Обзор обнаружения изменений в дистанционном зондировании на основе глубокого обучения: типичные алгоритмы и тенденции развития

  • role: First author第一作者
  • Affiliation:

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

  • Email:gcheng@nwpu.edu.cn
  • Introduction:E-mail gcheng@nwpu.edu.cn
CHENG Gong,  
  • role: Corresponding author通信作者
  • Affiliation:

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

  • Email:guangxingwang@mail.nwpu.edu.cn
  • Introduction: E-mail guangxingwang@mail.nwpu.edu.cn
WANG Guangxing*,  
  • Affiliation:

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

HAN Junwei

реферат

Обнаружение изменений на двухвременных снимках дистанционного зондирования является важным направлением интеллектуального анализа спутниковых изображений и их применения, целью которого является выявление изменений типов земной поверхности или характеристик объектов на одной и той же контролируемой территории за определённый временной интервал. Под воздействием больших данных дистанционного зондирования (особенно с распространением и накоплением изображений высокого разрешения) и глубинного обучения, технологии обнаружения изменений быстро развиваются и совершенствуются. В данной работе систематизирован и проанализирован обзор типичных алгоритмов и последних достижений в области обнаружения изменений на высокоразрешающих двухвременных снимках, включая задачи бинарного обнаружения изменений, семантического обнаружения изменений, оценки повреждений зданий, описания изменений; а также обозначены основные направления исследований, существующие проблемы и вызовы в текущем развитии данной области, с целью предоставления справочной информации для будущих исследований.

ключеви́че слова́

изображения высокого разрешения; двухвременные снимки; глубинное обучение; обнаружение изменений; обзор литературы

References

  1. 1.
    Bai B F, Fu W, Lu T and Li S T. 2022. Edge-guided recurrent convolutional neural network for multitemporal remote sensing image building change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5610613
  2. 2.
    Bandara W G C and Patel V M. 2022. A transformer-based siamese network for change detection//Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur: IEEE: 207-210
  3. 3.
    Cao Y X, Huang X and Weng Q H. 2023. A multi-scale weakly supervised learning method with adaptive online noise correction for high-resolution change detection of built-up areas. Remote Sensing of Environment, 297: 113779
  4. 4.
    Chang H, Wang P J, Diao W H, Xu G L and Sun X. 2024. A triple-branch hybrid attention network with bitemporal feature joint refinement for remote-sensing image semantic change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 5613816
  5. 5.
    Chang S Z and Ghamisi P. 2023. Changes to captions: an attentive network for remote sensing change captioning. IEEE Transactions on Image Processing, 32: 6047-6060
  6. 6.
    Chen H, Qi Z P and Shi Z W. 2022a. Remote sensing image change detection with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60: 5607514
  7. 7.
    Chen H R X, Song J, Han C X, Xia J S and Yokoya N. 2024. ChangeMamba: remote sensing change detection with spatiotemporal state space model. IEEE Transactions on Geoscience and Remote Sensing, 62: 4409720
  8. 8.
    Chen Z L, Zhou Y, Wang B, Xu X W, He N, Jin S and Jin S R. 2022b. EGDE-Net: a building change detection method for high-resolution remote sensing imagery based on edge guidance and differential enhancement. ISPRS Journal of Photogrammetry and Remote Sensing, 191: 203-222
  9. 9.
    Cheng G, Wang G X and Han J W. 2022. ISNet: towards improving separability for remote sensing image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5623811
  10. 10.
    Cui F Z and Jiang J. 2023. MTSCD-Net: a network based on multi-task learning for semantic change detection of bitemporal remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 118: 103294
  11. 11.
    Da Y F, Ji Z Y and Zhou Y S. 2022. Building damage assessment based on siamese hierarchical transformer framework. Mathematics, 10(11): 1898
  12. 12.
    Dai J F, Qi H Z, Xiong Y W, Li Y, Zhang G D, Hu H and Wei Y C. 2017. Deformable convolutional networks//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice: IEEE: 764-773
  13. 13.
    Deng L W and Wang Y. 2022. Post-disaster building damage assessment based on improved U-Net. Scientific Reports, 12(1): 15862
  14. 14.
    Ding L, Guo H T, Liu S C, Mou L C, Zhang J and Bruzzone L. 2022. Bi-temporal semantic reasoning for the semantic change detection in HR remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5620014
  15. 15.
    Ding L, Zhang J, Guo H T, Zhang K, Liu B and Bruzzone L. 2024a. Joint spatio-temporal modeling for semantic change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 62: 5610814
  16. 16.
    Ding L, Zhu K, Peng D F, Tang H, Yang K W and Bruzzone L. 2024b. Adapting segment anything model for change detection in VHR remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 62: 5611711
  17. 17.
    Dong S J, Wang L B, Du B and Meng X L. 2024. ChangeCLIP: remote sensing change detection with multimodal vision-language representation learning. ISPRS Journal of Photogrammetry and Remote Sensing, 208: 53-69
  18. 18.
    Fang L Y, Jiang Y Q, Yu H F, Zhang Y Y and Yue J. 2024. Point label meets remote sensing change detection: a consistency-aligned regional growth network. IEEE Transactions on Geoscience and Remote Sensing, 62: 5603911
  19. 19.
    Fang S, Li K Y, Shao J Y and Li Z. 2022. SNUNet-CD: a densely connected siamese network for change detection of vhr images. IEEE Geoscience and Remote Sensing Letters, 19: 8007805
  20. 20.
    Feng Y C, Jiang J W, Xu H H and Zheng J W. 2023. Change detection on remote sensing images using dual-branch multilevel intertemporal network. IEEE Transactions on Geoscience and Remote Sensing, 61: 4401015
  21. 21.
    Feng Y C, Xu H H, Jiang J W, Liu H and Zheng J W. 2022. ICIF-Net: intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 4410213
  22. 22.
    Fu K, Lu W X, Liu X Y, Deng C B, Yu H F and Sun X. 2024. A comprehensive survey and assumption of remote sensing foundation modal. National Remote Sensing Bulletin, 28(7): 1667-1680
  23. 23.
    Gu A and Dao T. 2023. Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv: 2312.00752
  24. 24.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778
  25. 25.
    Hong D F, Zhang B, Li X Y, Li Y X, Li C Y, Yao J, Yokoya N, Li H, Ghamisi P, Jia X P, Plaza A, Gamba P, Benediktsson J A and Chanussot J. 2024. SpectralGPT: spectral remote sensing foundation model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8): 5227-5244
  26. 26.
    Hoxha G, Chouaf S, Melgani F and Smara Y. 2022. Change captioning: a new paradigm for multitemporal remote sensing image analysis. IEEE Transactions on Geoscience and Remote Sensing, 60: 5627414
  27. 27.
    Hu J, Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 7132-7141
  28. 28.
    Huang R, Wang R F, Guo Q, Wei J D, Zhang Y X, Fan W and Liu Y. 2023. Background-mixed augmentation for weakly supervised change detection//Proceedings of the 37th AAAI Conference on Artificial Intelligence. Washington: AAAI: 7919-7927
  29. 29.
    Hussain M, Chen D M, Cheng A, Wei H and Stanley D. 2013. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 91-106
  30. 30.
    Jiang B, Wang Z T, Wang X X, Zhang Z Y, Chen L, Wang X and Luo B. 2023. VcT: visual change transformer for remote sensing image change detection. IEEE Transactions on Geoscience and Remote Sensing, 61: 2005214
  31. 31.
    Jiang H W, Peng M, Zhong Y J, Xie H F, Hao Z M, Lin J M, Ma X L and Hu X Y. 2022. A survey on deep learning-based change detection from high-resolution remote sensing images. Remote Sensing, 14(7): 1552
  32. 32.
    Kirillov A, Mintun E, Ravi N, Mao H Z, Rolland C, Gustafson L, Xiao T T, Whitehead S, Berg A C, Lo W Y, Dollár P and Girshick R. 2023. Segment anything//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE: 3992-4003
  33. 33.
    LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444
  34. 34.
    Lei J, Gu Y J, Xie W Y, Li Y S and Du Q. 2022. Boundary extraction constrained siamese network for remote sensing image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5621613
  35. 35.
    Li Q Y, Zhong R F, Du X and Du Y. 2022a. TransUNetCD: a hybrid transformer network for change detection in optical remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5622519
  36. 36.
    Li X H, Ai W H, Feng R T and Luo S J. 2023. Survey of remote sensing image registration based on deep learning. National Remote Sensing Bulletin, 27(2): 267-284
  37. 37.
    Li Z, Wang X X, Fang S, Zhao J L, Yang S Q and Li W. 2024a. A decoder-focused multitask network for semantic change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 5609115
  38. 38.
    Li Z L, Tang C, Liu X W, Li C D, Li X J and Zhang W. 2024b. MS-Former: memory-supported transformer for weakly supervised change detection with patch-level annotations. IEEE Transactions on Geoscience and Remote Sensing, 62: 5625213
  39. 39.
    Li Z L, Tang C, Liu X W, Zhang W, Dou J, Wang L Z and Zomaya A Y. 2023. Lightweight remote sensing change detection with progressive feature aggregation and supervised attention. IEEE Transactions on Geoscience and Remote Sensing, 61: 5602812
  40. 40.
    Li Z L, Tang C, Wang L Z and Zomaya A Y. 2022b. Remote sensing change detection via temporal feature interaction and guided refinement. IEEE Transactions on Geoscience and Remote Sensing, 60: 5628711
  41. 41.
    Li Z Z, Zhang S H and Ma J Y. 2024c. U-Match: exploring hierarchy-aware local context for two-view correspondence learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12): 10960-10977
  42. 42.
    Liu C Y, Chen K Y, Chen B W, Zhang H T, Zou Z X and Shi Z W. 2024a. RSCaMa: remote sensing image change captioning with state space model. IEEE Geoscience and Remote Sensing Letters, 21: 6010405
  43. 43.
    Liu C Y, Chen K Y, Zhang H T, Qi Z P, Zou Z X and Shi Z W. 2024b. Change-agent: toward interactive comprehensive remote sensing change interpretation and analysis. IEEE Transactions on Geoscience and Remote Sensing, 62: 5635616
  44. 44.
    Liu C Y, Zhao R, Chen H, Zou Z X and Shi Z W. 2022. Remote sensing image change captioning with dual-branch transformers: a new method and a large scale dataset. IEEE Transactions on Geoscience and Remote Sensing, 60: 5633520
  45. 45.
    Liu C Y, Zhao R, Chen J Q, Qi Z P, Zou Z X and Shi Z W. 2023. A decoupling paradigm with prompt learning for remote sensing image change captioning. IEEE Transactions on Geoscience and Remote Sensing, 61: 5622018
  46. 46.
    Lv Z Y, Huang H T, Li X H, Zhao M H, Benediktsson J A, Sun W W and Falco N. 2022. Land cover change detection with heterogeneous remote sensing images: review, progress, and perspective. Proceedings of the IEEE, 110(12): 1976-1991
  47. 47.
    Ma H J, Liu Y L, Ren Y H and Yu J X. 2019. Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3. Remote Sensing, 12(1): 44
  48. 48.
    Ma J J, Duan J Y, Tang X, Zhang X R and Jiao L C. 2024. EATDer: edge-assisted adaptive transformer detector for remote sensing change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 5602015
  49. 49.
    Mei J and Cheng M M. 2022. Damage assessment with global differences and local attention. Scientia Sinica Informationis, 52(11): 2058-2074
  50. 50.
    Mei L Y, Ye Z Y, Xu C, Wang H Z, Wang Y, Lei C, Yang W and Li Y S. 2024. SCD-SAM: adapting segment anything model for semantic change detection in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 62: 5626713
  51. 51.
    Noman M, Fiaz M, Cholakkal H, Khan S and Khan F S. 2024. ELGC-Net: efficient local–global context aggregation for remote sensing change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 4701611
  52. 52.
    Paranjape J N, de Melo C and Patel V M. 2024. A mamba-based siamese network for remote sensing change detection. arXiv preprint arXiv: 2407.06839
  53. 53.
    Park J M, Kim U H, Lee S H and Kim J H. 2022. Dual task learning by leveraging both dense correspondence and mis-correspondence for robust change detection with imperfect matches//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE: 13739-13749
  54. 54.
    Peng D F, Bruzzone L, Zhang Y J, Guan H Y, Ding H Y and Huang X. 2021a. SemiCDNet: a semisupervised convolutional neural network for change detection in high resolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 5891-5906
  55. 55.
    Peng D F, Bruzzone L, Zhang Y J, Guan H Y and He P F. 2021b. SCDNET: a novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 103: 102465
  56. 56.
    Peng X L, Zhong R F, Li Z and Li Q Y. 2021c. Optical remote sensing image change detection based on attention mechanism and image difference. IEEE Transactions on Geoscience and Remote Sensing, 59(9): 7296-7307
  57. 57.
    Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G and Sutskever I. 2021. Learning transferable visual models from natural language supervision//Proceedings of the 38th International Conference on Machine Learning. [s.l.]: PMLR: 8748-8763
  58. 58.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241
  59. 59.
    Seydi S T, Hasanlou M, Chanussot J and Ghamisi P. 2023. BDD-Net+: a building damage detection framework based on modified coat-net. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 4232-4247
  60. 60.
    Shi Q, Liu M X, Li S C, Liu X P, Wang F and Zhang L P. 2022. A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5604816
  61. 61.
    Sun X, Wang P J, Lu W X, Zhu Z C, Lu X N, He Q B, Li J X, Rong X E, Yang Z J, Chang H, He Q L, Yang G, Wang R P, Lu J W and Fu K. 2023. RingMo: a remote sensing foundation model with masked image modeling. IEEE Transactions on Geoscience and Remote Sensing, 61: 5612822
  62. 62.
    Tian S Q, Zhong Y F, Zheng Z, Ma A L, Tan X C and Zhang L P. 2022. Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: from benchmark datasets to urban application. ISPRS Journal of Photogrammetry and Remote Sensing, 193: 164-186
  63. 63.
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł and Polosukhin I. 2017. Attention is all you need//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc.: 6000-6010
  64. 64.
    Wang C, Shi A Y and Chen J Q. 2018. High Resolution Remote Sensing Image Change Detection. Beijing: Posts and Telecom Press
  65. 65.
    Wang C X, Zhao D P, Qi X H, Liu Z R and Shi Z W. 2023a. A hierarchical decoder architecture for multilevel fine-grained disaster detection. IEEE Transactions on Geoscience and Remote Sensing, 61: 5607114
  66. 66.
    Wang D C, Chen X N, Jiang M Y, Du S H, Xu B J and Wang J D. 2021. ADS-Net: an attention-based deeply supervised network for remote sensing image change detection. International Journal of Applied Earth Observation and Geoinformation, 101: 102348
  67. 67.
    Wang G X, Cheng G, Zhou P C and Han J W. 2024a. Cross-level attentive feature aggregation for change detection. IEEE Transactions on Circuits and Systems for Video Technology, 34(7): 6051-6062
  68. 68.
    Wang J H, Liu F, Wang H, Liu X, Jiao L C, Yang H, Li L L and Chen P H. 2023b. SDCDNet: a semi-dual change detection network framework with super-weak label for remote sensing image. IEEE Transactions on Geoscience and Remote Sensing, 61: 5612714
  69. 69.
    Wang J X, Li T, Chen S B, Tang J, Luo B and Wilson R C. 2022. Reliable contrastive learning for semi-supervised change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 4416413
  70. 70.
    Wang L K, Zhang M, Gao X and Shi W Z. 2024b. Advances and challenges in deep learning-based change detection for remote sensing images: a review through various learning paradigms. Remote Sensing, 16(5): 804
  71. 71.
    Wang L K, Zhang M and Shi W Z. 2023c. CS-WSCDNet: class activation mapping and segment anything model-based framework for weakly supervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 61: 5624812
  72. 72.
    Wang Q, Jing W, Chi K C and Yuan Y. 2024c. Cross-difference semantic consistency network for semantic change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 4406312
  73. 73.
    Wei X S, Xu Y Y, Zhang C L, Xia G S and Peng Y X. 2023. CAT: a coarse-to-fine attention tree for semantic change detection. Visual Intelligence, 1(1): 3
  74. 74.
    Woo S, Park J, Lee J Y and Kweo I S. 2018. CBAM: convolutional block attention module//15th European Conference on Computer Vision. Munich: Springer: 3-19
  75. 75.
    Wu C, Du B and Zhang L P. 2023. Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8): 9774-9788
  76. 76.
    Wu C Y, Zhang F, Xia J S, Xu Y C, Li G Q, Xie J B, Du Z H and Liu R Y. 2021. Building damage detection using U-Net with attention mechanism from pre- and post-disaster remote sensing datasets. Remote Sensing, 13(5): 905
  77. 77.
    Yan T Y, Wan Z F, Zhang P P, Cheng G and Lu H C. 2023. Transy-Net: learning fully transformer networks for change detection of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61: 4410012
  78. 78.
    Yang B, Mao Y, Chen J, Liu J Q, Chen J and Yan K. 2023. Review of remote sensing change detection in deep learning: Bibliometric and analysis. National Remote Sensing Bulletin, 27(9): 1988-2005
  79. 79.
    Yang C, Li Z C, Jiao H Z, Gao Z and Zhang L F. 2024. Enhancing perception of key changes in remote sensing image change captioning. arXiv preprint arXiv: 2409.12612
  80. 80.
    Yang K P, Xia G S, Liu Z C, Du B, Yang W, Pelillo M and Zhang L P. 2022. Asymmetric siamese networks for semantic change detection in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5609818
  81. 81.
    Yao Y Q, Cheng G, Xie X X and Han J W. 2021. Optical remote sensing image object detection based on multi-resolution feature fusion. National Remote Sensing Bulletin, 25(5): 1124-1137
  82. 82.
    You Z H, Chen S B, Wang J X and Luo B. 2024. Robust feature aggregation network for lightweight and effective remote sensing image change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 215: 31-43
  83. 83.
    Yuan X, Cheng G, Li G, Dai W, Yin W X, Feng Y C, Yao X W, Huang Z L, Sun X and Han J W. 2023. Progress in small object detection for remote sensing images. Journal of Image and Graphics, 28(6): 1662-1684
  84. 84.
    Zhang C, Wang L J, Cheng S L and Li Y M. 2022. SwinSUNet: pure transformer network for remote sensing image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5224713
  85. 85.
    Zhang C X, Yue P, Tapete D, Jiang L C, Shangguan B Y, Huang L and Liu G C. 2020. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 183-200
  86. 86.
    Zhang H T, Chen H, Zhou C Y, Chen K Y, Liu C Y, Zou Z X and Shi Z W. 2024a. BiFA: remote sensing image change detection with bitemporal feature alignment. IEEE Transactions on Geoscience and Remote Sensing, 62: 5614317
  87. 87.
    Zhang H T, Chen K Y, Liu C Y, Chen H, Zou Z X and Shi Z W. 2024b. CDMamba: remote sensing image change detection with mamba. arXiv preprint arXiv: 2406.04207
  88. 88.
    Zhang K, Zhao X, Zhang F, Ding L, Sun J D and Bruzzone L. 2023a. Relation changes matter: cross-temporal difference transformer for change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61: 5611615
  89. 89.
    Zhang S H and Ma J Y. 2024. ConvMatch: rethinking network design for two-view correspondence learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(5): 2920-2935
  90. 90.
    Zhang X T, Huang X and Li J Y. 2023b. Joint self-training and rebalanced consistency learning for semi-supervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 61: 5406613
  91. 91.
    Zhang X T, Huang X and Li J Y. 2023c. Semisupervised change detection with feature-prediction alignment. IEEE Transactions on Geoscience and Remote Sensing, 61: 5401016
  92. 92.
    Zhang X W, Yang Y Z, Ran L Y, Chen L, Wang K W, Yu L, Wang P and Zhang Y N. 2024c. Remote sensing image semantic change detection boosted by semi-supervised contrastive learning of semantic segmentation. IEEE Transactions on Geoscience and Remote Sensing, 62: 5624113
  93. 93.
    Zhao S J, Chen H, Zhang X L, Xiao P F, Bai L and Ouyang W L. 2024. RS-Mamba for large remote sensing image dense prediction. IEEE Transactions on Geoscience and Remote Sensing, 62: 5633314
  94. 94.
    Zheng Z, Wan Y, Zhang Y J, Xiang S Z, Peng D F and Zhang B. 2021a. CLNet: cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 175: 247-267
  95. 95.
    Zheng Z, Zhong Y F, Tian S Q, Ma A L and Zhang L P. 2022. ChangeMask: deep multi-task encoder-transformer-decoder architecture for semantic change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 183: 228-239
  96. 96.
    Zheng Z, Zhong Y F, Wang J J, Ma A L and Zhang L P. 2021b. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters. Remote Sensing of Environment, 265: 112636
  97. 97.
    Zhu Y, Lv K K, Yu Y and Xu W J. 2023. Edge-guided parallel network for VHR remote sensing image change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 7791-7803
  98. 98.
    Zhu Y S, Li L, Chen K Y, Liu C Y, Zhou F G and Shi Z W. 2024. Semantic-CC: boosting remote sensing image change captioning via foundational knowledge and semantic guidance. EEE Transactions on Geoscience and Remote Sensing, 62: 5648916

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website