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]WANG Yanli, DONG Zhipeng, WANG Mi. Ulva polifera Detection from High Resolution Remote Sensing Images Based on Dual-Path Convolutional Neural Networks[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2261-2270. DOI: 10.13203/j.whugis20230159
    [2]ZHANG Chunsen, LI Guojun, CUI Weihong. A Change Detection Method for Remote Sensing Image Based on Vector Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 309-317. DOI: 10.13203/j.whugis20190131
    [3]WEI Lifei, ZHONG Yanfei, ZHANG Liangpei, LI Pingxiang. Remote Sensing Image Change Detection Based onMulti-band Information Fusion[J]. Geomatics and Information Science of Wuhan University, 2014, 39(1): 8-11.
    [4]LI Liang, SHU Ning, GONG Yan. Remote Sensing Image Change Detection and Change Type Recognition Based on Spatiotemporal Relationship[J]. Geomatics and Information Science of Wuhan University, 2013, 38(5): 533-537.
    [5]YOU Hongjian. SAR Change Detection by Multi-scale Segmentation and Optimization[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 531-534.
    [6]YUAN Xiuxiao, JI Shunping. Change Detection Using Aerial Images with POS Data[J]. Geomatics and Information Science of Wuhan University, 2007, 32(4): 283-286.
    [7]ZHANG Xiaodong, LI Deren, GONG Jianya, QIN Qianqing. A Change Detection Method of Integrating Remote Sensing and GIS[J]. Geomatics and Information Science of Wuhan University, 2006, 31(3): 266-269.
    [8]FANG Shenghui, DIAN Yuanyong, LI Wei. Change Detection Based on Both Edges and Gray[J]. Geomatics and Information Science of Wuhan University, 2005, 30(2): 135-138.
    [9]LI Deren. Change Detection from Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2003, 28(S1): 7-12.
    [10]Fang Zhen, Zhang Jianqing, Zhang Zuxun. Change Detection Based on Aerial Image of Urban Area[J]. Geomatics and Information Science of Wuhan University, 1997, 22(3): 240-244.

Catalog

    Article views (6900) PDF downloads (2134) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return