[1] [1] ZHANGZuxun， JIANGHuiwei， PANGShiyan， et al. Review and prospect in change detection of multi-temporal remote sensing images［J］. Acta Geodaetica et Cartographica Sinica， 2022， 51（7）： 1091-1107.

[2] [2] ZHANGLiangpei， WUChen. Advance and future development of change detection for multi-temporal remote sensing imagery［J］. Acta Geodaetica et Cartographica Sinica， 2017， 46（10）： 1447-1459.

[3] [3] SINGHA. Review Article Digital change detection techniques using remotely-sensed data［J］. International Journal of Remote Sensing，1989，10（6）：989-1003.

[4] [4] LIDeren. Change detection from remote sensing images［J］. Geomatics and Information Science of Wuhan University， 2003， 28（Sup.1）：7-12.

[5] [5] DENGC B， ZHUZ. Continuous subpixel monitoring of urban impervious surface using Landsat time series［J］. Remote Sensing of Environment，2020，238：110929. DOI：10.1016/j.rse.2018.10.011

[6] [6] CAOY X， HUANGX. A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels［J］. Remote Sensing of Environment， 2023， 284： 113371. DOI： 10.1016/j.rse.2022.113371

[7] [7] XIAOP F，SHENGG W，ZHANGX L，et al.Direction-dominated change vector analysis for forest change detection［J］.International Journal of Applied Earth Observation and Geoinformation，2021，103：102492. DOI：10.1016/j.jag. 2021. 102492

[8] [8] PELLETIERF， CARDILLEJ A， WULDERM A， et al. Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat［J］. Remote Sensing of Environment， 2024， 301： 113931. DOI： 10.1016/j.rse.2023.113931

[9] [9] WANGX， FANX M， XUQ， et al. Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2022， 187： 225-239. DOI： 10.1016/j.isprsjprs.2022.03.011

[10] [10] YANGC H， WANGX， GUOS C， et al. Mapping co-seismic landslides in vegetated areas by incorporating tri-temporal logical information in change detection method［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 4706019. DOI： 10.1109/TGRS.2024.3427145

[11] [11] JIShunping， TIANSiqi， ZHANGChi. Urban Land Cover Classification and change detection using fully atrous convolutional neural network［J］. Geomatics and Information Science of Wuhan University， 2020， 45（2）： 233-241.

[12] [12] WANGChao， WANGShuai， CHENXiao， et al. Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module［J］. Acta Geodaetica et Cartographica Sinica， 2023， 52（2）： 283-296.

[13] [13] HEH X， YANJ N， LIANGD， et al. Time-series land cover change detection using deep learning-based temporal semantic segmentation［J］. Remote Sensing of Environment， 2024， 305： 114101. DOI： 10.1016/j.rse.2024.114101

[14] [14] LIUT， YANGL X， LUNGAD. Change detection using deep learning approach with object-based image analysis［J］. Remote Sensing of Environment， 2021， 256： 112308. DOI： 10.1016/j.rse.2021.112308

[15] [15] WANGY， DUB， RUL X， et al. Scene change detection VIA deep convolution canonical correlation analysis neural network［C］∥Proceedings of the IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE， 2019： 198-201.. DOI： 10.1109/igarss.2019.8898211

[16] [16] WUC， ZHANGL F， ZHANGL P. A scene change detection framework for multi-temporal very high resolution remote sensing images［J］. Signal Processing， 2016， 124： 184-197. DOI： 10.1016/j.sigpro.2015.09.020

[17] [17] WUC， DUB， ZHANGL P. Fully convolutional change detection framework with generative adversarial network for unsupervised， weakly supervised and regional supervised change detection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2023， 45（8）： 9774-9788. DOI： 10.1109/TPAMI.2023.3237896

[18] [18] FANGH， GUOS C， WANGX， et al. Automatic urban scene-level binary change detection based on a novel sample selection approach and advanced triplet neural network［J］. IEEE Transactions on Geoscience and Remote Sensing， 2023， 61： 5601518. DOI： 10.1109/TGRS.2023.3235917

[19] [19] LIUR C， JIANGD W， ZHANGL L， et al. Deep depthwise separable convolutional network for change detection in optical aerial images［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2020，13：1109-1118. DOI：10.1109/JSTARS.2020.2974276

[20] [20] PENGD F，ZHANGY J，GUANH Y. End-To-End change detection for high resolution satellite images using improved UNet++［J］. Remote Sensing， 2019， 11（11）： 1382. DOI： 10.3390/rs11111382

[21] [21] JIANGY， HUL， ZHANGY M， et al. WRICNet： A weighted rich-scale inception coder network for remote sensing image change detection［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022， 60： 4705313. DOI： 10.1109/TGRS.2022.3145652

[22] [22] LÜZ Y， HUANGH T， GAOL P， et al. Simple multiscale UNet for change detection with heterogeneous remote sensing images［J］. IEEE Geoscience and Remote Sensing Letters， 2022， 19： 2504905. DOI： 10.1109/LGRS.2022.3173300

[23] [23] LIUF， LIUY G， LIUJ， et al. Candidate-aware and change-guided learning for remote sensing change detection［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 5624919. DOI： 10.1109/TGRS.2024.3400215

[24] [24] ZHENGZ， ZHONGY F， TIANS Q， et al. ChangeMask： Deep multi-task encoder-transformer-decoder architecture for semantic change detection［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2022， 183： 228-239. DOI： 10.1016/j.isprsjprs.2021.10.015

[25] [25] JINGW， CHIK C， LIQ， et al. ChangeRD： A registration-integrated change detection framework for unaligned remote sensing images［J］. ISPRS Journal of Photogrammetry and Remote Sensing，2025，220：64-74. DOI：10.1016/j.isprsjprs. 2024.11.019

[26] [26] GAOJianwen， GUANHaiyan， PENGDaifeng， et al. Local-global semantic feature enhancement model for remote sensing imagery change detection［J］. Journal of Geo-Information Science， 2023， 25（3）： 625-637.

[27] [27] ZHAOXiang， WANGTao， ZHANGYan， et al. Remote sensing image change detection based on improved DeepLabv3+ Siamese network［J］. Journal of Geo-Information Science， 2022， 24（8）： 1604-1616.

[28] [28] ZHAOX Y，ZHAOK Y，LIS Y，et al.GeSANet：Geospatial-awareness network for VHR remote sensing image change detection［J］.IEEE Transactions on Geoscience and Remote Sen-sing，2023，61：5402814. DOI：10.1109/TGRS.2023.3272550

[29] [29] XUX T， YANGZ， LIJ J. AMCA： Attention-guided multiscale context aggregation network for remote sensing image change detection［J］. IEEE Transactions on Geoscience and Remote Sensing，2023，61：5908619. DOI：10.1109/TGRS. 2023.3272006

[30] [30] ZHANGM W， LIQ， MIAOY L， et al. Difference-guided aggregation network with multiimage pixel contrast for change detection［J］. IEEE Transactions on Geoscience and Remote Sensing，2023，61：5611114. DOI：10.1109/TGRS. 2023. 3278739

[31] [31] LIUS B， ZHAOD X， ZHOUY H， et al. Full-scale change detection network for remote sensing images based on deep feature fusion［J］. IEEE Transactions on Geoscience and Remote Sensing，2025，63：5617113. DOI：10.1109/TGRS. 2025.3555171

[32] [32] CHENY X， NINGX G， ZHANGR Q， et al. ESMII-Net： An edge-synergy and multidimensional information interaction network for remote sensing change detection［J］. International Journal of Applied Earth Observation and Geoinformation， 2025， 139： 104507. DOI： 10.1016/j.jag.2025.104507

[33] [33] CHENH， WUC， DUB， et al. Change detection in multisource VHR images via deep Siamese convolutional multiple-layers recurrent neural network［J］. IEEE Transactions on Geoscience and Remote Sensing， 2020， 58（4）： 2848-2864. DOI： 10.1109/TGRS.2019.2956756

[34] [34] ZHANGC X， YUEP， TAPETED， et al. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2020， 166： 183-200. DOI： 10.1016/j.isprsjprs.2020.06.003

[35] [35] LIANGZheheng， LIXiao， DENGPeng， et al. Remote sensing image change detection fusion method integrating multi-scale feature attention［J］. Acta Geodaetica et Cartographica Sinica， 2022， 51（5）： 668-676.

[36] [36] QIANS Y， XUEZ H， JIAM M， et al. Temporal-spectral-semantic-aware convolutional transformer network for multi-class tidal wetland change detection in Greater Bay Area［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2024， 216： 126-141. DOI： 10.1016/j.isprsjprs.2024.07.024

[37] [37] JINGW， BAIH C， SONGB B， et al. HeteCD： Feature consistency alignment and difference mining for heterogeneous remote sensing image change detection［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2025， 223： 317-327. DOI： 10.1016/j.isprsjprs.2025.03.008

[38] [38] HOUX， BAIY P， LIY， et al. High-resolution triplet network with dynamic multiscale feature for change detection on satellite images［J］. ISPRS Journal of Photogrammetry and Remote Sensing，2021，177：103-115. DOI：10.1016/j.isprs-jprs.2021.05.001

[39] [39] ZHAOY， CHENP， CHENZ C， et al. A triple-stream network with cross-stage feature fusion for high-resolution image change detection［J］. IEEE Transactions on Geoscience and Remote Sensing，2023，61：5600417. DOI：10.1109/TGRS. 2022.3233849

[40] [40] LÜZ Y， YANGT， ZHONGP D， et al. Hierarchical feature fusion triple network for change detection with bitemporal remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2025， 63： 4401512. DOI： 10.1109/TGRS.2025.3525811

[41] [41] SAHAS， BOVOLOF， BRUZZONEL. 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. DOI：10.1109/TGRS. 2018.2886643

[42] [42] ZHANT， GONGM G， JIANGX M， et al. Unsupervised scale-driven change detection with deep spatial–spectral features for VHR images［J］. IEEE Transactions on Geoscience and Remote Sensing，2020，58（8）：5653-5665. DOI：10.1109/TGRS.2020.2968098

[43] [43] CHENQ， YUEP， XUY J， et al. Feature-selection-based unsupervised transfer learning for change detection from VHR optical images［J］.Remote Sensing，2024，16（18）：3507. DOI： 10.3390/rs16183507

[44] [44] SAHAS，MOUL C，QIUC P，et al. Unsupervised deep joint segmentation of multitemporal high-resolution images［J］. IEEE Transactions on Geoscience and Remote Sensing，2020，58（12）：8780-8792. DOI：10.1109/TGRS.2020.2990640

[45] [45] WUC， CHENH， DUB， et al. Unsupervised change detection in multitemporal VHR images based on deep Kernel PCA convolutional mapping network［J］. IEEE Transactions on Cybernetics，2022，52（11）：12084-12098. DOI：10.1109/TCYB. 2021.3086884

[46] [46] ZHANGXinlong， CHENXiuwan， LIFei， et al. Change detection method for high resolution remote sensing images using deep learning［J］. Acta Geodaetica et Cartographica Sinica， 2017， 46（8）： 999-1008.

[47] [47] GONGM G， YANGY L， ZHANT， et al. A generative discriminatory classified network for change detection in multispectral imagery［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2019， 12（1）： 321-333. DOI： 10.1109/JSTARS.2018.2887108

[48] [48] DUB， RUL X， WUC， et al. Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2019， 57（12）： 9976-9992. DOI： 10.1109/TGRS.2019.2930682

[49] [49] GONGM G， YANGH L， ZHANGP Z. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2017， 129： 212-225. DOI： 10.1016/j.isprsjprs.2017.05.001

[50] [50] FANGH， DUP J， WANGX. A novel unsupervised binary change detection method for VHR optical remote sensing imagery over urban areas［J］. International Journal of Applied Earth Observation and Geoinformation， 2022， 108： 102749. DOI： 10.1016/j.jag.2022.102749

[51] [51] LIQ X， MUT K， TUNIYAZIA， et al. Progressive pseudo-label framework for unsupervised hyperspectral change detection［J］. International Journal of Applied Earth Observation and Geoinformation， 2024， 127： 103663. DOI： 10.1016/j.jag.2024.103663

[52] [52] TANGX， ZHANGH Y， MOUL C， et al. An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning［J］. IEEE Transactions on Geoscience and Remote Sensing， 2021， 60： 5609715. DOI： 10.1109/TGRS.2021.3106381

[53] [53] LIQ X， GONGH， DAIH S， et al. Unsupervised hyperspectral image change detection via deep learning self-generated credible labels［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2021， 14： 9012-9024.

[54] [54] CHENL S，LIS H，ZHUE Y.An object-based change detection method considering temporal-spatial similarity in long time series［J］. IEEE Transactions on Geoscience and Remote Sensing，2025，63：4503910. DOI：10.1109/TGRS. 2025. 3544094

[55] [55] WANGM Y， TANK， JIAX P， et al. A deep Siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images［J］. Remote Sensing， 2020， 12（2）： 205. DOI： 10.3390/rs12020205

[56] [56] LEIY， LIUX D， SHIJ， et al. Multiscale superpixel segmentation with deep features for change detection［J］. IEEE Access， 2019， 7： 36600-36616.

[57] [57] FENGWenqing， ZHANGYongjun. Object-oriented change detection for remote sensing images based on multiscale fusion［J］. Acta Geodaetica et Cartographica Sinica， 2015， 44（10）： 1142-1151.

[58] [58] LIUXuanguang， LIMengmeng， WANGXiaoqin， et al. Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images［J］. National Remote Sensing Bulletin， 2024， 28（2）： 437-454.

[59] [59] GONGM G， ZHANT， ZHANGP 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] [60] ZHANGX Z， LIUG， ZHANGC， et al. Two-phase object-based deep learning for multi-temporal SAR image change detection［J］. Remote Sensing， 2020， 12（3）： 548. DOI： 10.3390/rs12030548

[61] [61] ZHANGH， LIUW， NIUH， et al. Land cover change detection based on vector polygons and deep learning with high-resolution remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing，2023，62：4402218. DOI：10. 1109/tgrs.2023.3346958

[62] [62] LIUJ L， ZHOUW X， GUANH Y， et al. Similarity learning for land use scene-level change detection［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2024， 17： 6501-6513.

[63] [63] ZHOUWeixun，LIUJinglei，PENGDaifeng，et al. MtSCCD： Land-use scene classification and change-detection dataset for deep learning［J］. National Remote Sensing Bulletin， 2024， 28（2）： 321-333.

[64] [64] RUL X， DUB， WUC. Multi-temporal scene classification and scene change detection with correlation based fusion［J］. IEEE Transactions on Image Processing， 2020， 30： 1382-1394. DOI： 10.1109/TIP.2020.3039328

[65] [65] FANGH，GUOS C，LINC，et al. Scene-level change detection by integrating VHR images and POI data using a multiple-branch fusion network［J］.Remote Sensing Letters，2023， 14（8）： 808-820. DOI： 10.1080/2150704X.2023.2242588

[66] [66] SHIS N， ZHONGY F， LIUY H， et al. Cross-temporal high spatial resolution urban scene classification and change detection based on a class-weighted deep adaptation network［J］. Urban Informatics，2024，3（1）：3. DOI：10.1007/s44212-023-00029-1

[67] [67] XIEF， LIAOZ P， TANJ B， et al. MSFCN： A multiscale feature correlation network for remote sensing image scene change detection［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2025， 18： 8275-8299.

[68] [68] ZHOUW X， LIUJ L， HUANGX， et al. Monitoring scene-level land use changes with similarity-assisted change detection network［J］. International Journal of Remote Sensing， 2025，46（4）：1594-1621. DOI：10.1080/01431161. 2024. 2433758

[69] [69] RUL X， WUC， DUB， et al. Deep canonical correlation analysis network for scene change detection of multi-temporal VHR imagery［C］∥Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images （MultiTemp）. IEEE， 2019： 1-4. DOI： 10.1109/Multi-Temp.2019.8866943

[70] [70] FANGH， GUOS C， ZHANGP， et al. Scene change detection by differential aggregation network and class probability-based fusion strategy［J］. IEEE Transactions on Geoscience and Remote Sensing， 2023， 61： 5406918. DOI： 10.1109/TGRS.2023.3317701

[71] [71] WANGJ， ZHONGY F， ZHANGL P. Contrastive scene change representation learning for high-resolution remote sensing scene change detection［J］. IEEE Transactions on Geoscience and Remote Sensing，2024，62：5618118. DOI： 10.1109/TGRS.2024.3370556

[72] [72] NIELSENA A， CONRADSENK， SIMPSONJ J. Multivariate Alteration Detection （MAD） and MAF postprocessing in multispectral， bitemporal image data： New approaches to change detection studies［J］. Remote Sensing of Environment， 1998， 64（1）： 1-19. DOI： 10.1016/S0034-4257（97）00162-4

[73] [73] NIELSENA 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. DOI： 10.1109/tip.2006.888195

[74] [74] CELIKT. 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. DOI： 10.1109/LGRS.2009.2025059

[75] [75] WUC， DUB， ZHANGL P. Slow feature analysis for change detection in multispectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing， 2014， 52（5）： 2858-2874. DOI： 10.1109/TGRS.2013.2266673

[76] [76] SIMONYANK， ZISSERMANA. Very deep convolutional networks for large-scale image recognition［EB/OL］. 2014： 1409.1556. https：∥arxiv.org/abs/1409.1556v6. https：∥arxiv.org/abs/1409.1556v6

[77] [77] HEK M，ZHANGX Y，RENS Q，et al. Deep residual learning for image recognition［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition （CVPR）.IEEE，2016：770-778. DOI：10.1109/CVPR.2016.90

[78] [78] HUANGG， LIUZ， VAN DER MAATENL， et al. Densely connected convolutional networks［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2017： 2261-2269. DOI： 10.1109/CVPR.2017.243

[79] [79] HOWARDA， SANDLERM， CHENB， et al. Searching for MobileNetV3［C］∥Proceedings of the IEEE/CVF International Conference on Computer Vision （ICCV）. IEEE， 2019： 1314-1324.. DOI： 10.1109/iccv.2019.00140

[80] [80] ZHANY， FUK， YANM L， 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. DOI： 10.1109/LGRS.2017.2738149

[81] [81] GAOY H， GAOF， DONGJ Y， et al. SAR image change detection based on multiscale capsule network［J］. IEEE Geoscience and Remote Sensing Letters， 2021， 18（3）： 484-488. DOI： 10.1109/LGRS.2020.2977838