多时相遥感影像的变化检测研究现状与展望

doi:10.11947/j.AGCS.2022.20220070

摘要/Abstract

摘要： 变化检测作为摄影测量与遥感领域的研究热点之一,也是人工智能体系中极具研究价值的技术分支。二者的快速发展与深度融合,已使海量、复杂和多样的遥感数据快速智能化处理成为可能,广泛应用于资源监测、城市规划、灾害评估等诸多领域。随着遥感技术和计算能力的不断革新,变化检测体系也在不断发展和演化。本文主要从几何和语义两个角度对变化检测方法进行了分析和归纳总结,重点分析了几何信息的利用方式及深度神经网络的特征融合方式,随后总结了常用的变化检测公开样本数据集,最后对当前变化检测应用中遇到的核心问题及未来发展趋势进行了梳理与展望。

关键词: 摄影测量, 遥感影像, 多时相, 变化检测, 综述

Abstract: Change detection (CD), as one of the hot spots in the field of photogrammetry and remote sensing, is a technology branch with important research value in artificial intelligence (AI) systems. The rapid development and deep integration of the two subjects have made it possible to automatic and intelligent process the massive, complex and diverse remote sensing datasets. It has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. With the development of the remote sensing and computing power, change detection is also evolving. This paper mainly analyzes and summarizes the current change detection methods from the perspectives of geometry and semantics. We focus on the utilization of geometric information and the feature fusion method of deep neural networks. Furthermore, the paper provides a summary of open datasets derived from different sensors, along with information related to change detection. Finally, we point out the challenges of the change detection and the future trends of the area are summarized and prospected.

Key words: photogrammetry, remote sensing images, multi-temporal, change detection, review

中图分类号:

P237

张祖勋, 姜慧伟, 庞世燕, 胡翔云. 多时相遥感影像的变化检测研究现状与展望[J]. 测绘学报, 2022, 51(7): 1091-1107.

ZHANG Zuxun, JIANG Huiwei, PANG Shiyan, HU Xiangyun. Review and prospect in change detection of multi-temporal remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1091-1107.

参考文献

[1] CHUGHTAI A H, ABBASI H, KARAS I R. A review on change detection method and accuracy assessment for land use land cover[J]. Remote Sensing Applications:Society and Environment, 2021, 22:100482.
[2] JUSTICE C, GUTMAN G, VADREVU K P. NASA land cover and land use change (LCLUC):an interdisciplinary research program[J]. Journal of Environmental Management, 2015, 148:4-9.
[3] BROWN J F, TOLLERUD H J, BARBER C P, et al. Lessons learned implementing an operational continuous United States national land change monitoring capability:the land change monitoring, assessment, and projection (LCMAP) approach[J]. Remote Sensing of Environment, 2020, 238:111356.
[4] 陈军,廖安平,陈晋,等.全球30m地表覆盖遥感数据产品-GlobeLand30[J].地理信息世界, 2017, 24(1):1-8. CHEN Jun, LIAO Anping, CHEN Jin, et al. 30-meter global land cover data product-GlobeLand30[J]. Geomatics World, 2017, 24(1):1-8.
[5] GONG Peng, LIU Han, ZHANG Meinan, et al. Stable classification with limited sample:transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017[J]. Science Bulletin, 2019, 64(6):370-373.
[6] 赵继成.天地图建设与服务[J].地理信息世界, 2014, 21(1):10-11. ZHAO Jicheng. Establishment and service of Tianditu[J]. Geomatics World, 2014, 21(1):10-11.
[7] MOONEY P, CORCORAN P. The annotation process in OpenStreetMap[J]. Transactions in GIS, 2012, 16(4):561-579.
[8] WOODCOCK C E, ALLEN R, ANDERSON M, et al. Free access to Landsat imagery[J]. Science, 2008, 320(5879):1011.
[9] GONG Peng, BIGING G S, STANDIFORD R. Technical note:use of digital surface model for hardwood rangeland monitoring[J]. Journal of Range Management, 2000, 53(6):622-626.
[10] MARTHA T R, KERLE N, JETTEN V, et al. Landslide volumetric analysis using cartosat-1-derived DEMs[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3):582-586.
[11] CHAABOUNI-CHOUAYAKH H, D'ANGELO P, KRAUSS T, et al. Automatic urban area monitoring using digital surface models and shape features[C]//Proceedings of 2011 Joint Urban Remote Sensing Event. Munich, Germany:IEEE, 2011:85-88.
[12] CHAABOUNI-CHOUAYAKH H, KRAUSS T, D'ANGELO P, et al. 3D change detection inside urban areas using different digital surface models[C]//Proceedings of 2010 ISPRS Technical Commission Ⅲ Symposium on Photogrammetry Computer Vision and Image Analysis. 2010.
[13] JUNG F. Detecting building changes from multitemporal aerial stereopairs[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58(3-4):187-201.
[14] SASAGAWA A, BALTSAVIAS E, AKSAKAL S K, et al. Investigation on automatic change detection using pixel-changes and DSM-changes with ALOS-PRISM triplet images[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, XL-7/W2:213-217.
[15] PANG Shiyan, HU Xiangyun, WANG Zizheng, et al. Object-based analysis of airborne LiDAR data for building change detection[J]. Remote Sensing, 2014, 6(11):10733-10749.
[16] DE GÉLIS I, LEFōVRE S, CORPETTI T. 3D urban change detection with point cloud siamese networks[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, 43:879-886.
[17] MENDERES A, ERENER A, SARP G. Automatic detection of damaged buildings after earthquake hazard by using remote sensing and information technologies[J]. Procedia Earth and Planetary Science, 2015, 15:257-262.
[18] TURKER M, CETINKAYA B. Automatic detection of earthquake-damaged buildings using DEMs created from pre-and post-earthquake stereo aerial photographs[J]. International Journal of Remote Sensing, 2005, 26(4):823-832.
[19] TIAN Jiaojiao, CHAABOUNI-CHOUAYAKH H, REINARTZ P, et al. Automatic 3D change detection based on optical satellite stereo imagery[J]. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2010, 38(7B):586-591.
[20] TIAN Jiaojiao, CUI Shiyong, REINARTZ P. Building change detection based on satellite stereo imagery and digital surface models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):406-417.
[21] GRUEN A, AKCA D. Least squares 3D surface and curve matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2005, 59(3):151-174.
[22] WASER L T, BALTSAVIAS E, ECKER K, et al. Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images[J]. Remote Sensing of Environment, 2008, 112(5):1956-1968.
[23] QIN Rongjun, TIAN Jiaojiao, REINARTZ P. 3D change detection-Approaches and applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 122:41-56.
[24] PANG Shiyan, HU Xiangyun, CAI Zhongliang, et al. Building change detection from bi-temporal dense-matching point clouds and aerial images[J]. Sensors, 2018, 18(4):966.
[25] 杨钰琪,陈驰,杨必胜,等.基于UAV影像密集匹配点云多层次分割的建筑物层高变化检测[J].武汉大学学报(信息科学版), 2021, 46(4):489-496. YANG Yuqi, CHEN Chi, YANG Bisheng, et al. 3D change detection of buildings based on multi-level segmentation of dense matching point clouds from UAV images[J]. Geomatics and Information Science of Wuhan University, 2021, 46(4):489-496.
[26] TIAN J, REINARTZ P, D'ANGELO P, et al. Region-based automatic building and forest change detection on Cartosat-1 stereo imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79:226-239.
[27] QIN Rongjun. Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 96:179-192.
[28] PANG Shiyan, HU Xiangyun, ZHANG Mi, et al. Co-segmentation and superpixel-based graph cuts for building change detection from Bi-temporal digital surface models and aerial images[J]. Remote Sensing, 2019, 11(6):729.
[29] ZHANG Haiming, WANG Mingchang, WANG Fengyan, et al. A novel squeeze-and-excitation W-net for 2D and 3D building change detection with multi-source and multi-feature remote sensing data[J]. Remote Sensing, 2021, 13(3):440.
[30] TIAN Jiaojiao, NIELSEN A A, REINARTZ P. Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs[J]. International Journal of Image and Data Fusion, 2015, 6(2):155-169.
[31] TIAN Jiaojiao, CHAABOUNI-CHOUAYAKH H, REINARTZ P. 3D building change detection from high resolution spaceborne stereo imagery[C]//Proceedings of 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping. Xiamen, China:IEEE, 2011:1-7.
[32] LAK A M, ZOEJ M J V, MOKHTARZADE M. A new method for road detection in urban areas using high-resolution satellite images and Lidar data based on fuzzy nearest-neighbor classification and optimal features[J]. Arabian Journal of Geosciences, 2016, 9(5):358.
[33] NEBIKER S, LACK N, DEUBER M. Building change detection from historical aerial photographs using dense image matching and object-based image analysis[J]. Remote Sensing, 2014, 6(9):8310-8336.
[34] MALPICA J A, ALONSO M C, PAPÍ F, et al. Change detection of buildings from satellite imagery and LiDAR data[J]. International Journal of Remote Sensing, 2013, 34(5):1652-1675.
[35] TU Jihui, LI Deren, FENG Wenqing, et al. Detecting damaged building regions based on semantic scene change from multi-temporal high-resolution remote sensing images[J]. ISPRS International Journal of Geo-Information, 2017, 6(5):131.
[36] CHAABOUNI-CHOUAYAKH H, REINARTZ P. Towards automatic 3D change detection inside urban areas by combining height and shape information[J]. Photogrammetrie-Fernerkundung-Geoinformation, 2011(4):205-217.
[37] DU Shouji, ZHANG Yunsheng, QIN Rongjun, et al. Building change detection using old aerial images and new LiDAR data[J]. Remote Sensing, 2016, 8(12):1030.
[38] CHEN Baohua, CHEN Zhixiang, DENG Lei, et al. Building change detection with RGB-D map generated from UAV images[J]. Neurocomputing, 2016, 208:350-364.
[39] HOU Bin, WANG Yunhong, LIU Qingjie. Change detection based on deep features and low rank[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(12):2418-2422.
[40] 张家琦.遥感影像变化检测方法及应用研究[D].北京:中国地质大学(北京), 2015. ZHANG Jiaqi. Study on remote sensing image changedetection methods and application[D]. Beijing:China University of Geosciences (Beijing), 2005.
[41] 彭代锋.基于多特征信息挖掘的对象级光学卫星影像变化检测研究[D].武汉:武汉大学, 2017. PENG Daifeng. Object-based change detection with optical satellite imagery based on multi-features information mining[D]. Wuhan:Wuhan University, 2017.
[42] MAS J F. Monitoring land-cover changes:a comparison of change detection techniques[J]. International Journal of Remote Sensing, 1999, 20(1):139-152.
[43] 唐朴谦,杨建宇,张超,等.基于像素比值的面向对象分类后遥感变化检测方法[J].遥感信息, 2010(1):69-72. TANG Puqian, YANG Jianyu, ZHANG Chao, et al. An object-oriented post-classification remote sensing change detection after the pixel ratio[J]. Remote Sensing Information, 2010(1):69-72.
[44] CELIK T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4):772-776.
[45] DENG J S, WANG K, DENG Y H, et al. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data[J]. International Journal of Remote Sensing, 2008, 29(16):4823-4838.
[46] NIELSEN A A. The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data[J]. IEEE Transactions on Image Processing, 2007, 16(2):463-478.
[47] 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 Sensing, 2007, 45(1):218-236.
[48] BOVOLO F, MARCHESI S, BRUZZONE L. A framework for automatic and unsupervised detection of multiple changes in multitemporal images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(6):2196-2212.
[49] WU Chen, DU Bo, ZHANG Liangpei. Slow feature analysis for change detection in multispectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5):2858-2874.
[50] KHURANA M, SAXENA V. Exploring the effectiveness of various texture features for change detection in remote sensing images[C]//Proceedings of 2017 International Conference on Computer, Communications and Electronics (Comptelix). Jaipur, India:IEEE, 2017:96-100.
[51] HARALICK R M, SHANMUGAM K, DINSTEIN I H. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6):610-621.
[52] SALI E, WOLFSON H. Texture classification in aerial photographs and satellite data[J]. International Journal of Remote Sensing, 1992, 13(18):3395-3408.
[53] 朱朝杰,王仁礼,董广军.基于小波变换的纹理特征变化检测方法研究[J].仪器仪表学报, 2006, 27(S1):46-47. ZHU Chaojie, WANG Renli, DONG Guangjun. A method of texture feature based on wavelet for change detection[J]. Chinese Journal of Scientific Instrument, 2006, 27(S1):46-47.
[54] PADWICK C, PACIFICI F, MARCHISIO G, et al. Fusion of morphological and texture features for high resolution image change detection[C]//Proceedings of the ASPRS 2011 Annual Conference, Milwaukee:[s.n.]. 2011:1-5.
[55] 方圣辉,佃袁勇,李微.基于边缘特征的变化检测方法研究[J].武汉大学学报(信息科学版), 2005, 30(2):135-138. 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.
[56] 钟家强,王润生.一种基于线特征的道路网变化检测算法[J].遥感学报, 2007, 11(1):27-32. ZHONG Jiaqiang, WANG Runsheng. A road network change detection algorithm based on linear feature[J]. Journal of Remote Sensing, 2007, 11(1):27-32.
[57] 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.
[58] MYINT S W, LAM N S N, TYLER J M. Wavelets for urban spatial feature discrimination[J]. Photogrammetric Engineering&Remote Sensing, 2004(7):803-812.
[59] ZHANG Liangpei, HUANG Xin, HUANG Bo, et al. A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10):2950-2961.
[60] PESARESI M, BENEDIKTSSON J A. A new approach for the morphological segmentation of high-resolution satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(2):309-320.
[61] BENEDIKTSSON J A, PESARESI M, AMASON K. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9):1940-1949.
[62] MARCEAU D J. The scale issue in the social and natural sciences[J]. Canadian Journal of Remote Sensing, 1999, 25(4):347-356.
[63] BOVOLO F, BRUZZONE L. A multilevel parcel-based approach to change detection in very high resolution multitemporal images[C]//Proceedings of 2005 IEEE International Geoscience and Remote Sensing Symposium. Seoul, Korea:IEEE, 2005:2145-2148.
[64] 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.
[65] FALCO N, MURA M D, BOVOLO F, et al. Change detection in VHR images based on morphological attribute profiles[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3):636-640.
[66] WANG Biao, CHOI S, BYUN Y, et al. Object-based change detection of very high resolution satellite imagery using the cross-sharpening of multitemporal data[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5):1151-1155.
[67] MA Lei, LI Manchun, BLASCHKE T, et al. Object-based change detection in urban areas:the effects of segmentation strategy, scale, and feature space on unsupervised methods[J]. Remote Sensing, 2016, 8(9):761.
[68] BOCK M, XOFIS P, MITCHLEY J, et al. Object-oriented methods for habitat mapping at multiple scales-Case studies from Northern Germany and Wye Downs, UK[J]. Journal for Nature Conservation, 2005, 13(2-3):75-89.
[69] AGUIRRE-GUTIÉRREZ J, SEIJMONSBERGEN A C, DUIVENVOORDEN J F. Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico[J]. Applied Geography, 2012, 34:29-37.
[70] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2):167-181.
[71] HALL O, HAY G J. A multiscale object-specific approach to digital change detection[J]. International Journal of Applied Earth Observation and Geoinformation, 2003, 4(4):311-327.
[72] MILLER O, PIKAZ A, AVERBUCH A. Objects based change detection in a pair of gray-level images[J]. Pattern Recognition, 2005, 38(11):1976-1992.
[73] DE CHANT T, KELLY M. Individual object change detection for monitoring the impact of a forest pathogen on a hardwood forest[J]. Photogrammetric Engineering&Remote Sensing, 2009, 75(8):1005-1013.
[74] HAZEL G G. Object-level change detection in spectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3):553-561.
[75] BLASCHKE T. Towards a framework for change detection based on image objects[J]. G ttinger Geographische Abhandlungen, 2005, 113:1-9.
[76] LI Jiang, 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.
[77] DESCLÉE B, BOGAERT P, DEFOURNY P. Forest change detection by statistical object-based method[J]. Remote Sensing of Environment, 2006, 102(1-2):1-11.
[78] BONTEMPS S, BOGAERT P, TITEUX N, et al. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution[J]. Remote Sensing of Environment, 2008, 112(6):3181-3191.
[79] SEO P H, LEE J, JUNG D, et al. Attentive semantic alignment with offset-aware correlation kernels[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany:Springer, 2018:367-383.
[80] GONG Jinqi, HU Xiangyun, PANG Shiyan, et al. Roof-cut guided localization for building change detection from imagery and footprint map[J]. Photogrammetric Engineering&Remote Sensing, 2019, 85(8):543-558.
[81] ÖZCAN A H, VNSALAN C, REINARTZ P. A systematic approach for building change detection using multi-source data[C]//Proceedings of the 22nd Signal Processing and Communications Applications Conference (SIU). Trabzon, Turkey:IEEE, 2014:477-480.
[82] XIE Zhiwei, WANG Min, HAN Yaohui, et al. Hierarchical decision tree for change detection using high resolution remote sensing images[C]//Proceedings of the 6th International Conference on Geo-Informatics in Sustainable Ecosystem and Society. Handan, China:Springer, 2018:176-184.
[83] 刘波.基于SVDD特征选择的随机森林高分辨率遥感影像变化检测[D].兰州:兰州交通大学, 2018. LIU Bo. High resolution remote sensing imagery change detection based on random forest and SVDD feature selection[D]. Lanzhou:Lanzhou Jiaotong University, 2018.
[84] FENG Xiaoxue, LI Peijun. Urban built-up area change detection using multi-band temporal texture and one-class random forest[C]//Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). Shanghai, China:IEEE, 2019:1-4.
[85] DÉSIR C, BERNARD S, PETITJEAN C, et al. A random forest based approach for one class classification in medical imaging[C]//Proceedings of the 3rd International Workshop on Machine Learning in Medical Imaging. Nice, France:Springer, 2012:250-257.
[86] BAI Ting, SUN Kaimin, DENG Shiquan, et al. Multi-scale hierarchical sampling change detection using Random Forest for high-resolution satellite imagery[J]. International Journal of Remote Sensing, 2018, 39(21):7523-7546.
[87] RODRIGUEZ-GALIANO V F, GHIMIRE B, ROGAN J, et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67:93-104.
[88] VAPNIK V. The nature of statistical learning theory[M]. New York:Springer, 1999.
[89] 刘义志,赖华荣,张丁旺,等.多特征混合核SVM模型的遥感影像变化检测[J].国土资源遥感, 2019, 31(1):16-21. LIU Yizhi, LAI Huarong, ZHANG Dingwang, et al. Change detection of high resolution remote sensing image alteration based on multi-feature mixed kernel SVM model[J]. Remote Sensing for Land&Resources, 2019, 31(1):16-21.
[90] BOVOLO F, BRUZZONE L, MARCONCINI M. A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7):2070-2082.
[91] LI Wei, LU Miao, CHEN Xiuwan. Automatic change detection of urban land-cover based on SVM classification[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan, Italy:IEEE, 2015:1686-1689.
[92] YANG Zhigao, QIN Qianqing, ZHANG Qifeng. Change detection in high spatial resolution images based on support vector machine[C]//Proceedings of 2006 IEEE International Symposium on Geoscience and Remote Sensing. Denver, CO:IEEE, 2006:225-228.
[93] LI Peijun, XU Haiqing, GUO Jiancong. Urban building damage detection from very high resolution imagery using OCSVM and spatial features[J]. International Journal of Remote Sensing, 2010, 31(13):3393-3409.
[94] YE Su, CHEN Dongmei, YU Jie. A targeted change-detection procedure by combining change vector analysis and post-classification approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114:115-124.
[95] DAI X L, KHORRAM S. Remotely sensed change detection based on artificial neural networks[J]. Photogrammetric Engineering&Remote Sensing, 1999, 65(10):1187-1194.
[96] GOPAL S, WOODCOCK C. Remote sensing of forest change using artificial neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2):398-404.
[97] 肖平,李德仁.基于人工神经元网络技术的土地利用/覆盖变化探测[J].武汉大学学报(信息科学版), 2002, 27(6):586-590. XIAO Ping, LI Deren. Land use/cover change detection based on artificial neural network[J]. Geomatics and Information Science of Wuhan University, 2002, 27(6):586-590.
[98] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[99] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90.
[100] SÁNCHEZ J, PERRONNIN F. High-dimensional signature compression for large-scale image classification[C]//Proceedings of the CVPR 2011. Colorado Springs, CO:IEEE, 2011:1665-1672.
[101] 王庆.基于深度学习的遥感影像变化检测方法研究[D].武汉:武汉大学, 2019. WANG Qing. Research on remote sensing imagery change detection method based on deep learning[D]. Wuhan:Wuhan University, 2019.
[102] JIANG Huiwei, PENG Min, ZHONG Yuanjun, et al. A survey on deep learning-based change detection from high-resolution remote sensing images[J]. Remote Sensing, 2022, 14(7):1552.
[103] CHENG Gong, HAN Junwei, LU Xiaoqiang. Remote sensing image scene classification:benchmark and state of the art[J]. Proceedings of the IEEE, 2017, 105(10):1865-1883.
[104] WANG Shidong, GUAN Yu, SHAO Ling. Multi-granularity canonical appearance pooling for remote sensing scene classification[J]. IEEE Transactions on Image Processing, 2020, 29:5396-5407.
[105] CHENG Gong, XIE Xingxing, HAN Junwei, et al. Remote sensing image scene classification meets deep learning:challenges, methods, benchmarks, and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3735-3756.
[106] KERDEGARI H, RAZAAK M, ARGYRIOU V, et al. Urban scene segmentation using semi-supervised GAN[C]//Proceedings of SPIE 11155, Image and Signal Processing for Remote Sensing XXV. Strasbourg, France:SPIE, 2019:111551H.
[107] HAZEL G G. Multivariate Gaussian MRF for multispectral scene segmentation and anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3):1199-1211.
[108] CHEN Cheng, FAN Lei. Scene segmentation of remotely sensed images with data augmentation using U-net++[C]//Proceedings of 2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI). Shanghai, China:IEEE, 2021:201-205.
[109] WU Chen, ZHANG Lefei, ZHANG Liangpei. A scene change detection framework for multi-temporal very high resolution remote sensing images[J]. Signal Processing, 2016, 124:184-197.
[110] WANG Yong, DU Bo, RU Lixiang, et al. Scene change detection VIA deep convolution canonical correlation analysis neural network[C]//Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan:IEEE, 2019:198-201.
[111] WU Chen, ZHANG Liangpei, DU Bo. Kernel slow feature analysis for scene change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4):2367-2384.
[112] VOGEL J, SCHIELE B. A semantic typicality measure for natural scene categorization[M]//RASMUSSEN C E, BVLTHOFF H H, SCHÖLKOPF B, et al. Pattern Recognition. Berlin, Heidelberg:Springer, 2004:195-203.
[113] LI Feifei, PERONA P. A Bayesian hierarchical model for learning natural scene categories[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA:IEEE, 2005:524-531.
[114] HUANG Xin, LIU Hui, ZHANG Liangpei. Spatiotemporal detection and analysis of urban villages in mega city regions of china using high-resolution remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7):3639-3657.
[115] LI Jianjun, TANG Peiqi, WU Yong, et al. Scene change detection:semantic and depth information[J]. Multimedia Tools and Applications, 2022, 81(14):19301-19319.
[116] YANG Jun, JIANG Yugang, HAUPTMANN A G, et al. Evaluating bag-of-visual-words representations in scene classification[C]//Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval. Augsburg:ACM, 2007:197-206.
[117] BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2):121-167.
[118] RU Lixiang, DU Bo, WU Chen. Multi-temporal scene classification and scene change detection with correlation based fusion[J]. IEEE Transactions on Image Processing, 2020, 30:1382-1394.
[119] PENATTI O A B, NOGUEIRA K, DOS SANTOS J A. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA:IEEE, 2015:44-51.
[120] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the 3rd International Conference on Learning Representations. San Diego, CA:[s.n.], 2015.
[121] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV:IEEE, 2016:770-778.
[122] SAKURADA K, OKATANI T. Change detection from a street image pair using CNN features and superpixel segmentation[C]//Proceedings of the British Machine Vision Conference (BMVC). Swansea:BMVA Press, 2015:1-12.
[123] SAHA S, BOVOLO F, BRUZZONE L. Unsupervised deep change vector analysis for multiple-change detection in VHR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(6):3677-3693.
[124] ZHAO Jiaojiao, GONG Maoguo, LIU Jia, et al. Deep learning to classify difference image for image change detection[C]//Proceedings of 2014 International Joint Conference on Neural Networks (IJCNN). Beijing, China:IEEE, 2014:411-417.
[125] LIU Jia, GONG Maoguo, QIN Kai, et al. A deep convolutional coupling network for change detection based on heterogeneous optical and radar images[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(3):545-559.
[126] BENEDEK C, SZIRÁNYI T. Change detection in optical aerial images by a multilayer conditional mixed Markov model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(10):3416-3430.
[127] LEI Tao, ZHANG Yuxiao, LV Zhiyong, et al. Landslide inventory mapping from bitemporal images using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(6):982-986.
[128] LEBEDEV M A, VIZILTER Y V, VYGOLOV O V, et al. Change detection in remote sensing images using conditional adversarial networks[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, XLII-2:565-571.
[129] DAUDT R C, LE SAUX B, BOULCH A. Fully convolutional Siamese networks for change detection[C]//Proceedings of the 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece:IEEE, 2018:4063-4067.
[130] ZHAN Yang, FU Kun, YAN Menglong, et al. Change detection based on deep Siamese convolutional network for optical aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10):1845-1849.
[131] JIANG Huiwei, HU Xiangyun, LI Kun, et al. PGA-SiamNet:pyramid feature-based attention-guided Siamese network for remote sensing orthoimagery building change detection[J]. Remote Sensing, 2020, 12(3):484.
[132] LIU Yi, PANG Chao, ZHAN Zongqian, et al. Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutional network model[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5):811-815.
[133] DAUDT R C, LE SAUX B, BOULCH A, et al. Multitask learning for large-scale semantic change detection[J]. Computer Vision and Image Understanding, 2019, 187:102783.
[134] YANG Kunping, XIA Guisong, LIU Zicheng, et al. Asymmetric Siamese networks for semantic change detection in aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:5609818.
[135] GONG Maoguo, ZHAN Tao, ZHANG Puzhao, 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.
[136] GENG Jie, WANG Hongyu, FAN Jianchao, et al. Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering[C]//Proceedings of 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP). Shanghai, China:IEEE, 2017:1-3.
[137] LEI Yu, LIU Xiaodong, SHI Jiao, et al. Multiscale superpixel segmentation with deep features for change detection[J]. IEEE Access, 2019, 7:36600-36616.
[138] BROMLEY J, BENTZ J W, BOTTOU L, et al. Signature verification using a "Siamese" time delay neural network[J]. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(4):669-688.
[139] LEAL-TAIXÉ L, CANTON-FERRER C, SCHINDLER K. Learning by tracking:Siamese CNN for robust target association[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Las Vegas, NV:IEEE, 2016:418-425.
[140] EL AMIN A M, LIU Qingjie, WANG Yunhong. Convolutional neural network features based change detection in satellite images[C]//Proceedings of SPIE 10010, First International Workshop on Pattern Recognition. Tokyo, Japan:SPIE, 2016.
[141] DAUDT R C, LE SAUX B, BOULCH A, et al. Urban change detection for multispectral earth observation using convolutional neural networks[C]//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain:IEEE, 2018:2115-2118.
[142] MOU Lichao, BRUZZONE L, ZHU Xiaoxiang. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2):924-935.
[143] LIU Haobo, LU Hui, MOU Lichao. Learning a transferable change rule from a recurrent neural network for land cover change detection[J]. Remote Sensing, 2016, 8(6):506.
[144] GONG Maoguo, NIU Xudong, ZHANG Puzhao, et al. Generative adversarial networks for change detection in multispectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(12):2310-2314.
[145] CHEN Hongruixuan, WU Chen, DU Bo, et al. Deep Siamese multi-scale convolutional network for change detection in multi-temporal VHR images[C]//Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). Shanghai, China:IEEE, 2019.
[146] FUJITA A, SAKURADA K, IMAIZUMI T, et al. Damage detection from aerial images via convolutional neural networks[C]//Proceedings of 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA). Nagoya, Japan:IEEE, 2017:5-8.
[147] MA Wenping, XIONG Yunta, WU Yue, et al. Change detection in remote sensing images based on image mapping and a deep capsule network[J]. Remote Sensing, 2019, 11(6):626.
[148] PENG Daifeng, BRUZZONE L, ZHANG Yongjun, et al. SemiCDNet:a semisupervised convolutional neural network for change detection in high resolution remote-sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7):5891-5906.
[149] ZHANG Lin, HU Xiangyun, ZHANG Mi, et al. Object-level change detection with a dual correlation attention-guided detector[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 177:147-160.
[150] BOURDIS N, MARRAUD D, SAHBI H. Constrained optical flow for aerial image change detection[C]//Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, BC:IEEE, 2011:4176-4179.
[151] CHEN Hao, SHI Zhenwei. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 2020, 12(10):1662.
[152] JI Shunping, WEI Shiqing, LU Meng. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1):574-586.
[153] GUPTA R, GOODMAN B, PATEL N, et al. Creating xBD:a dataset for assessing building damage from satellite imagery[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, CA:IEEE, 2019:10-17.