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 |
[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.
|