstract. After the 2010, Mw7.1, Yushu earthquake, many researchers have conducted detail investigations of the surface rupture zone by optical image interpretation, field surveying and inversion of seismic waves. However, how larger of the crustal deformation area caused by the earthquake and the quantitative co-seismic displacements are still not available. In this paper, we first take advantage of D-InSAR, MAI, and optical image matching methods to determine the whole co-seismic displacement fields. Two PALSAR images and two SPOT5 images before and after the earthquake are processed and the co-seismic displacements at the surface rupture zone and far field are obtained. The results are consistent with the field investigations, which illustrates the rationality of the application of optical image matching technology in the earthquake.
AN AUTOMATIC FILTER ALGORITHM FOR DENSE IMAGE MATCHING POINT
CLOUDS
Authors: Dong Y. Q. , Zhang L. , Cui X. M. , Ai H. B.
Published: September 2017

Abstract. Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.
EFFECTS OF THE RAINSTORM ON URBAN ROAD TRAFFIC SPEED — A CASE STUDY OF SHENZHEN, CHINA
Authors: Li Q. , Hao X. , Wang W. , Wu A. , Xie Z.
Published: September 2017

Abstract. The adverse weather may significantly impact urban traffic speed and travel time. Understanding the influence of the rainstorm to urban traffic speed is of great importance for traffic management under stormy weather. This study aims to investigate the impact of rainfall intensity on traffic speed in the Shenzhen (China) during the period 1 July 2015–31 August 2016. The analysis was carried out for five 1-h periods on weekdays during the morning periods (6:00 AM–11:00 AM). Taxi-enabled GPS tracking data obtained from Shenzhen city are used in the analysis. There are several findings in this study. Firstly, nearly half of the roads are significantly affected by the rainstorm. Secondly, the proportion of positive correlated roads is about 35 %, but there still are some roads with uncorrelated traffic speed variation rates (SVR) and rainfall intensities. Thirdly, the impact of the rainstorm on traffic speed is not homogeneous but with obvious spatial difference. This research provides useful information that can be used in traffic management on a city-wide scale under stormy weather.
A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE
Authors: Gu H. Y. , Li H. T. , Liu Z. Y. , Shao C. Y.
Published: September 2017

Abstract. Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomings of existing machine learning method on insufficient usage of prior knowledge, and improve the versatility of rule sets. A two-step workflow has been introduced, firstly, an initial rule is built based on Random Forest and CART decision tree. Secondly, the initial rule is analyzed and validated based on human knowledge, where we use statistical confidence interval to determine its threshold. The test site is located in Potsdam City. We utilised the TOP, DSM and ground truth data. The results show that the method could determine rule set for Land Cover classification semi-automatically, and there are static features for different land cover classes.
A NEW 2D OTSU FOR WATER EXTRACTION FROM SAR IMAGE
Authors: Guo Y. , Zhang J.
Published: September 2017

Abstract. SAR image segmentation is a crucial step that heavily influences the performance of image interpretation. The texture factor to replace the neighborhood mean dimension in the traditional Otsu method is proposed in this work, aiming at the problem that the SAR image has unique characteristics and the original 2D Otsu method only considers the pixel neighborhood mean information. In this paper, TerraSAR image with the single band and single polarization is used to water extraction. Firstly, the semantic function is used to analyze the structural characteristics of the sample image to determine the optimal parameters of the texture information extraction. Then, calculate the textural measures such as contrast, entropy, homogeneity, mean and second moment based on gray level co-occurrence matrix(GLCM) method. The results are compared with the artificially marked images and the results of the original 2D Otsu.The experimental results achieve higher objective values, which shows the proposed algorithm using texture factor has a high practical value for SAR Image water segmentation.
AUTOMATIC OBJECT-ORIENTED ROUNDABOUTS EXTRCTION FROM HIGH RESOLUTION MULTISPECTRAL IMAGES
Authors: Li X. , Zhang W.
Published: September 2017

Abstract. Road roundabouts, a typical class of road facilities to avoid collision, are generally not directed extracted in existing road extraction methods. This paper presents a novel four-step approach for automatic vegetated roundabout extractions from high resolution multispectral satellite images, which combines object-oriented extraction, Support Vector Machine (SVM) classification and spatial relationship estimation. Firstly, after proper preconditioning, the vegetated roundabouts are extracted by object-oriented extract algorithm in ENVI with rules that simultaneously taking area, roundness and vegetation index (NDVI) into consideration. After a certain number of experiments, the set of three items’ thresholds can be found, which may stand as the general rules for vegetated roundabouts extraction in similar conditions. Next, the roads are classified using Support Vector Machine (SVM) and the outputs are several band shaped polygons. Then, the holes in road polygons will be detected by examining the topological relation in ArcGIS. Lastly, since the margin of extracted roundabout and the biggest detected hole may not strictly coincide, by comparing the distance between central points of both the extracted roundabout and the hole with the threshold, convincing determination can be made. The proposed automatic approach has been proved to have very high production accuracy that all above 85 % in each case of the test set, which is good enough for automatic vegetated roundabouts extraction from high resolution remote sensing images without manual interpretation.
EXTRACTION OF HUMAN SETTLEMENTS FROM HIGH RESOLUTION REMOTE SENSING IMAGERY BY FUSING BOTH RIGHT-ANGLE CORNERS AND RIGHT-ANGLE SIDES
Authors: Lin X. G. , Ning X. G.
Published: September 2017

Abstract. A method for human settlements extraction from high resolution remote sensing imagery using feature-level-based fusion of right-angle-corners and right-angle-sides is proposed in this paper. First, the corners and line segments are detected, the right-angle-corners and right-angle-sides are determined by cross verification of the detected corners and line segments, and these two types of features are rasterized. Second, a human settlement index image is built based on the density and distance of the right-angle-corners and right-angle-sides in a local region. Finally, the polygons of human settlements are generated through binary thresholding of the index image, conversion from raster format to vector format, and sieving. Three images are used for testing the proposed method. The experimental results suggest that our proposed method has higher accuracy than the existed method. Specifically, the correctness, completeness, and quality of our method are 6.76 %, 10.12 %, 12.14 % respectively higher than the existed method.
CLASSIFICATION OF HYPERSPECTRAL DATA BASED ON GUIDED FILTERING AND RANDOM FOREST
Authors: Ma H. , Feng W. , Cao X. , Wang L.
Published: September 2017

Abstract. Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.