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

[5] [5] DENG C B, ZHU Z. 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] CAO Y X, HUANG X. 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] XIAO P F, SHENG G W, ZHANG X 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] PELLETIER F, CARDILLE J A, WULDER M 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] WANG X, FAN X M, XU Q,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] YANG C H, WANG X, GUO S 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

[13] [13] HE H X, YAN J N, LIANG D,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] LIU T, YANG L X, LUNGA D. 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] WANG Y, DU B, RU L 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] WU C, ZHANG L F, ZHANG L 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] WU C, DU B, ZHANG L 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] FANG H, GUO S C, WANG X,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] LIU R C, JIANG D W, ZHANG L 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] PENG D F, ZHANG Y J, GUAN H 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] JIANG Y, HU L, ZHANG Y 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] L Z Y, HUANG H T, GAO L 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] LIU F, LIU Y G, LIU J,et al. Candidate-aware and changeguided 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] ZHENG Z, ZHONG Y F, TIAN S 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] JING W, CHI K C, LI Q,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

[28] [28] ZHAO X Y, ZHAO K Y, LI S Y,et al. GeSANet: Geospatial-awareness network for VHR remote sensing image change detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5402814. DOI: 10.1109/TGRS.2023.3272550

[29] [29] XU X T, YANG Z, LI J 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] ZHANG M W, LI Q, MIAO Y 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] LIU S B, ZHAO D X, ZHOU Y 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] CHEN Y X, NING X G, ZHANG R 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] CHEN H, WU C, DU B,et al. Change detection in multisource VHR images via deep Siamese convolutional multiplelayers recurrent neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2848-2864. DOI: 10.1109/TGRS.2019.2956756

[34] [34] ZHANG C X, YUE P, TAPETE D,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

[36] [36] QIAN S Y, XUE Z H, JIA M M,et al. Temporal-spectralsemantic-aware convolutional transformer network for multiclass 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] JING W, BAI H C, SONG B 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] HOU X, BAI Y P, LI Y,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] ZHAO Y, CHEN P, CHEN Z 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] L Z Y, YANG T, ZHONG P 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] 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. DOI: 10.1109/TGRS.2018.2886643

[42] [42] ZHAN T, GONG M G, JIANG X 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] CHEN Q, YUE P, XU Y 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] SAHA S, MOU L C, QIU C 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] WU C, CHEN H, DU B,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

[47] [47] GONG M G, YANG Y L, ZHAN T,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] DU B, RU L X, WU C,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] GONG M G, YANG H L, ZHANG P 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] FANG H, DU P J, WANG X. 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] LI Q X, MU T K, TUNIYAZI A,et al. Progressive pseudolabel 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] TANG X, ZHANG H Y, MOU L 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] LI Q X, GONG H, DAI H S,et al. Unsupervised hyperspectral image change detectionviadeep learning self-generated credible labels[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9012-9024.

[54] [54] CHEN L S, LI S H, ZHU E 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] WANG M Y, TAN K, JIA X 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] LEI Y, LIU X D, SHI J,et al. Multiscale superpixel segmentation with deep features for change detection[J]. IEEE Access, 2019, 7: 36600-36616.

[59] [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] [60] ZHANG X Z, LIU G, ZHANG C,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] ZHANG H, LIU W, NIU H,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] LIU J L, ZHOU W X, GUAN H 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.

[64] [64] RU L X, DU B, WU C. 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] FANG H, GUO S C, LIN C,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] SHI S N, ZHONG Y F, LIU Y 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] XIE F, LIAO Z P, TAN J 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] ZHOU W X, LIU J L, HUANG X,et al. Monitoring scenelevel 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] RU L X, WU C, DU B,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] FANG H, GUO S C, ZHANG P,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] WANG J, ZHONG Y F, ZHANG L 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] NIELSEN A A, CONRADSEN K, SIMPSON J 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] 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. DOI: 10.1109/tip.2006.888195

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

[75] [75] WU C, DU B, ZHANG L 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] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. 2014: 1409.1556. https://arxiv.org/abs/1409.1556v6.

[77] [77] HE K M, ZHANG X Y, REN S 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] HUANG G, LIU Z, VAN DER MAATEN L,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] HOWARD A, SANDLER M, CHEN B,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] ZHAN Y, FU K, YAN M 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] GAO Y H, GAO F, DONG J 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