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    Ecological Security of Wetland in Chang-Zhu-Tan Urban Agglomeration
    LIAO Liuwen,QIN Jianxin
    Journal of Geo-information Science    2016, 18 (9): 1217-1226.   DOI: 10.3724/SP.J.1047.2016.01217
    Abstract1140)   HTML47)    PDF (1944KB)(7670)      

    This paper takes Chang-Zhu-Tan Urban Agglomeration as study area. Based on the vegetation index and land use data interpreted from Landsat TM images and combined with the population, economic and climate data, a framework model was established. The results show that: (1) the mean values of wetland ecological security index in 2000, 2005 and 2010 for Chang-Zhu-Tan Urban Agglomeration were 0.7268, 0.7151 and 0.7196, respectively. The status of regional wetland ecological security was good, and the ecological security degree was relatively safe. In the recent decade, the overall performance of the regional ecological security index had decreased, and the decrease of the corresponding area was 21577 km2, which accounted for 22.28% of the total land area; (2) in this study area, there is obvious difference of regional ecological security, that the first-class wetland ecological safety area mainly distributed in the surrounding regions of Dongting Lake, the second-class ecological safety region distributed along the major rivers, the third-class ecological safety area mainly distributed in the border area of two or three cities, and the fourth-class wetland ecological safety area mainly distributed in Yueyang city, Xiangtan city, Changsha city and Hengyang city. (3) During the study period, the area of wetland in Chang-Zhu-Tan Urban Agglomeration has changed obviously, that the total area of wetland has decreased year after year. The fractal dimension of forest swamp, herbaceous swamp, lake, river and paddy field showed an increasing trend. The fragmentation index of ponds/rivers was significantly higher than that of other types of landscape, and the value of wetland ecosystem services was decreasing in general. Finally, the main factors affecting the ecological security of wetland were analyzed from the aspects of land use change and transition, and wetland landscape structure and function.

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    Cited: CSCD(8)
    Validation of AVHRR, IMS and MODIS Snow Cover Products in North of China
    CAO Dongjie,ZHENG Zhaojun,TANG Shihao,WANG Yuanxiang
    Journal of Geo-information Science    2015, 17 (11): 1341-1347.   DOI: 10.3724/SP.J.1047.2015.01341
    Abstract1043)   HTML21)    PDF (15481KB)(4699)      

    Comparing with other satellite sensors, AVHRR has the capability to analyze more than 10 years of medium-resolution satellite imagery on a daily basis. AVHRR thereby holds a great potential to detect, map and quantify long-term environmental changes. However, different satellites use different retrieval algorithms, wavelength bandwidths and atmospheric validations. So it is important to compare different snow cover products retrieved by different satellites. Here, we describe and extensively validate the snow cover products of the historical 0.05°×0.05° AVHRR data. The spatial and seasonal validation includes a comparison with IMS and MOD10A1. It is found that the AVHRR snow products are in good accordance with the MODIS snow products. The influence of acquisition geometry and the sensor-to-sensor consistency will be discussed in future.

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    Cited: CSCD(1)
    Full Three-Dimensional GIS and Its Key Roles in Smart City
    zhu qing
       2014, 16 (2): 151-157.   DOI: 10.3724/SP.J.1047.2014.00151
    Abstract1436)      PDF (3709KB)(3842)      

    Three-dimensional GIS (3D GIS) is one of the primary and typical contents of GIS technology at present and in the future, which overcomes the constraints of representing 3D GIS spatial information in two-dimensional map, as well as provides a more effective decision-making support for people's daily life. This paper focuses on the research progress and its key technologies of 3D GIS, including the data model, database management and visual analysis. The pilot applications of 3D GIS in Wuhan are also illustrated. The entire 3D space of the city is represented by 3D GIS. Then construction of the large-scale city digitalization is enabled with the improvement of city management. Finally, the applications of 3D GIS for spatio-temporal information bearing engine and spatial intelligence in smart city and city safety are investigated.

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    Cited: CSCD(27)
    Setting Parameters and Choosing Optimum Semivariogram Models of Ordinaty Kriging Interpolation ——A case study of spatial interpolation to January average temperature of Fujian province
    WU Xuewen, YAN Luming
       2007, 9 (3): 104-108.  
    Abstract814)      PDF (688KB)(3586)      
    This article discusses about the thereunder of choosing the optimum semivariogram models and setting the key parameters based on ARCGIS and GS+software from characteristics and laws of data through understanding the ordinary Kriging interpolation theory, and carries through an in-depth exploratory spatial data analysis taking the spatial interpolation to January average temperature of Fujian province as an example, using the obtained parameters and semivariogram models to simulate the spatial distribution of January average temperature of Fujian province. The aticle offers a clear way for reasonable spatial interpolation.
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    Research on Human Mobility in Big Data Era
    LU Feng,LIU Kang,CHEN Jie
    Journal of Geo-information Science    2014, 16 (5): 665-672.   DOI: 10.3724/SP.J.1047.2014.00665
    Abstract1519)   HTML37)    PDF (873KB)(3551)      

    Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.

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    Cited: CSCD(33)
    On Space-Air-Ground Integrated Earth Observation Network
    LI Deren
       2012, 14 (4): 419-425.   DOI: 10.3724/SP.J.1047.2012.00419
    Abstract974)      PDF (1423KB)(3255)      

    Space-Air-Ground integrated earth observation network (SAGIEON) is not only the most promising high-tech area, but also a fundamental infrastructure closely related to national security and economic/social development. Firstly, the scientific concept, key technologies, current situation and tendency of SAGIEON are comprehensively represented. Secondly, an integrated data processing system for native remote sensing satellites is introduced, including its objectives and key technologies. Thirdly, the connotation of generalized spatial information grid are proposed on the basis of the above mentioned discussions. Finally, some conclusions are drawn. For the propose of providing fast, precise and real-time spatial information service to everyone, it is very important to carry out research on the theories and technologies of SAGIEON.

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    Cited: CSCD(22)
    Review on High Resolution Remote Sensing Image Classification and Recognition
    LIU Yang,FU Zhengye,ZHENG Fengbin
    Journal of Geo-information Science    2015, 17 (9): 1080-1091.   DOI: 10.3724/SP.J.1047.2015.01080
    Abstract2804)   HTML20)    PDF (5897KB)(3151)      

    Target classification and recognition (TCR) of high resolution remote sensing image is an important approach of image analysis, for the understanding of earth observation system (EOS), and for extracting information from the automatic target recognition (ATR) system, which has important values in military and civil fields. This paper reviews the latest progress and key technologies between domestic and international remote sensing image TCR in optical, infrared, synthetic aperture radar (SAR) and synthetic aperture sonar (SAS). The main research levels and the contents of high resolution remote sensing image TCR are firstly discussed. Then, the key technologies and their existing problems of high resolution remote sensing image TCR are deeply analyzed, from aspects such as filtering and noise reduction, feature extraction, target detection, scene classification, target classification and target recognition. Finally, combined with the related technologies including parallel computing, neural computing and cognitive computing, the new methods of TCR are discussed. Specifically, the main framework includes three aspects, which are detailed in the following. Firstly, the predominant techniques of high resolution remote sensing image processing are discussed based on high performance parallel computing. And the hybrid parallel architecture of high resolution remote sensing image processing based on Apache Hadoop, open multi-processing (OpenMP) and compute unified device architecture (CUDA) are also presented in this paper. Secondly, application prospects of TCR accuracy promotion are analyzed based on a thorough study of neuromorphic computing, and the method of multi-level remote sensing image target recognition based on the deep neural network (DNN) is introduced. Thirdly, the model and algorithm of big data uncertainty analysis for remote sensing images are discussed based on probabilistic graphical model (PGM) of cognitive computing, and the multi-scale remote sensing image scene description is given based on hierarchical topic model (HTM). Moreover, according to the related research of multi-media neural cognitive computing (MNCC), we discuss the development trend and research direction of TCR for remote sensing images big data in the future.

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    Cited: CSCD(18)
    Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data
    LIU Xiang, LIU Rong-Gao, CHEN Jing-Meng, CHENG Xiao, ZHENG Guang
       2013, 15 (5): 734-743.   DOI: 10.3724/SP.J.1047.2013.00734
    Abstract827)      PDF (416KB)(2961)      

    Leaf area index (LAI) is a primary parameter for charactering leaf density and vegetation structure. Since it could represent the capability of vegetation for photosynthesis, respiration and transpiration, LAI is used as a critical parameter for modeling water, carbon and energy exchanges among soil, vegetation and the atmosphere. Several regional and global LAI datasets have been generated from satellite observations. This paper reviews current status of theoretical background, algorithms, products and evaluation of LAI from optical remote sensing data. First, the definition of LAI and its effects in ecosystem modeling are introduced. Then, the radiative transfer processes of photon in canopy are described briefly. Based on these processes, vegetation presents its own spectral response characteristics, which are related to biophysical and biochemical properties of leaves, canopy and soil background, making it possible to derive LAI from optical remote sensing data. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation index-based empirical regression method and physical model-based method. These two methods are presented subsequently, and their advantages and disadvantages are also discussed. Several major global LAI remote sensing products are reviewed, such as MOD15, CYCLOPES, GLOBCARBON and GLOBMAP LAI. The methods for LAI products evaluation and validation are presented, and several problems in LAI evaluation are also discussed. Finally, several problems in LAI retrieval are concluded, and directions for future research of LAI retrieval are then suggested.

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    Cited: CSCD(43)
    Research of Lightweight Vector Geographic Data Management Based on Main Memory Database Redis
    ZHU Jin, HU Bin, SHAO Hua, LUO Qing, JIANG Nan, ZHANG Jingyun
       2014, 16 (2): 165-172.   DOI: 10.3724/SP.J.1047.2014.00165
    Abstract1266)      PDF (1665KB)(2788)      

    Effective organization and management of vector geographic data is one of the key parts for spatial database application. The traditional vector geographic data service is usually based on magnetic disks and relational databases like Oracle Spatial. With the rapid development of wireless communication and mobile web technology, the performance of current vector geographic data service is declining dramatically under multi-user concurrent access, and can't meet the requirements of high performance and high concurrency. In order to improve the performance of vector geographic data service under multi-user concurrent access, we proposed a novel management approach of lightweight vector geographic data based on main memory database Redis. Redis is a main-memory lightweight key/value store. Its I/O performance is much better than traditional disk-based databases like Oracle and MS SQL server. At first, we analyzed Redis' key-value data model and data structure. Subsequently we designed a four level hierarchy organization structure of vector geographic database. We stored vector geographic data and its metadata based on Redis' plentiful data structures. Then, taking the grid spatial index as an example, we designed the storage method of spatial index and spatial query processing flow for Redis based on the hierarchy organization structure of vector geographic database. Our experimental results confirmed that compared to traditional relational spatial database-Oracle Spatial, our main memory style vector geographic data management approach greatly improves spatial query responding speed and its concurrent performance is excellent. The proposed approach can be used as a front end high performance cache of large spatial database or a high performance spatial indexes database.

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    Cited: CSCD(8)
    Spatiotemporal Point Process:A New Data Model, Analysis Methodology and Viewpoint for Geoscientific Problem
    FEI Tao, LI Ting, ZHOU Cheng-Hu
       2013, 15 (6): 793-800.   DOI: 10.3724/SP.J.1047.2013.00793
    Abstract1035)      PDF (1321KB)(2655)      

    The gridding computation is a major model in current geoscientific research due to its simplicity in organizing data resources. However, because the gridding computation equally distributes computational resources, it brings redundancy to the computational process and neglects catastrophe points in geoscientific phenomena, which might overlook the important patterns and bring more uncertainties to the research result. To overcome this weakness, this paper proposes to use the spatial point process model in geoscientific research. The spatial point process model is used to model spatial point based geoscientific phenomenon, also is applied to most of the other geoscientific processes (because they can be transformed into spatial point processes). In this regard, the spatial point process is not only a data model, but also an analysis tool for geoscientific problems. Moreover, it provided a new angle of view for observing geoscientific problems. To extract patterns from point process data, the authors propose the frame of multilevel decomposition of spatiotemporal point process. This frame is similar to the basic idea of signal decomposition. We first assume that any point data set is the overlay of an unknown number of homogeneous point processes. Then, the points are transformed into a mixture probability density function of the Kth nearest distance of each point. After that, the optimization method is used to separate clustering points from noise. Finally, the patterns are extracted using the density connectivity mechanism. The theory can be used to any type of point process data. It can be considered as the "Fourier transform" of point process data.

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    Cited: CSCD(6)
    Research on Multi-source Remote Sensing Information Fusion Application
    YUAN Jinguo, WANG Wei
       2005, 7 (3): 97-103.  
    Abstract699)      PDF (1310KB)(2611)      
    Multi-source remote sensing data fusion is the development trend of remote sensing technology in depth. This paper analyzes in detail algorithmic application characteristics of multi-source remote sensing data from three levels of pixel-based, feature-based and decision-based fusion processings. Take Fengning County for example, specific applications of remote sensing data fusion methods in information extraction are illuminated. The data used in this study is firstly pre-processed, then the principal components of Landsat TM data in 1999 are analyzed, the first three principal components account for 97.8% of the total information, the resulted image of inversed principal components transformation is clearer and has more abundant levels. To extract information from remote sensing image, we select the fusion image from Landsat TM pan and multi-spectral bands after principal components transformation, color composition scheme of bands 4, 3, 2 and bands 5, 4, 3, and vegetation index and greenness index after tasseled cap transformation are analyzed, the remote sensing image information fusion with DEM and spatial data of GIS database can also improve the accuracy of remote sensing information extraction. Problems to be resolved and future direction of multi-source remote sensing data fusion are put forward.
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    Cited: CSCD(11)
    Design of an Algorithm of Public Traffic Transfer Based on the Least Transfer
    FAN Xiaochun,ZHANG Xueying,LIU Xuejun,SHEN Qijun,FAN Xiaoming
       2009, 11 (2): 157-162.  
    Abstract445)      PDF (653KB)(2370)      
    At present there are two significant problems in the field of intelligent transportation systems,i.e.algorithmic efficiency and transfer routines.First of all,this paper describes route selection behaviors of passengers and the characteristics of city traffic networks,and then presents the public traffic network-transit matrix based on key stops.Secondly,based on the shortest path algorithm,a public traffic network-transit matrix and a non-transfer matrix are introduced to design the public traffic transfer algorithm.In this algorithm,the public traffic network transit matrix aims to decide which temp label notes are potential label notes,and non-transfer notes are always considered as the notes of the shortest path,in order to improve the performance of classical shortest path algorithm(Dijkstra).Finally,a case is used to evaluate the performance of this algorithm.The experimental results indicate that the proposed algorithm achieves better efficiency than the Dijkstra.And much more reasonable transfer frequency is obtained.It is believed that this algorithm can be used in general transit networks,especially high transfer-cost networks.
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    Cited: CSCD(5)
    Analysis on the Spatial-temporal Characteristics of Guangzhou City's Spatial Morphologic Evolution
    MU Fengyun, ZHANG Zengxiang, TAN Wenbin, LIU Bin
       2007, 9 (5): 94-98.  
    Abstract481)      PDF (718KB)(2075)      
    Based on the historic literatures and remotely sensed images, this paper studies the spatial-temporal characteristics of Guangzhou city's spatial morphologic evolution in recent one hundred years, and summarizes the historic characteristics and the laws of urban development since the formation of Guangzhou, especially the period since the implementation of reform and open policies. On the whole, Guangzhou experienced two major stages, i.e., traditional urban development period before 1923 and modern urbanization development period from 1923 to 2004, covering several sub-periods of stabilization period and fast development period. The total increased built-up area of the city from 1979 to 2004 is 385.56 km 2, an expansion of 3.46 times, and the average expansion rate is about 15.43 km 2 per year. Many factors have contributed to the urban spatial morphologic evolution. But four major driving forces, i.e., economic development, institution and policy change, city planning and transportation system are the most important factors. Physiographic environment is the base of the city expansion;economic development is the inherent impetus for the evolution of the spatial morphology, and city planning plays a vital guiding role to the construction and development.
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    Cited: CSCD(12)
    Fast Extraction of Built-up Land Information from Remote Sensing Imagery
    XU Hanqiu, DU Liping
       2010, 12 (4): 574-579.  
    Abstract940)      PDF (612KB)(2049)      
    The fast expansion of urban built-up land and accompanied sharp decrease in farm land have made timely monitoring of landuse changes become more important than ever before.The ability to monitor the built-up land dynamics in a cost-effective manner is highly desirable for local communities and decision makers alike.Fortunately,satellite remote sensing technique offers considerable promise to meet this requirement.Although the use of remote sensing technique in the monitoring of land use changes has become more and more popular and satellite imagery has been frequently used to discriminate built-up lands from non-built-up lands for the last few decades,the extraction of built-up land information from remote sensing imagery is still not an easy task due largely to the heterogeneous characteristics of the built-up land.Among many techniques developed for the extraction of built-up land information,the index-based built-up index(IBI) was created based on three existing thematic indices rather than original multispectral bands.The use of the three thematic indices-soil-adjusted vegetation index(SAVI),modified normalized difference water index(MNDWI) and normalized difference built-up index(NDBI)-greatly help the delineation of built-up land features in remote sensing imagery,because these three indices represent three major landuse components,which are vegetation,water and built-up land,respectively.Therefore,the IBI can significantly enhance built-up land information while suppressing background noise.Consequently,the built-up land can be effectively extracted from the IBI image with high accuracy.In order to quicken image processing,this built-up extraction technique has been programmed to form an easy-use module using the ER Mapper scripting language.The module was further integrated in the ER Mapper package by adding a button to the manual bar.This allows users to automatically perform the extraction procedure with high accuracy just in a few minutes.
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    Cited: CSCD(14)
    GDP Spatialization in China Based on Nighttime Imagery
    HAN Xiangdi, ZHOU Yi, WANG Shixin, LIU Rui, YAO Yao
       2012, 14 (1): 128-136.   DOI: 10.3724/SP.J.1047.2012.00128
    Abstract1462)      PDF (1596KB)(2020)      
    Resources and environmental sciences require greatly the spatialized socio-economic data sets which are always obtained from administrative regions at national or provincial level, and accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications. Developing alternative methods for making estimates of gross domestic product (GDP) may prove to be useful when other measures are of suspect accuracy or unavailable. Based on the summary and analysis of existing economic activity spatialization approaches and nighttime imagery applications in economic activity, this research explores the potential for estimating the GDP using relationship between the spatial patterns of nighttime satellite imagery and GDP in China by correlation analysis and regression analysis using concerned data processing software. With the regional differences of China's economic development, logarithmic regression models have been established between different night light indexes and GDP, primary industry, secondary industry, tertiary industry and the sum of secondary industry and tertiary industry at the provincial level. A clear logarithmic linear relationship between nightlight imagery and GDP, especially the correlation coefficient of night light index and the sum of secondary industry and tertiary industry is 0.824 and R 2 of them is 0.679 at national level, suggests that this method is available and feasible to estimate the spatial distribution of economic activity such as GDP. The result, 1-km grid GDP map of China based on nighttime light data, by comparing with the other GDP spatialization approaches, shows the obvious advantage to reflect complete details and characteristics of the national secondary industry and tertiary industry distribution, which is extending the field of nighttime light data research and applications for the socio-economic data in resources and environmental sciences.
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    Cited: CSCD(30)
    Research on High Resolution Remote Sensing Image Segmentation Methods Based on Features and Evaluation of Algorithms
    MING Dongping, LUO Jiancheng, ZHOU Chenghu, WANG Jing
       2006, 8 (1): 103-109.  
    Abstract614)      PDF (1491KB)(2005)      
    Image segmentation is a key technique in image processing and computer vision field. From the point of view of geo-processing and application of remote sensing images, this paper emphasizes the importance of image segmentation for information extraction and targets recognition from remote sensing images and sets a classification system of common remote sensing image segmentation methods. In addition, this paper states the thoughts of high resolution RS image segmentation methods evaluation and tests it by evaluating four typical image segmentation algorithms based on features with six images qualitatively and quantitatively. The four typical image segmentation algorithms are Max-Entropy (ME), Split&Merge (SM), improved Gauss Markov Random Field(GMRF) and Orientation&Phase(OP). In the qualitative evaluation, this paper analyses these algorithms in terms of their rationale and gets a rough evaluation. In the quantitative evaluation, image complexity is taken into account firstly and five measures are employed. The five measures are removed region rumber, non uniformity within region measure, contrast across region measure, variance contrast across region measure and edge gradient measure. The qualitatively and quantitatively evaluation results are important to perform the optimal selection of segmentation algorithm in practical work. In the end, this paper draws some conclusions about high resolution remote sensing image segmentation and enumerates the flaws of image segmentation methods evaluation, especially it concludes the application prospect of high resolution RS image segmentation.
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    Cited: CSCD(12)
    Rule-based Approach to Semantic Resolution of Chinese Addresses
    ZHNAG Xueying, LV Guonian, LI Boqiu, Chen Wenjun
       2010, 12 (1): 9-16.  
    Abstract898)      PDF (1656KB)(1977)      
    A geographic information system(GIS) integrates hardware,software,and data for capturing,managing,analyzing,and displaying all forms of geographically referenced information.Addresses are one of the most popular geographical reference systems in natural languages.Address geocoding is considered as the most effective approach to bridging the gap between business data in management information systems(MIS) and GIS,which supports geospatial information visualization and spatial analysis.Chinese address geocoding faces three significant problems,i.e.address models,address resolution and address matching,because of the un-standardization of Chinese place names and the shortage of national address databases.Address resolution aims to automatically split address strings in natural language into address units without semantic incompletion.It plays a fundamental role in address models and address matching.Previous research focuses on rule or gazetteer based approaches,which are easily implemented but with poor coverage and performance.In theory,Chinese address resolution is similar to word segmentation in Chinese natural language processing.Based on the investigation of large-scale Chinese place names and address syntactic patterns,this paper identifies primary and secondary general characters that represent a variety of address units.And then an address numerical representation method is presented to induce syntactical rules of Chinese addresses.Finally,we develop an RBAI algorithm for implementation Chinese address resolution and illustrate an example.The experimental results indicate that the proposed approach can achieve satisfactory efficiency and effectiveness for large-scale data processing,the accuracy ratio over 92% and the processing rate over 2,800 items per second.The proposed approach and system can be extended to such fields as land management,asset management,city plan,public security,postal system,taxation,public health management and other location-base services.
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    Cited: CSCD(28)
    Study on Methods Comparison of Typical Remote Sensing Classification Based on Multi-temporal Images
    PENG Guangxiong,GONG Adu,CUI Weihong,MIN Tao,CHEN Fengrui
       2009, 11 (2): 225-230.  
    Abstract718)      PDF (770KB)(1957)      
    The study area is located in Miller county,Yunnan province,China.An experiment to select the appropriate classification method for multi-temporal remote sensing images was done.Typical classification methods including Object-Oriented Classification(OOC),Back Propagation Neural Network(BPNN),Spectral Angle Mapper(SAM),Maximum Likelihood Classifier(MLC),and Comparison After Classification(CAC)were tested in this experiment.In this study,using two-phase remote sensing images of CBERS02B-CCD and Landsat-5 TM,the suitability and accuracy of typical methods to deal with multi-temporal images classification were compared,based on different phenological characteristics of sugarcane,corn and paddy.Using full sample test method,visual interpretation results were used as reference data to validate the accuracy of different classification methods.The experimental results show that the order of overall classification accuracy from high to low is OOC,BPNN,SAM,MLC,and CAC,and the Kappa accuracy of them is 0.655、0.635、0.631、0.601 and 0.577,respectively.As it is easy to identify paddy,its accuracy is higher than that of sugarcane and corn.The order of accuracy of paddy for different methods is as the same as the order of overall accuracy,the highest and lowest accuracy of paddy is 0.706 and 0.621,respectively.The accuracy curve position between the accuracy of various land covers and the overall accuracy are consistent for MLC and CAC,and the overall accuracy of CAC is the lowest one.The accuracy of corn for OOC is the highest one with Kappa of 0.611.The Kappa accuracy of sugarcane for OOC,SAM and BPNN is 0.594,0.575 and 0.575,respectively.In general speaking,for the remote sensing classification of Multi-temporal Images,OOC is the best,BPNN and SAM is better,MLC and CAC are the worst.The conclusions of this experiment have some guidance to select the appropriate classification method for multi-temporal remote sensing images.
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    Cited: CSCD(10)
    An Analysis of the Applications of Remote Sensing Method to the Forest Biomass Estimation
    XU Xinliang, CAO Mingkui
       2006, 8 (4): 122-128.  
    Abstract2191)      PDF (709KB)(1925)      
    The spectral information of remote sensing images has integrated and realistic characteristics. It has become an important means of using remote sensing information and GIS technology to estimate forest biomass in global change research area. Firstly,the development of using remote sensing information to estimate forest biomass was summarized in this paper. Then four methods which included the method based on relationship between remote sensing information and biomass, the method based on fusion remote sensing data and process model, the method based on K-Nearest neighbor and the method based on artificial neural network were discussed. Finally the shortcomings of current research and the emphases of future research were given in this paper.
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    Cited: CSCD(23)
    Urban Traffic Congestion Detection Based on Clustering Analysis of Real-time Traffic Data
    LU Xiao-Ya, SONG Zhi-Hao, XU Zhu, LI Mu-Zi, LI Ting, SUN Wei-E
       2012, 14 (6): 775-780.   DOI: 10.3724/SP.J.1047.2012.00775
    Abstract1186)      PDF (2190KB)(1887)      

    Traffic congestion in urban road network heavily restricts transportation efficiency. Detecting traffic congestions in the spatio-temporal sense and identifying network bottlenecks become an important task in transportation management. Up to now, many traffic congestion detection methods have been proposed, which have focused on the detection of momentary local congestions. Larger-scale, longer-time and regular congestions can't be detected using these methods. That is because congestions have different temporo-spatial scales, and a characteristic is not considered in those methods. This paper proposes a new kind of urban traffic congestion detection method that deals with spatio-temporal extension of congestion. It is based on spatio-temporal clustering analysis of real-time traffic data. By defining a proper spatio-temporal correlation, the classic DBSCAN algorithm is adapted to tackle spatio-temporal clustering. With it we can detect longer time and regular traffic congestion in the spatio-temporal sense. Experiments have been conducted using real traffic condition data of Chengdu to validate the effectiveness of the method. The experiment shows that the proposed method can detect the congestion areas and identify the spatio-temporal extent of congestions accurately. The detected congestion areas were compared with congestion report from local traffic management authority and found to be consistent with the later.

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    Cited: CSCD(9)
    Classification of Hyperspectral Images with Spectral-Spatial Sparse Representation
    ZHU Yong,WU Bo
    Journal of Geo-information Science    2016, 18 (2): 263-271.   DOI: 10.3724/SP.J.1047.2016.00263
    Abstract989)   HTML1)    PDF (5281KB)(1884)      

    A novel sparse representation classification model with spectral-spatial sparsity properties is presented to improve the classification accuracy of hyperspectral images. Firstly, this method uses the wavelet dictionary as the core dictionary to extract spectral domain sparse information, and then the spectral dimension sparse representation classification is transformed into the wavelet domain (WSRC) by inverse wavelet transformation. After that, we actually extract the sparse spectral features of the hyperspectral images and increase the recognition of the original dictionary. Secondly, considering the unity and diversity of the spatial adjacent object, we realize the sparse coding of the neighborhood pixels, and then accumulate the sparse codes. At the same time, we classify the hyperspectral images using a linear classifier that is based on the accumulated sparse codes. This method ensures that we extract the main sparse signal of the neighborhood pixels on the basis of the personality features of sparse encoding, and it performs better than the joint sparse representation model (JSRC) which is directly based on the neighborhood pixels. Finally, two commonly used hyperspectral images are utilized to validate the proposed model. The experimental results demonstrate that the proposed algorithm outperforms other models in terms of overall accuracy and kappa coefficient measurements.

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    Cited: CSCD(2)
    Research of the Parallel Spatial Database Proto System Based on MPP Architecture
    CHEN Dalun,CHEN Rongguo,XIE Jiong
    Journal of Geo-information Science    2016, 18 (2): 151-159.   DOI: 10.3724/SP.J.1047.2016.00151
    Abstract1957)   HTML9)    PDF (5597KB)(1878)      

    The efficiency for querying complex spatial information resources is an important indicator to evaluate the performance of current spatial databases. Traditional single node relation spatial data management is difficult to meet the demand of high-performance in querying large amounts of spatial data, especially for the complex join query on multi-table. In order to solve this problem, we design and implement a spatial database middleware prototype system. This system takes full advantages of the massive parallel processing (MPP) and shared-nothing architecture. In consideration of the characteristics of spatial data, we design the spatial data parallel import, multi-spatial-tables join strategy, spatial data query optimization and other algorithms and models. This paper firstly introduces the development status of parallel database systems in recent years, and then elaborates its MPP architecture and its organizational model, and the strategy of the join query on multi-spatial-table. Finally, we made some query experiments on massive spatial data and analyzed the results of these inquiries. The experimental results show that this system indicates a good performance (nearly linear speedup) in processing the complex query of massive spatial data. Compared with the tradition single node database, this system can fully improve the efficiency of complex querying for large spatial data, and it is a more efficient solution to solve the complex spatial data queries.

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    Cited: CSCD(5)
    A Review on the Application Research of Trajectory Data Mining in Urban Cities
    MOU Naixia,ZHANG Hengcai,CHEN Jie,ZHANG Lingxian,DAI Honglei
    Journal of Geo-information Science    2015, 17 (10): 1136-1142.   DOI: 10.3724/SP.J.1047.2015.01136
    Abstract1449)   HTML13)    PDF (1224KB)(1877)      

    The trajectory datasets record a series of position information at different times, so they become the new data sources to study the laws of human mobility. As a main form of social remote sensing data, trajectory datasets also bring a new individual viewpoint to study geographical phenomena. With the emergence of big data, trajectory data mining becomes a hot topic in geographical information science, urban computing and other correlative disciplines. In this paper, we gave a brief review on trajectory data mining and its applications in cities. First, we listed the data sets frequently adopted by human mobility research, gave the classification and their typical applications using FCD data, mobile phone data, smart cards data, check-in data, etc. Then, we summarized its application in solving cities’ problems from four aspects: (1) the identification of urban spatial structure and function unit; (2) the patterns recognition of human activity and the behavior prediction of human movement; (3) the traffic time estimation and the anomaly detection of intelligent transportation; (4) other applications in urban computing such as in urban air and noise pollution, disaster prevention and rescue, even in intelligent tourism and information recommendation. At the end, we pointed out the challenges and further research directions of trajectory data mining.

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    Cited: CSCD(14)
    On Geographic Knowledge Graph
    LU Feng,YU Li,QIU Peiyuan
    Journal of Geo-information Science    2017, 19 (6): 723-734.   DOI: 10.3724/SP.J.1047.2017.00723
    Abstract1501)   HTML27)    PDF (5515KB)(1847)      

    Web texts contain a great deal of implicit geospatial information, which provide great potential for the geographic knowledge acquisition and service. Geographic knowledge graph is the key to extend traditional geographic information service to geographic knowledge service, and also the ultimate goal of the collection and processing of implicit geographic information from web texts. This paper systematically reviews the state of the arts of the researches on open geographic semantic web, geographic entity and relation extraction, geographic semantic web alignment, and knowledge graph storage methods. The pressing key scientific issues are also addressed, including the quality evaluation of geospatial information collected from web texts, geographic semantic understanding, spatial semantic computing model, and heterogeneous geographic semantic web alignment.

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    Cited: CSCD(11)
    Principle and Methods on Layout Based Web Map Publishing Mode with Integrated Map Data and High Fidelity
    LI Heyuan,LI Hongsheng,HAN Jiafu,LUO Bin
    Journal of Geo-information Science    2016, 18 (4): 469-476.   DOI: 10.3724/SP.J.1047.2016.00469
    Abstract837)   HTML9)    PDF (10933KB)(1813)      

    Based on the analysis of the status quo on web map publishing, particularly the status quo on thematic map publishing, a new concept of layout based web map was proposed. The layout based web map changes the GIS system tendency and static images map tendency of thematic map publishing. It provides the high fidelity map quality, meanwhile realizes the simultaneous publishing of thematic data and thematic map. This paper introduces the design and implementation techniques of the publish system on layout based web map. These techniques decompose the thematic map image and the polygon data into a quad-tree mode. On one hand, it divides the thematic map image into a map tile pyramid with given regular grid size. On the other hand, the thematic data features (mostly are the polygon features) are decomposed into grid dataset with variable resolution. It records every grid attribute based on a unique identification and stores the complex thematic data with information of its region, time, index and value as a data element, which increase the efficiency in thematic data query. In addition, this paper introduces two application scenarios. The first one is a topographic map document published in the EPS format. The second one is a layout based web map publishing system for the “Atlas on Population and Environment, People′s Republic of China”. A comparison of the merits and demerits was made among five web map publishing modes, including WebGIS, static images, PDF/GeoPDF map, SVG map and layout based web map. The layout based web map approach with high fidelity and interactive maps is considered to be promising for the digital thematic map publishing in the web mapping field.

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    Cited: CSCD(1)
    Fast Image Processing Method of UAV without Control Data
    GONG Adu, HE Xiaoying, LEI Tianjie, LI Jing
       2010, 12 (2): 254-260.  
    Abstract703)      PDF (1847KB)(1802)      
    Unmanned Aerial Vehicles(UAV) images quickly processing method without other GCP(Ground Control Point) data is discussed in this paper,and the UAV images of Disaster Areas of Wenchuan Earthquake in Sichuan Province are used as the typical test data source.In the handling progress,only the images and auxiliary data recorded by the UAV system itself are used to stitch and rectify the image mosaics.The main work contains images,which were recorded by digital camera on the UAV and auxiliary data,which were recorded by GPS(Global Positioning Satellite) system on the UAV analyzing,flying area blocking,image auto-stitching after blocking,image rectifying and image mosaic.The image auto-stitching is the key point of the whole research.Firstly,a detailed analysis on UAV images and auxiliary data is done.With the analysis result,many questions are put out,such as the number of images is so large and UAV image distortion is worse than that of traditional photogrammetry.These bring a lot of difficulties to the work,that the normal methods can not be used.Base on this situation,a new strategy is proposed in this paper.That is,in the small area,which is determined by the experiment,the auto-stitching method base on image matching is raised,then the regional images after auto-stitched are corrected according to auxiliary data of UAV.In the image matching progress,SIFT(Scale-Invariant Features Transform) algorithm is applied in order to achieve high efficiency and high precision.Then pseudo center points collected from auxiliary data are used to rectify the regional images.From the result image after stitching and rectifying,a conclusion can be drawn that the relative accuracy is high and the mosaic image is visually dislocation-free.
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    Cited: CSCD(18)
    Automatic Selection of Optimal Segmentation Scale of High-resolution Remote Sensing Images
    YAN Rui-Juan, SHI Run-He, LI Jing-Yao
       2013, 15 (6): 902-910.   DOI: 10.3724/SP.J.1047.2014.00902
    Abstract777)      PDF (5226KB)(1768)      

    With the increasing of spatial resolution of imaging sensors, object-oriented feature information extraction technology is developing rapidly. The advantages of object-based classification over the traditional pixel-based approach are well documented. Image segmentation is a key step to realize the object-oriented classification. The choice of scale parameter is very important and has a great influence on the segmentation effectiveness, but the choice of scale parameter is still decided by the repeated attempts and subjective judgments of operator, which are lacking in stability and reliability. Thus, an objective and unsupervised method is proposed for selecting optimal parameter for image segmentation to ensure best quality results. In this paper, WorldView 2 as data source, a new method based on principal component transform is introduced to choose an optimal parameter for image segmentation. We choose principal component images as the editor of image segmentation and eigenvalues as the weights of heterogeneity f and segmentation global score. Segmentation images, ranging from 20 to 200 scale, step by 10, are created in Definiens Professional 8.7. Then, the global intra-segment and inter-segment heterogeneity indexes are taken into account to identify the optimal segmentation scale (i. e. the highest GS value) by using the cubic spline interpolation function method. After comparison with the results of image segmentation based on traditional three bands, image segmentation effect obtained by principal component transform has obvious advantages. As a result, the method in this paper can effectively avoid the subjectivity of the artificial segmentation, one-sidedness and inefficiency, improve the quality of high-resolution image segmentation. The method also makes a good preprocessing work for later image classification and information extraction.

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    Cited: CSCD(14)
    Spatio-temporal Analysis of Aggregated Human Activities Based on Massive Mobile Phone Tracking Data
    CAO Jinzhou,TU Wei,LI Qingquan,CAO Rui
    Journal of Geo-information Science    2017, 19 (4): 467-474.   DOI: 10.3724/SP.J.1047.2017.00467
    Abstract1069)   HTML11)    PDF (1587KB)(1767)      

    Urban space and the behavior of human activities constantly interact with each other. Investigation on distribution of aggregated human activities and spatio-temporal change benefits data-driven policy-making in urban planning and urban governing. In the era of big data, with the development of information and communication technologies, it is possible to collect city-scale data with high resolution in space and time by various location-aware devices and sensors. Exploration of spatial-temporal activities attracts a lot of attention. By taking about 10 million one-day tracking data of mobile phone users in Shenzhen, China as an example, this paper firstly identified their stay locations according to spatial and temporal rules to generate stay trajectory for each individual and recovered activity semantic information by labelling activity types for each stay locations. Then, the significant differences in patterns of distributions of stay locations and their activities were analyzed. Spatial and temporal distributions of different human activities were explored, respectively. The study shows that the distribution of stay locations and activities is obviously heterogeneous. The average number of stay locations of an individual per day is 2.1, while the average number of activities an individual engaged in per day is 3.4. This study furthermore suggests that different types of activities have temporal variance and spatial heterogeneity. The temporal distribution fluctuates significantly over 24 hours, which is in accordance with daily routine. The spatial distribution overall obeys “space power law”, and the spatial distribution of social activity, which has a faster-down tail, shows a more obvious pattern of spatial segregation than the other two activities. The study revealed the diversity and heterogeneity of spatial and temporal distribution of human aggregated activities in urban space, which is meaningful in analyzing human activities research and facilitating urban traffic optimization and urban planning.

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    Cited: CSCD(8)
    Spatial and Temporal Variations of Dengue Fever Epidemics in China from 2004 to 2013
    NING Wenyan, LU Liang, REN Hongyan, LIU Qiyong
       2015, 17 (5): 614-621.   DOI: 10.3724/SP.J.1047.2015.00614
    Abstract841)      PDF (15037KB)(1762)      

    Dengue fever is an acute insect-borne disease transmitted by the Aedes mosquito, which is a class B infectious disease in China. Understanding the variations occurred in the spatial and temporal distribution of dengue fever epidemics will bring improvements to dengue fever prevention and control. In this study, monthly incidence data for dengue fever at municipality level across China were analyzed for the period from 2004 to 2013. The relationships between the incidence rates of dengue fever, the involved municipalities, and the imported cases were determined. The geographic pattern of dengue fever incidence rates was examined by GIS, spatial autocorrelation analysis and from the tracks of the centre of mass. Results showed that: (i) annually, the incidence rates for indigenous dengue fever cases exhibited the highest values between August and October, while the imported cases peaked between July and October. (ii) The logarithmic values of indigenous dengue fever cases was significantly correlated with the numbers of imported cases (r=0.669, p<0.05), while the number of municipalities with imported cases was linearly correlated to the number of all municipalities that have dengue fever cases (r=0.939, p<0.05). (iii) In addition to the increasing incidence rate, the dengue fever epidemic was affecting an increasing number of municipalities. The range of the epidemic was steadily increasing and gradually spreading toward inland area from the southeastern coast. (iv) Dengue fever cases did not distributed randomly with respect to time and geographical space. The highest density occurred in areas of Pearl River Delta, Hanjiang River Delta, Dehong prefecture, and Xishuangbanba prefecture. The centre of mass of dengue fever incidence rates was not stable and moved from the southeast coast (Fujian and Guangdong provinces) to the southwest (border of Yunnan province), which revealed the changes of the dengue fever distribution pattern. Our results indicate that the dengue fever epidemic in China is driven by imported cases from other countries. According to the temporal and spatial characteristics of the increasing incidence rates at municipality level and the expanding range of dengue fever in China, a stronger border inspection for people entering from abroad, especially from Southeast Asia and during the peak epidemic months between July and October, may be effective in preventing the spread of this rising epidemic.

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    Cited: CSCD(4)
    The Three-dimensional Urban Growth Simulating Based on Cellular Automata
    QIN Jing, FANG Chuang-Lin, WANG Xiang
       2013, 15 (5): 662-671.   DOI: 10.3724/SP.J.1047.2013.00662
    Abstract758)      PDF (5495KB)(1758)      

    Research of urban growth has focused on the two-dimensional flat space, while the development of the modern city is three-dimensional. So the development and changes of the modern urban space could not be accurately described by two-dimensional method. Therefore, the research of three-dimensional urban growth has great significance to the future development of the city. Based on the theory of self-organization in urban development, the urban growth simulating model using three-dimensional cellular automata (3DCA) which proposed by Bengguigui was improved. The center distance parameter and the traffic distance parameter were added to the model. And new three-dimensional urban growth models were set up: the center distance model and the transport distance model. The two establishment steps of the model are as follows: Firstly, described the calculation methods and the economic interpretations of all the model parameters. Secondly, gave the potential development function of three-dimensional urban growth and the transition rules of the cellular state. The three-dimensional urban growth simulating experiments based on the given models was developed by NetLogo 3D. NetLogo 3D is a programmable modeling environment for natural and social phenomena simulation, which could show simulating results both in two dimension and three dimension view. The experiment results show that the three-dimensional urban growth simulating with the models proposed in this paper is more approximate to the reality city extension progress than Bengguigui's model, and also prove that the urban development is a self-organized process.

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    Cited: CSCD(9)
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