SUI Haigang, FENG Wenqing, LI Wenzhuo, SUN Kaimin, XU Chuan. Review of Change Detection Methods for Multi-temporal Remote Sensing Imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1885-1898. DOI: 10.13203/j.whugis20180251
Citation: SUI Haigang, FENG Wenqing, LI Wenzhuo, SUN Kaimin, XU Chuan. Review of Change Detection Methods for Multi-temporal Remote Sensing Imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1885-1898. DOI: 10.13203/j.whugis20180251

Review of Change Detection Methods for Multi-temporal Remote Sensing Imagery

Funds: 

The National Natural Science Foundation of China 41771457

the National Key Research and Development Program of China 2016YFB0502600

More Information
  • Author Bio:

    SUI Haigang, PhD, professor, majors in remote sensing, GIS and multi-sensor integration. E-mail: haigang_sui@263.net

  • Received Date: July 11, 2018
  • Published Date: December 04, 2018
  • Change detection for remote sensing imagery is the process to determine difference of the same object or phenomenon at different times. Real-time automatic change detection technology is of great significance for excavating potential of image data and maintaining the current situation of geospatial data. With the development of remote-sensing earth observation technology, varieties of remote-sensing sensors for different tasks have emerged. Change detection is also diversified with the coming up of multi-resolution remote-sensing data, with advanced theories and techniques developed for continuously different sensors. This paper reviews the development of multi-temporal remote sen-sing image change detection technologies and summarizes the classification system of multi-temporal remote sensing image change detection. And the latest developments in change detection research are summarized from three aspects:pre-processing, change detection strategies and accuracy assessment. This paper also points out the challenges that change detection is facing and possible countermeasures, in the hope of deepening the research into change detection technology for remote sensing images.
  • [1]
    Ashbindu S. Review Article Digital Change Detection Techniques Using Remotely-Sensed Data[J]. International Journal of Remote Sensing, 2010, 10(6):989-1003 http://www.bioone.org/servlet/linkout?suffix=i1551-5036-26-3-541-Singh1&dbid=16&doi=10.2112%2F08-1172.1&key=10.1080%2F01431168908903939
    [2]
    Bruzzone L, Bovolo F. A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images[J]. Proceedings of the IEEE, 2013, 101(3):609-630 doi: 10.1109/JPROC.2012.2197169
    [3]
    Radke R J, Andra S, Alkofahi O, et al. Image Change Detection Algorithms:A Systematic Survey[J]. IEEE Transactions on Image Processing, 2005, 14(3):294-307 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0212914409/
    [4]
    李德仁.利用遥感影像进行变化检测[J].武汉大学学报·信息科学版, 2003, 28(s1):7-12 http://ch.whu.edu.cn/CN/abstract/abstract4718.shtml

    Li Deren. Change Detection from Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2003, 28(s1):7-12 http://ch.whu.edu.cn/CN/abstract/abstract4718.shtml
    [5]
    Vol N. Change Analysis in the United Arab Emi-rates:An Investigation of Techniques[J]. Photogrammetric Engineering and Remote Sensing, 1999, 65(4):475-484
    [6]
    Munyati C. Wetland Change Detection on the Kafue Flats, Zambia, by Classification of a Multi-temporal Remote Sensing Image Dataset[J]. International Journal of Remote Sensing, 2000, 21(9):1787-1806 doi: 10.1080/014311600209742
    [7]
    李亮, 舒宁, 王琰.利用归一化互信息进行基于像斑的遥感影像变化检测[J].遥感信息(理论研究), 2011, 6:18-22 http://d.old.wanfangdata.com.cn/Periodical/ygxx201106004

    Li Liang, Shu Ning, Wang Yan. Segment-Based Remote Sensing Image Change Detection Using Normalized Mutual Information[J]. Remote Sensing Information(Theoretical Research), 2011, 6:18-22 http://d.old.wanfangdata.com.cn/Periodical/ygxx201106004
    [8]
    Desclée B, Bogaert P, Defourny P. Forest Change Detection by Statistical Object-Based Method[J]. Remote Sensing of Environment, 2006, 102(1):1-11 http://www.sciencedirect.com/science/article/pii/S0034425706000344
    [9]
    Wang Wenjie, Zhao Zhongming, Zhu Haiqing.Object-Oriented Multi-feature Fusion Change Detection Method for High Resolution Remote Sensing Image[C]. The 17th International Conference on Geoinformatics, Fairfax, VA, USA, 2009
    [10]
    李亮, 舒宁, 王凯, 等.融合多特征的遥感影像变化检测方法[J].测绘学报, 2014, 43(9):945-953 http://d.old.wanfangdata.com.cn/Periodical/chxyxb201505014

    Li Liang, Shu Ning, Wang Kai, et al. Change Detection Method for Remote Sensing Images Based on Multi-features Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(9):945-953 http://d.old.wanfangdata.com.cn/Periodical/chxyxb201505014
    [11]
    赵忠明, 孟瑜, 岳安志, 等.遥感时间序列影像变化检测研究进展[J].遥感学报, 2016, 20(5):1110-1125 http://d.old.wanfangdata.com.cn/Periodical/ygxb201605034

    Zhao Zhongming, Meng Yu, Yue Anzhi, et al. Review of Remotely Sensed Time Series Data for Change Detection[J]. Journal of Remote Sensing, 2016, 20(5):1110-1125 http://d.old.wanfangdata.com.cn/Periodical/ygxb201605034
    [12]
    李权, 周兴社.基于KPCA的多变量时间序列数据异常检测方法研究[J].计算机测量与控制, 2011, 19(4):822-825 http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz201104025

    Li Quan, Zhou Xingshe. Multivariate Time Series Anomaly Detection Method Based on KPCA[J]. Computer Measurement and Control, 2011, 19(4):822-825 http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz201104025
    [13]
    Asner G P, Keller M, Pereira R J, et al. Remote Sensing of Selective Logging in Amazonia[J]. Remote Sensing of Environment, 2002, 80(3):483-496 doi: 10.1016/S0034-4257(01)00326-1
    [14]
    Chen Gang, Hay G J, Carvalho L M T, et al. Object-Based Change Detection[J]. International Journal of Remote Sensing, 2012, 33(14):4434-4457 doi: 10.1080/01431161.2011.648285
    [15]
    Zanetti M, Bruzzone L. A Theoretical Framework for Change Detection Based on a Compound Multiclass Statistical Model of the Difference Image[J]. IEEE Transactions on Geoscience and Remote Sen-sing, 2018, 99:1-15 http://ieeexplore.ieee.org/document/8078269/
    [16]
    Bovolo F, Bruzzone L. An Adaptive Multiscale Random Field Technique for Unsupervised Change Detection in VHR Multitemporal Images[C]. IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 2009
    [17]
    周启鸣.多时相遥感影像变化检测综述[J].地理信息世界, 2011, 9(2):28-33 doi: 10.3969/j.issn.1672-1586.2011.02.007

    Zhou Qiming. Review on Change Detection Using Multi-temporal Remotely Sensed Imagery[J]. Geomatics World, 2011, 9(2):28-33 doi: 10.3969/j.issn.1672-1586.2011.02.007
    [18]
    Sui Haigang, Zhou Qiming, Gong Jianya, et al. Processing of Multi-temporal Data and Change Detection[M]//Li Z L, Chen J, Baltsavias E. Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences. London: Taylor and Francis Group, 2008: 227-247
    [19]
    Li Deren. Remotely Sensed Images and GIS Data Fusion for Automatic Change Detection[J]. International Journal of Image and Data Fusion, 2010, 1(1):99-108 doi: 10.1080/19479830903562074
    [20]
    Hussain M, Chen D, Cheng A, et al. Change Detection from Remotely Sensed Images:From Pixel-Based to Object-Based Approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 80(2):91-106 http://www.sciencedirect.com/science/article/pii/S0924271613000804
    [21]
    Karantzalos K. Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data[J]. Computational Approaches for Urban Environments, 2015, 13:237-272 doi: 10.1007/978-3-319-11469-9_10
    [22]
    张良培, 武辰.多时相遥感影像变化检测的现状与展望[J].测绘学报, 2017, 46(10):1447-1459 doi: 10.11947/j.AGCS.2017.20170340

    Zhang Liangpei, Wu Chen.Advance and Future Development of Change Detection for Multi-temporal Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1447-1459 doi: 10.11947/j.AGCS.2017.20170340
    [23]
    Coppin P, Jonckheere I, Nackaerts K, et al. Review Article Digital Change Detection Methods in Ecosystem Monitoring:A Review[J]. International Journal of Remote Sensing, 2004, 25(9):1565-1596 doi: 10.1080/0143116031000101675
    [24]
    Lu D, Mausel P, Brondízio E, et al. Change Detection Techniques[J]. International Journal of Remote Sensing, 2004, 25(12):2365-2401 doi: 10.1080/0143116031000139863
    [25]
    Cao G, Li Y, Liu Y, et al. Automatic Change Detection in High-Resolution Remote-Sensing Images by Means of Level Set Evolution and Support Vector Machine Classification[J]. International Journal of Remote Sensing, 2014, 35(16):6255-6270 doi: 10.1080/01431161.2014.951740
    [26]
    Li P J, Xu H Q. Land-Cover Change Detection Using One-Class Support Vector Machine[J]. Photogrammetric Engineering and Remote Sensing, 2010, 76(3):255-263 doi: 10.14358/PERS.76.3.255
    [27]
    Yang Z, Qin Q, Zhang Q. Change Detection in High Spatial Resolution Images Based on Support Vector Machine[C]. IEEE International Conference on Geoscience and Remote Sensing Symposium, Denver, USA, 2006
    [28]
    Huang X, Xie Y, Wei J, et al. Automatic Recognition of Desertification Information Based on the Pattern of Change Detection-CART Decision Tree[J]. Journal of Catastrophology, 2017, 32(1):36-42 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zhx201701008
    [29]
    Zhang Z, Li A N, Lei G, et al. Change Detection of Remote Sensing Images Based on Multiscale Segmentation and Decision Tree Algorithm over Mountainous Area:A Case Study in Panxi Region, Sichuan Province[J]. Acta Ecologica Sinica, 2014, 34(24):7222-7232 http://en.cnki.com.cn/Article_en/CJFDTOTAL-STXB201424009.htm
    [30]
    Im J, Jensen J R. A Change Detection Model Based on Neighborhood Correlation Image Analysis and Decision Tree Classification[J]. Remote Sensing of Environment, 2005, 99(3):326-340 doi: 10.1016/j.rse.2005.09.008
    [31]
    Molinier M, Oleg A, Teemu M, et al. Clear-Cut Mapping in Landsat8 Images with a Change Detection Method Based on the Random Forest Algorithm[C]. International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Annecy, France, 2015
    [32]
    Seo D K, Yong H K, Yang D E, et al. Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection[J]. Remote Sensing, 2017, 9(11):1163-1174 doi: 10.3390/rs9111163
    [33]
    Liu D, Song K, Townshend J R G, et al. Using Local Transition Probability Models in Markov Random Fields for Forest Change Detection[J]. Remote Sensing of Environment, 2008, 112(5):2222-2231 doi: 10.1016/j.rse.2007.10.002
    [34]
    Jia L, Li M, Zhang P, et al. SAR Image Change Detection Based on Multiple Kernel k-Means Clustering with Local-Neighborhood Information[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6):856-860 doi: 10.1109/LGRS.2016.2550666
    [35]
    Lv H, Lu H, Mou L. Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection[J]. Remote Sensing, 2016, 8(6):506-528 doi: 10.3390/rs8060506
    [36]
    Wang Q, Shi W, Atkinson P M, et al. Land Cover Change Detection at Subpixel Resolution with a Hopfield Neural Network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(3):1339-1352 http://ieeexplore.ieee.org/document/6906234/
    [37]
    Jia L, Li M, Zhang P, et al. SAR Image Change Detection Based on Correlation Kernel and Multistage Extreme Learning Machine[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):5993-6006 doi: 10.1109/TGRS.2016.2578438
    [38]
    Chang N B, Han M, Yao W, et al. Change Detection of Land Use and Land Cover in an Urban Region with SPOT-5 Images and Partial Lanczos Extreme Learning Machine[J]. Journal of Applied Remote Sensing, 2010, 4(1):2816-2832 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2c1f033fd1fbe150b1a8220a5dfe3012
    [39]
    Pijanowski B C, Brown D G, Shellito B A, et al. Using Neural Networks and GIS to Forecast Land Use Changes:A Land Transformation Model[J]. Computers Environment and Urban Systems, 2002, 26(6):553-575 doi: 10.1016/S0198-9715(01)00015-1
    [40]
    Chen Xiuwan. Using Remote Sensing and GIS to Analyze Land Cover Change and Its Impacts on Regional Sustainable Development[J]. International Journal of Remote Sensing, 2002, 23(1):107-124 doi: 10.1080/01431160010007051
    [41]
    Hao M, Shi W, Zhang H, et al. Unsupervised Change Detection with Expectation-Maximization-Based Level Set[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 11(1):210-214 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4cc741b38cc9abdbf3235ca1718dabb4
    [42]
    Cao G, Liu Y, Shang Y. Automatic Change Detection in Remote Sensing Images Using Level Set Method with Neighborhood Constraints[J]. Journal of Applied Remote Sensing, 2014, 8(1):083678 doi: 10.1117/1.JRS.8.083678
    [43]
    Bruzzone L, Prieto D. Automatic Analysis of the Difference Image for Unsupervised Change Detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3):1171-1182 doi: 10.1109/36.843009
    [44]
    Hao M, Zhang H, Shi W, et al. Unsupervised Change Detection Using Fuzzy-Means and MRF from Remotely Sensed Images[J]. Remote Sensing Letters, 2013, 4(12):1185-1194 doi: 10.1080/2150704X.2013.858841
    [45]
    Zhou L, Cao G, Li Y, et al. Change Detection Based on Conditional Random Field with Region Connection Constraints in High-Resolution Remote Sensing Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8):3478-3488 doi: 10.1109/JSTARS.2016.2514610
    [46]
    Cao G, Zhou L, Li Y. A New Change-Detection Method in High-Resolution Remote Sensing Images Based on a Conditional Random Field Model[J]. International Journal of Remote Sensing, 2016, 37(5):1173-1189 doi: 10.1080/01431161.2016.1148284
    [47]
    佟国峰, 李勇, 丁伟利, 等.遥感影像变化检测算法综述[J].中国图象图形学报, 2015, 20(12):1561-1571 doi: 10.11834/jig.20151201

    Tong Guofeng, Li Yong, Ding Weili, et al. Review of Remote Sensing Image Change Detection[J]. Journal of Image and Graphics, 2015, 20(12):1561-1571 doi: 10.11834/jig.20151201
    [48]
    Stow D A, Chen D M. Sensitivity of Multi-temporal NOAA AVHRR Data of an Urbanizing Region to Land-Use/Land-Cover Changes and Misregistration[J]. Remote Sensing of Environment, 2002, 80(2):297-307 doi: 10.1016/S0034-4257(01)00311-X
    [49]
    Chen G, Zhao K, Powers R. Assessment of the Image Misregistration Effects on Object-Based Change Detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87(19):19-27 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1c9ca1c73b837684051550a20186c036
    [50]
    Bovolo F, Bruzzone L. A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain[J]. IEEE Transactions on Geoscience and Remote Sen-sing, 2007, 45(1):218-236 doi: 10.1109/TGRS.2006.885408
    [51]
    张晓东, 李德仁, 龚健雅, 等.遥感影像与GIS分析相结合的变化检测方法[J].武汉大学学报·信息科学版, 2006, 31(3):266-269 http://ch.whu.edu.cn/CN/abstract/abstract2403.shtml

    Zhang Xiaodong, Li Deren, Gong Jianya, et al. A Change Detection Method of Integrating Remote Sensing and GIS[J]. Geomatics and Information Science of Wuhan University, 2006, 31(3):266-269 http://ch.whu.edu.cn/CN/abstract/abstract2403.shtml
    [52]
    Paolini L, Grings F, Sobrino J A, et al. Radiometric Correction Effects in Landsat Multi-date/Multi-sensor Change Detection Studies[J]. International Journal of Remote Sensing, 2006, 27(4):685-704 doi: 10.1080/01431160500183057
    [53]
    Li Wenzhuo, Sun Kaimin, Zhang Hongya. Algorithm for Relative Radiometric Consistency Process of Remote Sensing Images Based on Object-Oriented Smoothing and Contourlet Transforms[J]. Journal of Applied Remote Sensing, 2014, 8(1):083607 doi: 10.1117/1.JRS.8.083607
    [54]
    Zhang P Q, Xu-Chu Y, Liu Z, et al. A Study on Relative Radiometric Correction of Multi-temporal Remote Sensing Images[J]. Journal of Remote Sensing, 2006, 10(3):339-344 http://en.cnki.com.cn/Article_en/CJFDTotal-YGXB200603008.htm
    [55]
    Gao F, Dong J Y, Li B, et al. Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 13(12):1792-1796 http://ieeexplore.ieee.org/document/7589111/
    [56]
    Geng J, Wang H Y, Fan J C, et al. Change Detection of SAR Images Based on Supervised Contractive Auto-encoders and Fuzzy Clustering[C]. 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China, 2017
    [57]
    Gong M, Zhao J, Liu J, et al. Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(1):125-138 doi: 10.1109/TNNLS.2015.2435783
    [58]
    Zhang H, Gong M G, Zhang P Z, et al. Feature-Level Change Detection Using Deep Representation and Feature Change Analysis for Multispectral Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(11):1666-1670 doi: 10.1109/LGRS.2016.2601930
    [59]
    Gong M G, Zhan T, Zhang P Z, et al. Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5):2658-2673 doi: 10.1109/TGRS.2017.2650198
    [60]
    Su L Z, Gong M G, Zhang P Z, et al. Deep Learning and Mapping Based Ternary Change Detection for Information Unbalanced Images[J]. Pattern Recognition, 2017, 66(C):213-228 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7c55e5957a42db4a146d8cace30ccfb7
    [61]
    Hay G J, Niemann K O. Visualizing 3-D Texture:A Three-Dimensional Structure Approach to Model Forest Texture[J]. Canadian Journal of Remote Sensing, 1994, 20(2):90-101
    [62]
    Baatz M, Schäpe A. An Optimization Approach for High Quality Multi-scale Image Segmentation[C]. Beiträge Zum AGIT-Symposium, Karlsruhe, Germany, 2000
    [63]
    裴欢, 孙天娇, 王晓妍.基于Landsat8 OLI影像纹理特征的面向对象土地利用/覆盖分类[J].农业工程学报, 2018, 34(2):248-255 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201802034

    Pei Huan, Sun Tianjiao, Wang Xiaoyan. Object-Oriented Land Use/Cover Classification Based on Texture Features of Landsat8 OLI Image[J]. Transactions of the Chinese Society of Agricultu-ral Engineering, 2018, 34(2):248-255 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201802034
    [64]
    Cai S, Liu D. A Comparison of Object-Based and Contextual Pixel-Based Classifications Using High and Medium Spatial Resolution Images[J]. Remote Sensing Letters, 2013, 4(10):998-1007 doi: 10.1080/2150704X.2013.828180
    [65]
    Zhang P, Lv Z, Shi W. Object-Based Spatial Feature for Classification of very High Resolution Remote Sensing Images[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6):1572-1576 doi: 10.1109/LGRS.2013.2262132
    [66]
    Mahmoudi F T, Samadzadegan F, Reinartz P. Context Aware Modification on the Object Based Image Analysis[J]. Journal of the Indian Society of Remote Sensing, 2015, 43(4):709-717 doi: 10.1007/s12524-015-0453-5
    [67]
    Roelfsema C. High Spatial Resolution Remote Sensing for Environmental Monitoring and Management Preface[J]. Spatial Science, 2008, 53(1):43-47 http://www.ingentaconnect.com/content/spatial/jss/2008/00000053/00000001/art00005
    [68]
    Zhou W, Troy A, Grove M. Object-Based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data[J]. Sensors, 2008, 8(3):1613-1636 doi: 10.3390/s8031613
    [69]
    Lefebvre A, Corpetti T, Hubert-Moy L. Object-Oriented Approach and Texture Analysis for Change Detection in very High Resolution Images[C]. IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2009
    [70]
    Chant T D, Kelly M, Huang B. Individual Object Change Detection for Monitoring the Impact of a Forest Pathogen on a Hardwood Forest[J]. Photogrammetric Engineering and Remote Sensing, 2009, 75(8):1005-1013 doi: 10.14358/PERS.75.8.1005
    [71]
    Stow D. Handbook of Applied Spatial Analysis[M]. New York:Springer, 2010
    [72]
    Huang J, Shen S. Land Use Change Detection Using High Spatial Resolution Remotely Sensed Image and GIS Data[J]. Journal of Yangtze River Scientific Research Institute, 2012, 29(1):49-52 http://d.old.wanfangdata.com.cn/Periodical/cjkxyyb201201010
    [73]
    Zhang P, Ruan B, Chao J. An Object-Based Basic Farmland Change Detection Using High Spatial Resolution Image and GIS Data of Land Use Planning[J]. Key Engineering Materials, 2012, 500:492-499 doi: 10.4028/www.scientific.net/KEM.500
    [74]
    Toure S, Stow D, Shih H, et al. An Object-Based Temporal Inversion Approach to Urban Land Use Change Analysis[J]. Remote Sensing Letters, 2016, 7(5):503-512 doi: 10.1080/2150704X.2016.1157640
    [75]
    Chen Q, Chen Y. Multi-feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis[J]. Remote Sensing, 2016, 8(7):549-568 doi: 10.3390/rs8070549
    [76]
    Duro D C, Franklin S E, Dubé M G. A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery[J]. Remote Sensing of Environment, 2012, 118(6):259-272 http://www.sciencedirect.com/science/article/pii/S0034425711004172
    [77]
    Wang R S M, Roberts S A, Efford N D. Object-Based Approach to Integrate Remotely Sensed Data with Geodata Within a GIS Context for Land-Use Classification at Urban-Rural Fringe Area[J]. Proceedings of SPIE-the International Society for Optical Engineering, 1997, 3222:362-370 http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=932457
    [78]
    陈扬洋, 明冬萍, 徐录, 等.高空间分辨率遥感影像分割定量实验评价方法综述[J].地球信息科学学报, 2017, 19(6):818-830 doi: 10.3969/j.issn.1560-8999.2017.06.011

    Chen Yangyang, Ming Dongping, Xu Lu, et al. An Overview of Quantitative Experimental Methods for Segmentation Evaluation of High Spatial Remote Sensing Images[J]. Journal of Geo-information Science, 2017, 19(6):818-830 doi: 10.3969/j.issn.1560-8999.2017.06.011
    [79]
    Gong J Y, Sui H G, Sun K M, et al. Object-Level Change Detection Based on Full-Scale Image Segmentation and Its Application to Wenchuan Earthquake[J]. Science in China, 2008, 51(2):110-122 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK200802388556
    [80]
    Basaeed E, Bhaskar H, Hill P, et al. A Supervised Hierarchical Segmentation of Remote-Sensing Images Using a Committee of Multi-scale Convolutional Neural Networks[J]. International Journal of Remote Sensing, 2016, 37(7):1671-1691 doi: 10.1080/01431161.2016.1159745
    [81]
    Zhao B, Zhong Y, Zhang L. A Spectral-Structural Bag-of-Features Scene Classifier for very High Spatial Resolution Remote Sensing Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116:73-85 doi: 10.1016/j.isprsjprs.2016.03.004
    [82]
    Zhu Q, Zhong Y, Zhao B, et al. Bag-of-Visual-Words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 13(6):747-751 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=f107f3fb007eb153352cbd83f6182f8e
    [83]
    Wu C, Zhang L, Du B. Kernel Slow Feature Analysis for Scene Change Detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017(99):1-18 http://ieeexplore.ieee.org/document/7817860/
    [84]
    Wu C, Zhang L, Zhang L. A Scene Change Detection Framework for Multi-temporal very High Resolution Remote Sensing Images[J]. Signal Proce-ssing, 2016, 124(C):184-197 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=5a2e6f64cca8c7e9d6898e383d8587a8
    [85]
    Cheng G, Li Z, Yao X, et al. Remote Sensing Image Scene Classification Using Bag of Convolutional Features[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10):1735-1739 doi: 10.1109/LGRS.2017.2731997
    [86]
    Hu F, Xia G S, Hu J, et al. Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery[J]. Remote Sensing, 2015, 7(11):14680-14707 doi: 10.3390/rs71114680
    [87]
    Zou Q, Ni L, Zhang T, et al. Deep Learning Based Feature Selection for Remote Sensing Scene Classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11):2321-2325 doi: 10.1109/LGRS.2015.2475299
    [88]
    Guan H, Li J, Yu Y, et al. DEM Generation from LiDAR Data in Wooded Mountain Areas by Cross-Section-Plane Analysis[J]. International Journal of Remote Sensing, 2014, 35(3):927-948 doi: 10.1080/01431161.2013.873833
    [89]
    Zhao L J, Tang P, Huo L Z. Land-Use Scene Classification Using a Concentric Circle-Structured Multiscale Bag-of-Visual-Words Model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 7(12):4620-4631 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fe8618456dc543b4e122c2964bd85210
    [90]
    Lin W, Liu Y, Feng J. Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification[J]. Computational Intelligence and Neuroscience, 2017(2):1-14 http://europepmc.org/abstract/MED/28706534
    [91]
    Zhu Q, Zhong Y, Zhang L, et al. Scene Classification Based on the Fully Sparse Semantic Topic Mo-del[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017(99):1-14 http://ieeexplore.ieee.org/document/7959103/
    [92]
    Zhao W, Du S. Scene Classification Using Multi-scale Deeply Described Visual Words[J]. International Journal of Remote Sensing, 2016, 37(17):4119-4131 doi: 10.1080/01431161.2016.1207266
    [93]
    Gehrke S, Morin K, Downey M, et al. Semi-global Matching: An Alternative to LiDAR for DSM Gene-ration[C]. The 2010 Canadian Geomatics Confe-rence and Symposium of Commission, Calgary, Canada, 2010
    [94]
    Westoby M, Brasington J, Glasser N, et al. 'Structure-from-Motion' Photogrammetry:A Low-Cost, Effective Tool for Geoscience Applications[J]. Geomorphology, 2012, 179:300-314 doi: 10.1016/j.geomorph.2012.08.021
    [95]
    Stal C, Tack F, de Maeyer P, et al. Airborne Photogrammetry and LiDAR for DSM Extraction and 3D Change Detection over an Urban Area-A Comparative Study[J]. International Journal of Remote Sensing, 2013, 34:1087-1110 doi: 10.1080/01431161.2012.717183
    [96]
    White J C, Wulder M A, Vastaranta M, et al. The Utility of Image-Based Point Clouds for Forest Inventory:A Comparison with Airborne Laser Scanning[J]. Forests, 2013, 4:518-536 doi: 10.3390/f4030518
    [97]
    Shorter N, Kasparis T. Automatic Vegetation Identification and Building Detection from a Single Nadir Aerial Image[J]. Remote Sensing, 2009, 1(4):731-757 doi: 10.3390/rs1040731
    [98]
    Chen L C, Lin L J. Detection of Building Changes from Aerial Images and Light Detection and Ranging (LiDAR) Data[J]. Journal of Applied Remote Sensing, 2012, 4(12):2785-2802 doi: 10.1117/1.3525560
    [99]
    Liu Z, Gong P, Shi P, et al. Automated Building Change Detection Using UltraCamD Images and Existing CAD Data[J]. International Journal of Remote Sensing, 2010, 31(6):1505-1517 doi: 10.1080/01431160903475340
    [100]
    Hermosilla T, Ruiz L A, Recio J A, et al. Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data[J]. Remote Sensing, 2011, 3(6):1188-1210 doi: 10.3390/rs3061188
    [101]
    Awrangjeb M, Ravanbakhsh M, Fraser C S. Automatic Detection of Residential Buildings Using LiDAR Data and Multispectral Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(5):457-467 doi: 10.1016/j.isprsjprs.2010.06.001
    [102]
    Huertas A, Nevatia R. Detecting Changes in Aerial Views of Man-Made Structures[J]. Image and Vision Computing, 2000, 18(8):583-596 doi: 10.1016/S0262-8856(99)00063-3
    [103]
    Gonzalez J, Ambrosio I, Arevalo V. Automatic Urban Change Detection from the IRS-1D PAN[C]. Remote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop, Rome, Italy, 2001
    [104]
    Rowe N C, Grewe L L. Change Detection for Linear Features in Aerial Photographs Using Edge-Finding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7):1608-1612 doi: 10.1109/36.934092
    [105]
    柳稼航.利用遥感技术进行城市建筑物震害的自动识别与分类方法研究[D].北京: 中国地震局地质研究所, 2003

    Liu Jiahang. A Method Study on Automatic Recognition and Classification of Earthquake-Caused Building Damage in Cities Using Remote Sensing[D]. Beijing: Institute of Geology, China Seismology Bureau, 2003
    [106]
    顾文俊, 赵忠明, 王苓涓.基于变化检测技术的城区建筑变化目标提取[J].计算机工程与应用, 2004, 40(1):198-200 doi: 10.3321/j.issn:1002-8331.2004.01.065

    Gu Wenjun, Zhao Zhongming, Wang Linjuan. The Detection of the Changed Building in City Based on Change Detection Technology[J]. Computer Engineering and Applications, 2004, 40(1):198-200 doi: 10.3321/j.issn:1002-8331.2004.01.065
    [107]
    刘臻, 宫鹏, 史培军, 等.基于相似度验证的自动变化探测研究[J].遥感学报, 2005, 9(5):537-543 http://d.old.wanfangdata.com.cn/Periodical/ygxb200505004

    Liu Zhen, Gong Peng, Shi Peijun, et al. Study on Change Detection Automatically Based on Similarity Calibration[J]. Journal of Remote Sensing, 2005, 9(5):537-543 http://d.old.wanfangdata.com.cn/Periodical/ygxb200505004
    [108]
    耿忠.面向单波段高分辨率遥感影像的人工目标变化检测技术研究[J].地理信息世界, 2007, 5(6):36-41 doi: 10.3969/j.issn.1672-1586.2007.06.009

    Geng Zhong. Research on Artificial Object Changing Detection Techniques of Single-band Oriented High Resolution Remote Sensing Image[J]. Geomatics World, 2007, 5(6):36-41 doi: 10.3969/j.issn.1672-1586.2007.06.009
    [109]
    Li W, Sun K, Li D, et al. A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection[J]. Remote Sensing, 2017, 9(6):625-633 doi: 10.3390/rs9060625
    [110]
    Benoît M, Eric F L. Land-Cover-Change Trajectories in Southern Cameroon[J]. Annals of the Association of American Geographers, 2000, 90(3):467-494 doi: 10.1111/0004-5608.00205
    [111]
    Liu H, Zhou Q. Accuracy Analysis of Remote Sensing Change Detection by Rule-Based Rationality Evaluation with Post-Classification Comparison[J]. International Journal of Remote Sensing, 2004, 25(5):1037-1050 doi: 10.1080/0143116031000150004
    [112]
    Zhou Q, Sun B. Spatial Pattern Analysis of Water-Driven Land Cover Change in Aridzone, Northwest of China[M]//Advances in Earth Observation of Global Change. Netherlands:Springer, 2010:17-26
    [113]
    欧阳赟, 马建文, 戴芹.多时相遥感变化检测的动态贝叶斯网络研究[J].遥感学报, 2006, 10(4):440-448 http://d.old.wanfangdata.com.cn/Periodical/ygxb200604002

    Ouyang Yun, Ma Jianwen, Dai Qin. Study on Dynamic Bayesian Networks for Multi-temporal Remote Sensing Change Detection[J]. Journal of Remote Sensing, 2006, 10(4):440-448 http://d.old.wanfangdata.com.cn/Periodical/ygxb200604002
    [114]
    Vaduva C, Gavat I, Datcu M. Latent Dirichlet Allocation for Spatial Analysis of Satellite Images[J]. IEEE Transactions on Geoscience and Remote Sen-sing, 2013, 51(5):2770-2786 doi: 10.1109/TGRS.2012.2219314
    [115]
    Salmon B P, Kleynhans W, Bergh F V D, et al. Land Cover Change Detection Using the Internal Covariance Matrix of the Extended Kalman Filter over Multiple Spectral Bands[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3):1079-1085 doi: 10.1109/JSTARS.2013.2241023
    [116]
    Li J, Narayanan R M. A Shape-Based Approach to Change Detection of Lakes Using Time Series Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(11):2466-2477 doi: 10.1109/TGRS.2003.817267
    [117]
    Du P, Liu S, Gamba P, et al. Fusion of Difference Images for Change Detection over Urban Areas[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(4):1076-1086 doi: 10.1109/JSTARS.2012.2200879
    [118]
    Li J, Narayanan R M. A Shape-Based Approach to Change Detection of Lakes Using Time Series Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(11):2466-2477 doi: 10.1109/TGRS.2003.817267
    [119]
    Warner T. Hyperspherical Direction Cosine Change Vector Analysis[J]. International Journal of Remote Sensing, 2005, 26(6):1201-1215 doi: 10.1080/0143116042000298252
    [120]
    Michener W K, Houhoulis P F. Detection of Vegetation Changes Associated with Extensive Flooding in a Forested Ecosystem[J]. Photogrammetric Engineering and Remote Sensing, 1998, 63(12):1363-1374 http://europepmc.org/abstract/AGR/IND20903985
    [121]
    冯文卿, 张永军.利用多尺度融合进行面向对象的遥感影像变化检测[J].测绘学报, 2015, 44(10):1142-1151 http://d.old.wanfangdata.com.cn/Periodical/chxb201510011

    Feng Wenqing, Zhang Yongjun. Object-Oriented Change Detection for Remote Sensing Images Based on Multi-scale Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(10):1142-1151 http://d.old.wanfangdata.com.cn/Periodical/chxb201510011
    [122]
    冯文卿, 眭海刚, 涂继辉, 等.联合像素级和对象级分析的遥感影像变化检测[J].测绘学报, 2017, 46(9):1147-1155 http://d.old.wanfangdata.com.cn/Periodical/chxb201709010

    Feng Wenqing, Sui Haigang, Tu Jihui, et al. Remote Sensing Image Change Detection Based on the Combination of Pixel-Level and Object-Level Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(9):1147-1155 http://d.old.wanfangdata.com.cn/Periodical/chxb201709010
    [123]
    全吉成, 刘一超, 薛峰.基于模糊综合评判的遥感图像变化检测方法[J].现代电子技术, 2013, 36(8):112-113 doi: 10.3969/j.issn.1004-373X.2013.08.037

    Quan Jicheng, Liu Yichao, Xue Feng. Detection Method of Remote Sensing Image Change Detection Based on Fuzzy Comprehensive Evaluation[J]. The Modern Electronic Technology, 2013, 36(8):112-113 doi: 10.3969/j.issn.1004-373X.2013.08.037
    [124]
    Gong Peng, Mu Lan. Error Detection Through Consistency Checking[J]. Geographic Information Sciences, 2000, 6(2):188-193 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0215092614/
    [125]
    Nemmour H, Chibani Y. Fuzzy Neural Network Architecture for Change Detection in Remotely Sensed Imagery[J]. International Journal of Remote Sensing, 2006, 27(4):705-717 doi: 10.1080/01431160500275648
    [126]
    Morisette J T. Accuracy Assessment Curves for Satellite-Based Change Detection[J]. Photogrammetric Engineering and Remote Sensing, 2000, 66(7):875-880
    [127]
    Lowell K. An Area-Based Accuracy Assessment Methodology for Digital Change Maps[J]. International Journal of Remote Sensing, 2001, 22(17):3571-3596 doi: 10.1080/01431160010031270
    [128]
    Biging G S, Colby D R, Congalton R G. Sampling Systems for Change Detection Accuracy Assessment[M]. Chelsea, Michigan:Ann Arbor Press, 1999
  • Related Articles

    [1]LI Jianan, LI Yu, ZHAO Quanhua, JIANG Haonan, HONG Yong. SAR Image Absolute Radiometric Calibration Based on RCS Modeling of Communication Tower[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1746-1755. DOI: 10.13203/j.whugis20210052
    [2]CAO Jiannong. Modeling Method of Structured Feature Multi-scale Analysis for High Resolution Remote Sensing Image Information Extraction[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1943-1953. DOI: 10.13203/j.whugis20180253
    [3]YAN Xiongfeng, AI Tinghua, ZHANG Xiang, YANG Wei. A Vector Pyramid Model to Support Continuous Multi-scale Representation of Spatial Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(4): 502-508. DOI: 10.13203/j.whugis20150723
    [4]LIU Po, GONG Jianhua. Parallel Construction of Global Pyramid for Large Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2016, 41(1): 117-122. DOI: 10.13203/j.whugis20130718
    [5]CHAI Yong, LIN Xueyuan, HE You. B-Spline Pyramidal Direction Filter Banks and Its Application to Remote Sensing Image Fusion[J]. Geomatics and Information Science of Wuhan University, 2010, 35(7): 873-876.
    [6]LIU Yan, LIU Jingnan, LI Tao, XIA Ye. Monitoring Damage of State Grid Transmission Tower in Bad Weather by High-Resolution SAR Satellites[J]. Geomatics and Information Science of Wuhan University, 2009, 34(11): 1354-1358.
    [7]JIANG Zhijun, YI Huarong. An Image Pyramid-Based Feature Detection and Tracking Algorithm[J]. Geomatics and Information Science of Wuhan University, 2007, 32(8): 680-683.
    [8]MEI Wensheng, HAN Guoming, ZHANG Zhenglu. 3D Relative Datum Method in Positioning of Main Pylon of Cable-Stayed Bridge[J]. Geomatics and Information Science of Wuhan University, 2007, 32(2): 168-171.
    [9]ZOU Qin, JIA Yonghong. Fusion of Remote Sensing Images Based on Morphological Pyramid and Its Performance Evaluation[J]. Geomatics and Information Science of Wuhan University, 2006, 31(11): 971-974.
    [10]XIAO Jun, MO Yimin, HU Guoqing. High Precision Vertical Pendulum Tiltmeter for Measuring Earth Tide[J]. Geomatics and Information Science of Wuhan University, 2004, 29(11): 973-976,989.
  • Cited by

    Periodical cited type(103)

    1. 王培晓,张恒才,张岩,程诗奋,张彤,陆锋. 地理空间智能预测研究进展与发展趋势. 地球信息科学学报. 2025(01): 60-82 .
    2. 张勤,朱登轩. “数据的流动”:数字技术驱动城市韧性治理的理路探析. 湖湘论坛. 2024(02): 46-56 .
    3. 李艳莉,孙珍珠. 我国开放大学的分布特征、影响因素与优化路径. 中国成人教育. 2024(03): 3-13 .
    4. 郑宇,易修文,齐德康,潘哲逸. 基于城市知识体系的公共数据要素构建方法. 大数据. 2024(04): 130-148 .
    5. 胡秋实,李锐,吴华意,刘朝辉,蔡晶. 顾及城市场景变化的人口分析单元表达. 武汉大学学报(信息科学版). 2024(10): 1788-1799 .
    6. 郑宇. 城市感知体系. 武汉大学学报(信息科学版). 2024(10): 1770-1787 .
    7. 张艳丰,黄亚婷,赵资澧. 数字空间视角下区域智慧城市群发展水平测度实证研究. 情报探索. 2024(11): 82-89 .
    8. 董一民,硕天鸾. 数据要素推动智慧城市发展:战略、挑战与对策. 中国新通信. 2024(23): 62-64 .
    9. 陈欣,郭文月,孙群,李少梅,温伯威. 一种街景影像的多模态地理场所情感度量方法. 测绘科学技术学报. 2024(06): 666-673+680 .
    10. 郑宇. 城市知识体系. 武汉大学学报(信息科学版). 2023(01): 1-16 .
    11. 李振,孙建星,王少阳,马基栋. 时空轨迹应用分类及其智能处理方法分析. 通信技术. 2023(01): 28-32 .
    12. ZHANG Yingna,王悦,胡昊宇,袁春来. 基于手机信令大数据的京津冀城市群人口时空分布与流动特征分析. 地域研究与开发. 2023(03): 161-167+180 .
    13. 刘敬一,彭举,唐建波,胡致远,郭琦,姚晨,陈金勇. 融合多特征的轨迹数据自适应聚类方法. 地球信息科学学报. 2023(07): 1363-1377 .
    14. 邓敏,刘启亮. “大知识”时代地理信息科学专业本科人才培养探索与实践. 测绘通报. 2023(08): 178-181 .
    15. 郑宇. 城市治理一网统管. 武汉大学学报(信息科学版). 2022(01): 19-25 .
    16. 宋轩,高云君,李勇,关庆锋,孟小峰. 空间数据智能:概念、技术与挑战. 计算机研究与发展. 2022(02): 255-263 .
    17. 陈李越,柴迪,王乐业. UCTB:时空人群流动预测工具箱. 计算机科学与探索. 2022(04): 835-843 .
    18. 刘耀林,刘启亮,邓敏,石岩. 地理大数据挖掘研究进展与挑战. 测绘学报. 2022(07): 1544-1560 .
    19. 吴华意,胡秋实,李锐,刘朝辉. 城市人口时空分布估计研究进展. 测绘学报. 2022(09): 1827-1847 .
    20. 焦利民,刘耀林. 可持续城市化与国土空间优化. 武汉大学学报(信息科学版). 2021(01): 1-11 .
    21. 张志沛. 基于POI数据的城市功能区识别研究——以呼和浩特市中心城区为例. 科学技术创新. 2021(03): 98-100 .
    22. 陈彦如,张涂静娃,杜千,冉茂亮,王红军. 基于深度森林的高铁站室内热舒适度等级预测. 计算机应用. 2021(01): 258-264 .
    23. 叶光辉,毕崇武. 基于标签语义挖掘的城市画像研究评述. 现代情报. 2021(02): 162-167 .
    24. 崔巍. 大数据时代新型智慧城市建设路径研究. 社会科学战线. 2021(02): 251-255 .
    25. 罗桑扎西,甄峰,张姗琪. 复杂网络视角下的城市人流空间概念模型与研究框架. 地理研究. 2021(04): 1195-1208 .
    26. 肖凡智,张雨竹,尹耀宽,许建潮,刘钢. 城市计算中的显露模式分析方法研究. 计算机与数字工程. 2021(04): 766-770+775 .
    27. 盛宇裕,毕硕本,范京津,NKUNZIMANA Athanase,许志慧. 运用交通运行状况指标分析交通热点时空模式. 武汉大学学报(信息科学版). 2021(05): 746-754 .
    28. 胡添,刘涛,杜萍,余贝贝,张萌生. 空间同位模式支持下城市服务业关联发现及特征分析. 地球信息科学学报. 2021(06): 969-978 .
    29. 雷永琪,李娜,陈智军,何渡,张雨昂. 基于作息时空特征优化神经网络的出租车乘客候车时长预测. 软件导刊. 2021(08): 29-37 .
    30. 刘臣,陈静娴,郝宇辰,李秋,甄俊涛. 基于时空网络的地铁进出站客流量预测. 计算机工程与应用. 2021(18): 248-254 .
    31. 王誓伟,徐晓斌,梁中军. 基于城市计算的分布式异常数据分级过滤算法. 计算机集成制造系统. 2021(09): 2525-2531 .
    32. 刘浩,薛梅. 虚拟地理环境下的地理空间认知初步探索. 遥感学报. 2021(10): 2027-2039 .
    33. 张伟杰,於志勇,黄昉菀,朱伟平. 面向积水推测的机会式感知轨迹选择. 郑州大学学报(理学版). 2021(04): 102-108 .
    34. 纪圣塨,郑宇,王诏远,李天瑞. 基于前向搜索和投票的移动群智感知动态用户招募方法. 计算机学报. 2021(10): 1998-2015 .
    35. 倪哲,刘轶伦. 基于出租车轨迹数据的动态可达性分析. 城市建筑. 2021(29): 19-21 .
    36. 马强,王亮绪,吴昊圆,龚鑫,李卓勋. 基于POI权重与频率密度的上海城市功能区变化分析. 地理信息世界. 2021(04): 16-22 .
    37. 张隅希,段宗涛,朱依水,王路阳,周祎,郭宇. 机动车油耗模型研究综述. 计算机工程与应用. 2021(24): 14-26 .
    38. 马潇雅,刘远刚,赵翔. 城市公共服务设施优化配置模型研究的近期进展与展望. 测绘通报. 2020(02): 9-16 .
    39. 贾冲,冯慧芳,杨振娟. 基于出租车GPS轨迹和POI数据的商业选址推荐. 计算机与现代化. 2020(02): 21-25+30 .
    40. 刘岩,刘铭. 基于CNKI的国内大数据研究热点及趋势分析. 北京警察学院学报. 2020(01): 63-71 .
    41. 张杨燚,谢辉,毛进,李纲. 面向城市数据画像构建的多源数据需求与融合方法研究. 情报理论与实践. 2020(06): 88-96 .
    42. 郑晓琳,刘启亮,刘文凯,吴智慧. 智能卡和出租车轨迹数据中蕴含城市人群活动模式的差异性分析. 地球信息科学学报. 2020(06): 1268-1281 .
    43. 郑小红,龙军,蔡志平. 关于网约车订单分配策略的综述. 计算机工程与科学. 2020(07): 1267-1275 .
    44. 丁彦文,许捍卫,汪成昊. 融合OSM路网与POI数据的城市功能区识别研究. 地理与地理信息科学. 2020(04): 57-63 .
    45. 吴俊杰,刘冠男,王静远,左源,部慧,林浩. 数据智能:趋势与挑战. 系统工程理论与实践. 2020(08): 2116-2149 .
    46. 乐阳,李清泉,郭仁忠. 融合式研究趋势下的地理信息教学体系探索. 地理学报. 2020(08): 1790-1796 .
    47. 吴俊杰,郑凌方,杜文宇,王静远. 从风险预测到风险溯源:大数据赋能城市安全管理的行动设计研究. 管理世界. 2020(08): 189-202 .
    48. 陈思. 基于人口生命周期的空间比对分析模型研究. 地理空间信息. 2020(12): 24-26+30+6 .
    49. 张艳丰,邹凯,彭丽徽,曹丹. 数字空间视角下智慧城市全景数据画像实证研究. 情报学报. 2020(12): 1330-1339 .
    50. 张奇,成毅,徐立,葛文. 顾及运动特征的滑动窗口轨迹数据压缩改进算法. 测绘科学技术学报. 2020(06): 622-627 .
    51. 王楠,杜云艳,易嘉伟,刘张,王会蒙. 基于手机信令数据的北京市空间品质时空动态分析. 地球信息科学学报. 2019(01): 86-96 .
    52. 金和平,郭创新,许奕斌,廖伟涵. 能源大数据的系统构想及应用研究. 水电与抽水蓄能. 2019(01): 1-13 .
    53. 康军,郭佳豪,段宗涛,唐蕾,张凡. 大规模轨迹数据并行化地图匹配算法. 测控技术. 2019(02): 98-102 .
    54. 刘艳芳,方飞国,刘耀林,罗名海. 时空大数据在空间优化中的应用. 测绘地理信息. 2019(03): 7-20 .
    55. 刘君. 基于微博签到数据城市热点探测. 合作经济与科技. 2019(17): 12-16 .
    56. 熊文,周钱梅,杨昆,代浩,孙黎. 基于时空相似性的大规模轨迹数据融合技术. 集成技术. 2019(05): 26-33 .
    57. 方华强,颜寒祺,陈波,程承旗. 基于自编码网络的移动轨迹异常检测. 地理信息世界. 2019(05): 41-44+52 .
    58. 马捷,葛岩,蒲泓宇,张云开. 基于多源数据的智慧城市数据融合框架. 图书情报工作. 2019(15): 6-12 .
    59. 张贝娜,冯震华,张丰,杜震洪,刘仁义,周芹. 基于时空多视图BP神经网络的城市空气质量数据补全方法研究. 浙江大学学报(理学版). 2019(06): 737-744 .
    60. 孙勇,王会蒙,靳奉祥,杜云艳,季民,易嘉伟. 一种基于空间-拓扑结构相似性的复杂轨迹聚类算法. 地球信息科学学报. 2019(11): 1669-1678 .
    61. 牟乃夏,徐玉静,张恒才,陈洁,张灵先,刘希亮. 移动轨迹聚类方法研究综述. 测绘通报. 2018(01): 1-7 .
    62. 杨喜平,方志祥. 移动定位大数据视角下的人群移动模式及城市空间结构研究进展. 地理科学进展. 2018(07): 880-889 .
    63. 林楠,尹凌,赵志远. 基于滑动窗口的手机定位数据个体停留区域识别算法. 地球信息科学学报. 2018(06): 762-771 .
    64. 申兴发,王兰迪. 公共自行车系统的租赁点聚类与功能识别. 计算机工程. 2018(01): 44-50 .
    65. 尹馨予,许一男. 基于手机信令数据的城市人口动态分布感知模型研究. 内蒙古科技与经济. 2018(06): 73-74 .
    66. 姚迪,张超,黄建辉,陈越新,毕经平. 时空数据语义理解:技术与应用. 软件学报. 2018(07): 2018-2045 .
    67. 彭雨滕,马林兵,周博,何桂林. 自发地理信息研究热点分析. 世界地理研究. 2018(01): 129-140 .
    68. 高磊,黄家宽,姜晓许,刘兴权. 基于个体移动数据的城市活力实证研究. 科技创新与生产力. 2018(05): 64-67+72 .
    69. 陈天宇. 城市计算在智慧城市建设中的应用分析. 通讯世界. 2018(08): 64-65 .
    70. 谷岩岩,焦利民,董婷,王艳东,许刚. 基于多源数据的城市功能区识别及相互作用分析. 武汉大学学报(信息科学版). 2018(07): 1113-1121 .
    71. 赵志远,尹凌,方志祥,萧世伦,杨喜平. 轨迹数据的时间采样间隔对停留识别和出行网络构建的影响. 武汉大学学报(信息科学版). 2018(08): 1152-1158 .
    72. 徐小辉. 智慧城市环境下传感器数据融合研究. 信息与电脑(理论版). 2018(13): 164-165+170 .
    73. 冯慧芳,柏凤山,徐有基. 基于轨迹大数据的城市交通感知和路网关键节点识别. 交通运输系统工程与信息. 2018(03): 42-47+54 .
    74. 胡清华,陆晨,胡倩,魏淑珍,蒋东升,黄艳艳. 以福州为示范的城市空气质量实时精细化模拟与预报. 中国环境管理. 2018(03): 99-104 .
    75. 潘志宏,万智萍,谢海明. 跨平台框架下基于移动感知的智慧公交应用研究. 计算机工程与应用. 2018(19): 243-247+260 .
    76. 杜龙飞,田兆君,鲁义,银亚飞. 大数据时代下智慧城市公共安全应急管理现状分析及对策. 安全. 2018(11): 50-52 .
    77. 邬群勇,邹智杰,邱端昇,苏克云. 结合出租车轨迹数据的城市道路拥堵时空分析. 福州大学学报(自然科学版). 2018(05): 724-731 .
    78. 张振宇,陈安. 技术革命与应急管理变革:路径、实践与未来. 天津商业大学学报. 2018(05): 16-21+28 .
    79. 姜鹏,曹琳,倪砼. 新一代人工智能推动城市规划变革的趋势展望. 规划师. 2018(11): 5-12 .
    80. 廖伟华,聂鑫. 城市计算视角下的空间粗糙关联规则方法研究. 热带地理. 2018(06): 751-758 .
    81. 崔羽,顾琼,张霄兵,李鹏飞,唐明. 转型下城乡规划编制的信息化顶层设计. 规划师. 2018(12): 79-83 .
    82. 怀松垚,陈筝,刘颂. 基于新数据、新技术的城市公共空间品质研究. 城市建筑. 2018(06): 12-20 .
    83. 雷程程,张岸,齐清文,苏惠敏. 格网化的位置微博数据抓取与人群信息提取. 测绘科学. 2017(02): 125-129 .
    84. 白晓辉,陈思,谭鲁渊,王红. 规划实施动态评估技术支撑体系研究. 测绘通报. 2017(02): 112-115 .
    85. 孔令礼. 面向智慧城市的大数据中心建设方案设计. 测绘通报. 2017(10): 143-147 .
    86. 张炫铤,李爽. 基于LBS的移动餐饮信息系统设计与研究. 城市地理. 2017(02): 175 .
    87. 王宇. 以数据为中心的城市交通研究进展. 城市地理. 2017(24): 16-17 .
    88. 杜圣东,杨燕,滕飞. 交通大数据:一种基于微服务的敏捷处理架构设计. 大数据. 2017(03): 53-67 .
    89. 朱燕,李宏伟,樊超,许栋浩,施方林. 基于聚类的出租车异常轨迹检测. 计算机工程. 2017(02): 16-20 .
    90. 王桐,王鹏,柳冰忆. 城市环境下跨层VANET路由协议研究. 计算机工程. 2017(11): 55-65 .
    91. 马新强,刘勇,范婧,黄羿,吴茂念,张明义. 大数据驱动下智慧城市建设的若干思考. 科技导报. 2017(21): 131-137 .
    92. 曾子明,杨倩雯. 城市突发事件智慧管控情报体系构建研究. 情报理论与实践. 2017(10): 51-55+79 .
    93. 唐炉亮,杨雪,靳晨,刘章,李清泉. 基于约束高斯混合模型的车道信息获取. 武汉大学学报(信息科学版). 2017(03): 341-347 .
    94. 王亚飞,杨卫东,徐振强. 基于出租车轨迹的载客热点挖掘. 信息与电脑(理论版). 2017(16): 141-143 .
    95. 蒋云良,董墨萱,范婧,高少文,刘勇,马新强. 基于POI数据的城市功能区识别方法研究. 浙江师范大学学报(自然科学版). 2017(04): 398-405 .
    96. 王淑芳. 基于卫星定位系统的营运车辆时空特征研究综述. 交通信息与安全. 2017(01): 19-25 .
    97. 汪飞,张繁,吴斐然,顾天瑜,高思远,赵烨,鲍虎军. 面向多源城市出行数据的可视化查询模型. 计算机辅助设计与图形学学报. 2016(01): 25-31 .
    98. 黄文彬,吴家辉,徐山川,王军. 数据驱动的移动用户行为研究框架与方法分析. 情报科学. 2016(07): 14-20+40 .
    99. 褚冬竹,马可,魏书祥. “行为—空间/时间”研究动态探略——兼议城市设计精细化趋向. 新建筑. 2016(03): 92-98 .
    100. 牟乃夏,张恒才,陈洁,张灵先,戴洪磊. 轨迹数据挖掘城市应用研究综述. 地球信息科学学报. 2015(10): 1136-1142 .
    101. 吴运超,黄晓春,王浩然,崔浩,鲁旭. 面向智慧城市的数字规划发展思考与实践. 《规划师》论丛. 2015(00): 101-107 .
    102. 刘俊岭,王薇,于戈,孙焕良,许鸿斐. 空间区域中对象流动模式构建方法研究. 计算机工程与科学. 2015(10): 1899-1908 .
    103. 张立嘉. 城市计算研究. 山西科技. 2015(04): 127-129 .

    Other cited types(209)

Catalog

    Article views PDF downloads Cited by(312)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return