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ZHUO Li, YU Wanting, JIA Tongyao, LI Jiafeng. Research Progress of Transformer-based Remote Sensing Image Change Detection[J]. Journal of Beijing University of Technology. DOI: 10.11936/bjutxb2024010034
Citation: ZHUO Li, YU Wanting, JIA Tongyao, LI Jiafeng. Research Progress of Transformer-based Remote Sensing Image Change Detection[J]. Journal of Beijing University of Technology. DOI: 10.11936/bjutxb2024010034

Research Progress of Transformer-based Remote Sensing Image Change Detection

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  • Received Date: January 30, 2024
  • Revised Date: March 29, 2024
  • Available Online: May 15, 2025
  • Due to complex factors, including illumination, seasons, phenology, solar height and angle changes, as well as the scattered and diverse nature of the target areas to be detected, along with the variability in scale and direction, remote sensing image change detection enfaces great technical challenges. In recent years, Transformer has demonstrated remarkable success in various fields such as natural language processing, object detection, and image segmentation, becoming a research focus. This paper reviews the latest research progress of Transformer-based remote sensing image change detection, and analyzes two types of methods based on pure transformer and convolutional neural Network (CNN) + Transformer. Then, a comparative analysis of the detection performance of different methods on public datasets is conducted, highliting the respective merits and limitations of various methods. Finally, future possible development trends are discussed.
  • [1]
    RADKE R J, ANDRA S, AL-KOFAHI O, et al. Image change detection algorithms: a systematic survey[J]. IEEE Trans Image Process, 2005, 14(3): 294-307.
    [2]
    WEISMILLER R, KRISTOF S J, SCHOLZ D, et al. Change detection in coastal zone environments[J]. Photogrammetric Engineering and Remote Sensing, 1977, 43(12): 1533-1539.
    [3]
    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. TONG G F, LI Y, DING W L, et al. Review of remote sensing image change detection[J]. Journal of Image and Graphics, 2015, 20(12): 1561-1571. (in Chinese)
    [4]
    LIU S C, MARINELLI D, BRUZZONE L, et al. A review of change detection in multitemporal hyperspectral images: current techniques, applications, and challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(2): 140-158.
    [5]
    任秋如, 杨文忠, 汪传建, 等. 遥感影像变化检测综述[J]. 计算机应用, 2021, 41(8): 2294-2305. REN Q R, YANG W Z, WANG C J, et al. Review of remote sensing image change detection[J]. Journal of Computer Applications, 2021, 41(8): 2294-2305. (in Chinese)
    [6]
    LV Z Y, LIU T F, BENEDIKTSSON J A, et al. Land cover change detection techniques: very-high-resolution optical images: a review[J]. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(1): 44-63.
    [7]
    JIANG H W, PENG M, ZHONG Y J, et al. A survey on deep learning-based change detection from high-resolution remote sensing images[J]. Remote Sensing, 2022, 14(7): 1552.
    [8]
    SHI W Z, ZHANG M, ZHANG R, et al. Change detection based on artificial intelligence: state-of-the-art and challenges[J]. Remote Sensing, 2020, 12(10): 1688.
    [9]
    MOSER G, ANFINSEN S N, LUPPINO L T, et al. Change detection with heterogeneous remote sensing data: from semi-parametric regression to deep learning[C]//2020 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE, 2020: 3892-3895.
    [10]
    KHELIFI L, MIGNOTTE M. Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis[EB/OL]. [2024-01-09]. https://arxiv.org/abs/2006.05612v1.
    [11]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[EB/OL]. [2024-01-09]. https://arxiv.org/abs/1706.03762.
    [12]
    CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//Computer Vision -ECCV 2020. Cham: Springer, 2020: 213-229.
    [12]
    CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//European Conference on Computer Vision. Cham: Springer, 2020: 213-229.
    [13]
    CHEN H, QI Z P, SHI Z W. Remote sensing image change detection with transformers[EB/OL]. [2024-01-09]. https://arxiv.org/abs/2103.00208v3.
    [14]
    BANDARA W G C, PATEL V M. A transformer-based Siamesenetwork for change detection[C]//2022 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE, 2022: 207-210.
    [15]
    ZHANG C, WANG L J, CHENG S L, et al. SwinSUNet: pure transformernetwork for remote sensing image change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5224713.
    [16]
    GUO Q, WANG R F, HUANG R, et al. IDET: iterative difference-enhanced transformers for high-quality change detection[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2025, 9(2): 1093-1106.
    [17]
    BANDARA W G C, NAIR N G, PATEL V M. DDPM-CD: remote sensing change detection using denoising diffusion probabilistic models[EB/OL]. [2024-03-09]. https://arxiv.org/abs/2206.11892.
    [18]
    HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[C]//2020 Advances in Neural Information Processing Systems. New York: Curran Associates, 2020: 6840-6851.
    [19]
    FENG J F, YANG X Y, GU Z J, et al. SMBCNet: a transformer-based approach for change detection in remote sensing images through semantic segmentation[J]. Remote Sensing, 2023, 15(14): 3566.
    [20]
    DU R H, FU F Y, LIU W. Transformer-based feature fusion shrinkage method for remote sensing image change detection[C]//20235th International Conference on Geoscience and Remote Sensing Mapping. Piscataway, NJ: IEEE, 2023: 155-159.
    [21]
    TENG Y H, LIU S, SUN W C, et al. A VHR bi-temporal remote-sensing image change detectionnetwork based on Swin transformer[J]. Remote Sensing, 2023, 15(10): 2645.
    [22]
    MA H L, ZHAO L R, LI B Q, et al. Change detection needs neighborhood interaction in transformer[J]. Remote Sensing, 2023, 15(23): 5459.
    [23]
    GUO Q L, ZHANG J P, ZHU S Y, et al. Deep multiscale Siamesenetwork with parallel convolutional structure and self-attention for change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 3131993.
    [24]
    KE Q T, ZHANG P. Hybrid-TransCD: a hybrid transformer remote sensing image change detectionnetwork via token aggregation[J]. ISPRS International Journal of Geo-Information, 2022, 11(4): 263.
    [25]
    SONG F, ZHANG S X, LEI T, et al. MSTDSNet-CD: multiscale Swin transformer and deeply supervisednetwork for change detection of the fast-growing urban regions[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3165885.
    [26]
    JIANG B, WANG Z T, WANG X X, et al. VcT: visual change transformer for remote sensing image change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-14.
    [27]
    WU Y P, LI L, WANG N, et al. CSTSUNet: a cross Swin transformer-based Siamese U-shapenetwork for change detection in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-15.
    [28]
    FENG Y C, JIANG J W, XU H H, et al. Change detection on remote sensing images using dual-branch multilevel intertemporalnetwork[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4401015.
    [28]
    FENG Y C, JIANG J W, XU H H, et al. Change detection on remote sensing images using dual-branch multilevel intertemporalnetwork[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-15.
    [29]
    XU X T, LI J J, CHEN Z. TCIANet: transformer-based context information aggregationnetwork for remote sensing image change detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 1951-1971.
    [30]
    BENEDEK C, SZIRANYI 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.
    [31]
    FUJITA A, SAKURADA K, IMAIZUMI T, et al. Damage detection from aerial images via convolutional neuralnetworks[C]//2017 Fifteenth IAPR International Conference on Machine Vision Applications. Piscataway, NJ: IEEE, 2017: 5-8.
    [32]
    JI S P, WEI S Q, LU M. Fully convolutionalnetworks 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.
    [33]
    LEBEDEV M A, VIZILTER Y V, VYGOLOV O V, et al. Change detection in remote sensing images using conditional adversarialnetworks[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, 422: 565-571.
    [34]
    DAUDT R C, LE SAUX B, BOULCH A, et al. Urban change detection for multispectral earth observation using convolutional neuralnetworks[C]//2018 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ: IEEE, 2018: 2115-2118.
    [35]
    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.
    [36]
    ZHANG C X, YUE P, TAPETE D, et al. A deeply supervised image fusionnetwork for change detection in high resolution bi-temporal remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166: 183-200.
    [37]
    CHEN H, SHI Z W. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 2020, 12(10): 1662.
    [38]
    YANG K P, XIA G S, LIU Z C, et al. Asymmetric Siamesenetworks for semantic change detection in aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 3113912.
    [39]
    GUPTA R, HOSFELT 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. Piscataway, NJ: IEEE, 2019: 10-17.
    [40]
    SHAO R Z, DU C, CHEN H, et al. SUNet: change detection for heterogeneous remote sensing images from satellite and UAV using a dual-channel fully convolutionnetwork[J]. Remote Sensing, 2021, 13(18): 3750.
    [41]
    DAUDT R C, LE SAUX B, BOULCH A. Fully convolutional Siamesenetworks for change detection[C]//2018 25th IEEE International Conference on Image Processing. Piscataway, NJ: IEEE, 2018: 4063-4067.
    [42]
    LIU Y, PANG C, ZHAN Z Q, et al. Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutionalnetwork model [J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5): 811-815.
    [43]
    FANG S, LI K Y, SHAO J Y, et al. SNUNet-CD: a densely connected Siamesenetwork for change detection of VHR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3056416.

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