深度学习的遥感变化检测综述：文献计量与分析

杨彬; 毛银; 陈晋; 刘建强; 陈杰; 闫凯

doi:10.11834/jrs.20222156

变化检测与深度学习 | 浏览量 : 0 下载量: 2089 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

深度学习的遥感变化检测综述：文献计量与分析
Review of remote sensing change detection in deep learning： Bibliometric and analysis
2023年27卷第9期页码：1988-2005
纸质出版日期： 2023-09-07 ，
DOI： 10.11834/jrs.20222156

扫描看全文

杨彬，毛银，陈晋，刘建强，陈杰，闫凯.2023.深度学习的遥感变化检测综述：文献计量与分析.遥感学报，27（9）： 1988-2005

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
杨彬，毛银，陈晋，刘建强，陈杰，闫凯.2023.深度学习的遥感变化检测综述：文献计量与分析.遥感学报，27（9）： 1988-2005 DOI： 10.11834/jrs.20222156.

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 DOI： 10.11834/jrs.20222156.

摘要

遥感变化检测可以获取地表变化信息，对于理解人与自然相互作用，推动可持续发展具有重要意义。随着遥感成像技术的提升和计算机科学的快速发展，高光谱、高时间、高空间分辨率的遥感影像已广泛应用，促进了深度学习的遥感变化检测发展以及多领域成功应用。与传统遥感变化检测不同，基于深度学习的变化检测提取遥感影像的深度差异特征，无需构建特征工程，检测精度和效率均有所提高。本文结合文献计量学全面分析本领域研究现状和热点，发现基于深度学习的变化检测在国内机构学者的主导下快速发展并取得了大量研究成果。这些成果大都基于高分辨率图像和CNN网络，并成功应用于土地利用/覆盖和建筑变化检测等。在此基础上，从像素、对象和场景3个粒度对基于深度学习的遥感变化检测方法分类介绍，阐述开展像素、对象和场景的特征提取以及后续网络分析过程，其中基于对象和场景的方法具有优势。最后，归纳总结目前面临的挑战及未来可能发展方向。由于遥感平台的发展和应用需求的增加，多模态异质变化检测是未来发展趋势。另外，深度学习的方法还需要克服非理想样本问题，关注多元变化信息获取，以及推进变化检测的广泛应用等。

Abstract

Remote sensing change detection can provide information on land surface change

which is important for studying man-nature interactions and facilitating sustainable development. With the advancement of remote sensing imaging technology and the rapid development of computer technology

extensive remote sensing images with various modes and spectral

spatial

and temporal resolutions have been collected

enabling the development of massive remote sensing change detection methods based on deep learning and their successful application in a wide range of fields.

Unlike previous reviews

this work examines remote sensing change detection based on deep learning from the perspectives of bibliometric analysis

research scale

and critical problem exploration to provide reference materials for future remote sensing change detection research. The definition and importance of remote sensing change detection as well as the motivation for this review are briefly presented in the introduction. The literature structure and research hotspot information of existing research

such as the number of publications

distribution of journals and institutions

main researchers

common data sources

network model

and application field information

are clarified in the second section

which is combined with bibliometric analysis. In the third section

focus is on deep learning-based remote sensing change detection algorithms

which are categorized and presented on three scales: pixel

object

and scene. How to extract pixels

objects

and scenes from remote sensing images as well as how to perform network analysis are also explained. In the fourth section

the limitations of deep learning-based remote sensing change detection are covered

and the most recent research are presented to address these issues as well as future development possibilities. Next

a segment dedicated to the finale.

The bibliometric analysis reveals deep learning-based change detection has progressed rapidly in the last three years

with fruitful research results and domestic institutional scholars dominating. High-resolution images and CNN are the most used data sources and network model

and extensive land use/coverage and building change detection are hot application fields. As for methods

different research scales respond to varied data features and network model structures. The object and scene technique have advantages

and they face similar issues

which are summarized below. First is the problem of detecting changes using multimodal remote sensing data. To address this

adversarial training

attention mechanisms

and feature deep fusion methods based on feature space transformation appear promising. Multimodal data fusion and other multimodal learning approaches are among the future’s emerging directions. Second

change detection under small sample and imbalanced sample settings is difficult. Semi-supervised schemes must be improved to address the problem of small sample size

and self-supervised methods are predicted to become a research hotspot. The oversampling technique and ensemble learning in deep learning models provide a new path for unbalanced samples. The third issue is obtaining diversified change information. Semantic change detection

which obtains extensive information on change types

and Transformer for time series change detection

which obtains long-term change information

are the future trends. Furthermore

deep learning-based change detection requires advances in gathering dynamic information such as time and seasonal pattern of change.

This work systematically compiles and reviews the research status and progress of deep learning-based remote sensing image change detection. Multimodal heterogeneous change detection

semantic change detection

and time series change detection are future prospects as application needs and data diversity grow. In the areas of resources

the environment

and disaster relief

practical uses of existing knowledge are few. Continuously extending the in-depth study of new technologies and methods is required as is promoting wide

in-depth remote sensing change detection research and application.

关键词

遥感变化检测深度学习文献计量方法分类挑战及发展综述

Keywords

remote sensingchange detectiondeep learningbibliometricmethods classificationchallenges and prospectsreview

references

Adarme M O, Feitosa R Q, Happ P N, De Almeida C A and Gomes A R. 2020. Evaluation of deep learning techniques for deforestation detection in the Brazilian amazon and Cerrado biomes from remote sensing imagery. Remote Sensing, 12(6): 910 [DOI: 10.3390/rs12060910http://dx.doi.org/10.3390/rs12060910]

Bai S J, Kolter J Z and Koltun V. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arxiv preprint arXiv: 1803.01271 [DOI: 10.48550/arXiv.1803.01271http://dx.doi.org/10.48550/arXiv.1803.01271]

Baytas I M, Xiao C, Zhang X, Wang F, Jain A K and Zhou J Y. 2017. Patient subtyping via time-aware LSTM networks//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax: ACM: 65-74 [DOI: 10.1145/3097983.3097997http://dx.doi.org/10.1145/3097983.3097997]

Benedek C and Sziranyi T. 2009. Change detection in optical aerial images by a multilayer conditional mixed Markov model. IEEE Transactions on Geoscience and Remote Sensing, 47(10): 3416-3430 [DOI: 10.1109/tgrs.2009.2022633http://dx.doi.org/10.1109/tgrs.2009.2022633]

Bueno I T, Acerbi Júnior F W, Silveira E M O, Mello J M, Carvalho L M T, Gomide L R, Withey K and Scolforo J R S. 2019. Object-based change detection in the Cerrado biome using Landsat time series. Remote Sensing, 11(5): 570 [DOI: 10.3390/rs11050570http://dx.doi.org/10.3390/rs11050570]

Cao G, Wang B S, Xavier H C, Yang D and Southworth J. 2017. A new difference image creation method based on deep neural networks for change detection in remote-sensing images. International Journal of Remote Sensing, 38(23): 7161-7175 [DOI: 10.1080/01431161.2017.1371861http://dx.doi.org/10.1080/01431161.2017.1371861]

Chen C, Ma H X, Yao G R, Lv N, Yang H, Li C and Wan S H. 2021a. Remote sensing image augmentation based on text description for waterside change detection. Remote Sensing, 13(10): 1894 [DOI: 10.3390/rs13101894http://dx.doi.org/10.3390/rs13101894]

Chen G, Hay G J, Carvalho L M T and Wulder M A. 2012. Object-based change detection. International Journal of Remote Sensing, 33(14): 4434-4457 [DOI: 10.1080/01431161.2011.648285http://dx.doi.org/10.1080/01431161.2011.648285]

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 [DOI: 10.1109/tgrs.2021.3095166http://dx.doi.org/10.1109/tgrs.2021.3095166]

Chen H and Shi Z W. 2020. A Spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sensing, 12(10): 1662 [DOI: 10.3390/rs12101662http://dx.doi.org/10.3390/rs12101662]

Chen H R X, Wu C, Du B, Zhang L P and Wang L. 2020a. Change detection in multisource VHR images via deep Siamese convolutional multiple-layers recurrent neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2848-2864 [DOI: 10.1109/tgrs.2019.2956756http://dx.doi.org/10.1109/tgrs.2019.2956756]

Chen J, Chen X H, Cui X H and Chen J. 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters, 8(2): 317-321 [DOI: 10.1109/lgrs.2010.2068537http://dx.doi.org/10.1109/lgrs.2010.2068537]

Chen J, Gong P, He C Y, Pu R L and Shi P J. 2003a. Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing, 69(4): 369-379 [DOI: 10.14358/pers.69.4.369http://dx.doi.org/10.14358/pers.69.4.369]

Chen J, Yuan Z Y, Peng J, Chen L, Huang H Z, Zhu J W, Liu Y and Li H F. 2021b. DASNet: dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 1194-1206 [DOI: 10.1109/jstars.2020.3037893http://dx.doi.org/10.1109/jstars.2020.3037893]

Chen K A, Lin W Y, Li J G, See J, Wang J and Zou J N. 2021c. AP-Loss for accurate one-stage object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11): 3782-3798 [DOI: 10.1109/tpami.2020.2991457http://dx.doi.org/10.1109/tpami.2020.2991457]

Chen L, Zhang D Z, Li P and Lv P. 2020b. Change detection of remote sensing images based on attention mechanism. Computational Intelligence and Neuroscience, 2020: 6430627 [DOI: 10.1155/2020/6430627http://dx.doi.org/10.1155/2020/6430627]

Chen X H, Yang D D, Chen J and Cao X. 2015. An improved automated land cover updating approach by integrating with downscaled NDVI time series data. Remote Sensing Letters, 6(1): 29-38 [DOI: 10.1080/2150704x.2014.998793http://dx.doi.org/10.1080/2150704x.2014.998793]

Chen Z, Duan J, Kang L and Qiu G P. 2022b. Class-imbalanced deep learning via a class-balanced ensemble. IEEE Transactions on Neural Networks and Learning Systems, 33(10): 5626-5640 [DOI: 10.1109/tnnls.2021.3071122http://dx.doi.org/10.1109/tnnls.2021.3071122]

Chen Z, Zhang Y F, Ouyang C, Zhang F and Ma J. 2018. Automated landslides detection for mountain cities using multi-temporal remote sensing imagery. Sensors, 18(3): 821 [DOI: 10.3390/s18030821http://dx.doi.org/10.3390/s18030821]

Chen Z J, Chen J, Shi P J and Tamura M. 2003b. An IHS-based change detection approach for assessment of urban expansion impact on arable land loss in China. International Journal of Remote Sensing, 24(6): 1353-1360 [DOI: 10.1080/0143116021000047910http://dx.doi.org/10.1080/0143116021000047910]

Cheng H Q, Wu H Y, Zheng J, Qi K L and Liu W X. 2021. A hierarchical self-attention augmented Laplacian pyramid expanding network for change detection in high-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 182: 52-66 [DOI: 10.1016/j.isprsjprs.2021.10.001http://dx.doi.org/10.1016/j.isprsjprs.2021.10.001]

Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E. 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9): 1565-1596 [DOI: 10. 1080/0143116031000101675http://dx.doi.org/10.1080/0143116031000101675]

Cui B, Zhang Y H, Yan L, Wei J J and Wu H A. 2019. An unsupervised SAR change detection method based on stochastic subspace ensemble learning. Remote Sensing, 11(11): 1314 [DOI: 10.3390/rs11111314http://dx.doi.org/10.3390/rs11111314]

Dablain D, Krawczyk B and Chawla N V. 2022. DeepSMOTE: fusing deep learning and SMOTE for imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, Early Access [DOI: 10.1109/tnnls.2021.3136503http://dx.doi.org/10.1109/tnnls.2021.3136503]

Dargan S, Kumar M, Ayyagari M R and Kumar G. 2020. A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4): 1071-1092 [DOI: 10.1007/s11831-019-09344-whttp://dx.doi.org/10.1007/s11831-019-09344-w]

Daudt R C, Le Saux B, Boulch A and Gousseau Y. 2018. Urban change detection for multispectral earth observation using convolutional neural networks//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 2115-2118 [DOI: 10.1109/IGARSS.2018.8518015http://dx.doi.org/10.1109/IGARSS.2018.8518015]

De Angeli K, Gao S, Danciu I, Durbin E B, Wu X C, Stroup A, Doherty J, Schwartz S, Wiggins C, Damesyn M, Coyle L, Penberthy L, Tourassi G D and Yoon H J. 2022. Class imbalance in out-of-distribution datasets: improving the robustness of the TextCNN for the classification of rare cancer types. Journal of Biomedical Informatics, 125: 103957 [DOI: 10.1016/j.jbi.2021.103957http://dx.doi.org/10.1016/j.jbi.2021.103957]

de Bem P P, de Carvalho Júnior O A, de Carvalho O L F, Gomes R A T and Guimarães R F. 2020. Performance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areas. Remote Sensing, 12(16): 2576 [DOI: 10.3390/rs12162576http://dx.doi.org/10.3390/rs12162576]

Dian Y Y, Fang S H and Yao C H. 2016. Change detection for high-resolution images using multilevel segment method. Journal of Remote Sensing, 20(1): 129-137

佃袁勇, 方圣辉, 姚崇怀. 2016. 多尺度分割的高分辨率遥感影像变化检测. 遥感学报, 20(1): 129-137 [DOI: 10.11834/jrs.20165074http://dx.doi.org/10.11834/jrs.20165074]

Dong S, Wang P and Abbas K. 2021. A survey on deep learning and its applications. Computer Science Review, 40: 100379 [DOI: 10.1016/j.cosrev.2021.100379http://dx.doi.org/10.1016/j.cosrev.2021.100379]

Du J, Zhou Y H, Liu P, Vong C M and Wang T F. 2023. Parameter-free loss for class-imbalanced deep learning in image classification. IEEE Transactions on Neural Networks and Learning Systems, 34(6): 3234-3240 [DOI: 10.1109/tnnls.2021.3110885http://dx.doi.org/10.1109/tnnls.2021.3110885]

Erturk A, Iordache M D and Plaza A. 2017. Sparse unmixing with dictionary pruning for hyperspectral change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1): 321-330 [DOI: 10.1109/jstars.2016.2606514http://dx.doi.org/10.1109/jstars.2016.2606514]

Gao F, Dong J Y, Li B and Xu Q Z. 2016. Automatic change detection in synthetic aperture radar images based on PCANet. IEEE Geoscience and Remote Sensing Letters, 13(12): 1792-1796 [DOI: 10.1109/lgrs.2016.2611001http://dx.doi.org/10.1109/lgrs.2016.2611001]

Gao F, Liu X P, Dong J Y, Zhong G Q and Jian M W. 2017. Change detection in SAR images based on deep Semi-NMF and SVD networks. Remote Sensing, 9(5): 435 [DOI: 10.3390/rs9050435http://dx.doi.org/10.3390/rs9050435]

Gao Y H, Gao F, Dong J Y and Li H C. 2021. SAR image change detection based on multiscale capsule network. IEEE Geoscience and Remote Sensing Letters, 18(3): 484-488 [DOI: 10.1109/lgrs.2020.2977838http://dx.doi.org/10.1109/lgrs.2020.2977838]

Gao Y H, Gao F, Dong J Y and Wang S K. 2019. Transferred deep learning for sea ice change detection from synthetic-aperture radar images. IEEE Geoscience and Remote Sensing Letters, 16(10): 1655-1659 [DOI: 10.1109/lgrs.2019.2906279http://dx.doi.org/10.1109/lgrs.2019.2906279]

Geng J, Ma X R, Zhou X J and Wang H Y. 2019. Saliency-guided deep neural networks for SAR image change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 7365-7377 [DOI: 10.1109/tgrs.2019.2913095http://dx.doi.org/10.1109/tgrs.2019.2913095]

Gong M G, Niu X D, Zhan T and Zhang M Y. 2019. A coupling translation network for change detection in heterogeneous images. International Journal of Remote Sensing, 40(9): 3647-3672 [DOI: 10.1080/01431161.2018.1547934http://dx.doi.org/10.1080/01431161.2018.1547934]

Gong M G, Niu X D, Zhang P Z and Li Z T. 2017a. Generative adversarial networks for change detection in multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 14(12): 2310-2314 [DOI: 10.1109/lgrs.2017.2762694http://dx.doi.org/10.1109/lgrs.2017.2762694]

Gong M G, Yang H L and Zhang P Z. 2017b. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 212-225 [DOI: 10.1016/j.isprsjprs.2017.05.001http://dx.doi.org/10.1016/j.isprsjprs.2017.05.001]

Gong M G, Zhan T, Zhang P Z and Miao Q G. 2017c. Superpixel-based difference representation learning for change detection in multispectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2658-2673 [DOI: 10.1109/tgrs.2017.2650198http://dx.doi.org/10.1109/tgrs.2017.2650198]

Han Y, Javed A, Jung S and Liu S C. 2020. Object-based change detection of very high resolution images by fusing pixel-based change detection results using weighted Dempster-Shafer theory. Remote Sensing, 12(6): 983 [DOI: 10.3390/rs12060983http://dx.doi.org/10.3390/rs12060983]

Healey S P, Cohen W B, Yang Z Q, Brewer C K, Brooks E B, Gorelick N, Hernandez A J, Huang C Q, Joseph Hughes M, Kennedy R E, Loveland T R, Moisen G G, Schroeder T A, Stehman S V, Vogelmann J E, Woodcock C E, Yang L M and Zhu Z. 2018. Mapping forest change using stacked generalization: an ensemble approach. Remote Sensing of Environment, 204: 717-728 [DOI: 10.1016/j.rse.2017.09.029http://dx.doi.org/10.1016/j.rse.2017.09.029]

Healey S P, Cohen W B, Yang Z Q and Krankina O N. 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3): 301-310 [DOI: 10.1016/j.rse.2005.05.009http://dx.doi.org/10.1016/j.rse.2005.05.009]

Hong D F, Gao L R, Yokoya N, Yao J, Chanussot J, Du Q and Zhang B. 2021. More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 59(5): 4340-4354 [DOI: 10.1109/tgrs.2020.3016820http://dx.doi.org/10.1109/tgrs.2020.3016820]

Hossain M S, Betts J M and Paplinski A P. 2021. Dual Focal Loss to address class imbalance in semantic segmentation. Neurocomputing, 462: 69-87 [DOI: 10.1016/j.neucom.2021.07.055http://dx.doi.org/10.1016/j.neucom.2021.07.055]

Hou B, Liu Q J, Wang H and Wang Y H. 2020. From W-Net to CDGAN: bitemporal change detection via deep learning techniques. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1790-1802 [DOI: 10.1109/tgrs.2019.2948659http://dx.doi.org/10.1109/tgrs.2019.2948659]

Hu M Q, Wu C, Zhang L P and Du B. 2021. Hyperspectral Anomaly change detection based on autoencoder. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 3750-3762 [DOI: 10.1109/jstars.2021.3066508http://dx.doi.org/10.1109/jstars.2021.3066508]

Huang F H, Yu Y and Feng T H. 2019. Hyperspectral remote sensing image change detection based on tensor and deep learning. Journal of Visual Communication and Image Representation, 58: 233-244 [DOI: 10.1016/j.jvcir.2018.11.004http://dx.doi.org/10.1016/j.jvcir.2018.11.004]

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 [DOI: 10.1016/j.isprsjprs.2013.03.006http://dx.doi.org/10.1016/j.isprsjprs.2013.03.006]

Ji S P, Shen Y Y, Lu M and Zhang Y J. 2019a. Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sensing, 11(11): 1343 [DOI: 10.3390/rs11111343http://dx.doi.org/10.3390/rs11111343]

Ji S P, Tian S Q and Zhang C. 2020. Urban land cover classification and change detection using fully atrous convolutional neural network. Geomatics and Information Science of Wuhan University, 45(2): 233-241

季顺平, 田思琪, 张驰. 2020. 利用全空洞卷积神经元网络进行城市土地覆盖分类与变化检测. 武汉大学学报(信息科学版), 45(2): 233-241 [DOI: 10.13203/j.whugis20180481http://dx.doi.org/10.13203/j.whugis20180481]

Ji S P, Wei S Q and Lu M. 2019b. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 574-586 [DOI: 10.1109/tgrs.2018.2858817http://dx.doi.org/10.1109/tgrs.2018.2858817]

Jiang F L, Gong M G, Zhan T and Fan X L. 2020. A semisupervised GAN-based multiple change detection framework in multi-spectral images. IEEE Geoscience and Remote Sensing Letters, 17(7): 1223-1227 [DOI: 10.1109/lgrs.2019.2941318http://dx.doi.org/10.1109/lgrs.2019.2941318]

Jiang X, Li G, Zhang X P and He Y. 2022. A semisupervised Siamese network for efficient change detection in heterogeneous remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 4700718 [DOI: 10.1109/tgrs.2021.3061686http://dx.doi.org/10.1109/tgrs.2021.3061686]

Jing L L and Tian Y L. 2021. Self-supervised visual feature learning with deep neural networks: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11): 4037-4058 [DOI: 10.1109/tpami.2020.2992393http://dx.doi.org/10.1109/tpami.2020.2992393]

Jing R, Liu S, Gong Z N, Wang Z H, Guan H L, Gautam A and Zhao W J. 2020. Object-based change detection for VHR remote sensing images based on a Trisiamese-LSTM. International Journal of Remote Sensing, 41(16): 6209-6231 [DOI: 10.1080/01431161.2020.1734253http://dx.doi.org/10.1080/01431161.2020.1734253]

Kalinicheva E, Ienco D, Sublime J and Trocan M. 2020. Unsupervised change detection analysis in satellite image time series using deep learning combined with graph-based approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 1450-1466 [DOI: 10.1109/jstars.2020.2982631http://dx.doi.org/10.1109/jstars.2020.2982631]

Ke Q T and Zhang P. 2021. MCCRNet: a multi-level change contextual refinement network for remote sensing image change detection. ISPRS International Journal of Geo-Information, 10(9): 591 [DOI: 10.3390/ijgi10090591http://dx.doi.org/10.3390/ijgi10090591]

Kennedy R E, Townsend P A, Gross J E, Cohen W B, Bolstad P, Wang Y Q and Adams P. 2009. Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment, 113(7): 1382-1396 [DOI: 10.1016/j.rse.2008.07.018http://dx.doi.org/10.1016/j.rse.2008.07.018]

Kerner H R, Wagstaff K L, Bue B D, Gray P C, Bell J F and Ben Amor H. 2019. Toward generalized change detection on planetary surfaces with convolutional autoencoders and transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10): 3900-3918 [DOI: 10.1109/jstars.2019.2936771http://dx.doi.org/10.1109/jstars.2019.2936771]

Khan S H, He X M, Porikli F and Bennamoun M. 2017. Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5407-5423 [DOI: 10.1109/tgrs.2017.2707528http://dx.doi.org/10.1109/tgrs.2017.2707528]

Khelifi L and Mignotte M. 2020. Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis. IEEE Access, 8: 126385-126400 [DOI: 10.1109/access.2020.3008036http://dx.doi.org/10.1109/access.2020.3008036]

Kong Y L, Huang Q Q, Wang C Y, Chen J B, Chen J S and He D X. 2018. Long short-term memory neural networks for online disturbance detection in satellite image time series. Remote Sensing, 10(3): 452 [DOI: 10.3390/rs10030452http://dx.doi.org/10.3390/rs10030452]

Lei T, Zhang Y X, Lv Z Y, Li S Y, Liu S G and Nandi A K. 2019a. Landslide inventory mapping from bitemporal images using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 16(6): 982-986 [DOI: 10.1109/lgrs.2018.2889307http://dx.doi.org/10.1109/lgrs.2018.2889307]

Lei Y, Liu X D, Shi J, Lei C and Wang J. 2019b. Multiscale Superpixel segmentation with deep features for change detection. IEEE Access, 7: 36600-36616 [DOI: 10.1109/access.2019.2902613http://dx.doi.org/10.1109/access.2019.2902613]

Li H, Gong M G, Zhang M Y and Wu Y. 2021. Spatially self-paced convolutional networks for change detection in heterogeneous images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 4966-4979 [DOI: 10.1109/jstars.2021.3078437http://dx.doi.org/10.1109/jstars.2021.3078437]

Li M K, Li M, Zhang P, Wu Y, Song W Y and An L. 2019a. SAR image change detection using PCANet guided by saliency detection. IEEE Geoscience and Remote Sensing Letters, 16(3): 402-406 [DOI: 10.1109/lgrs.2018.2876616http://dx.doi.org/10.1109/lgrs.2018.2876616]

Li S, Wang Y F, Chen P P, Xu X L, Cheng C Q and Chen B. 2017. Spatiotemporal fuzzy clustering strategy for urban expansion monitoring based on time series of pixel-level optical and SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5): 1769-1779 [DOI: 10.1109/jstars.2017.2657607http://dx.doi.org/10.1109/jstars.2017.2657607]

Li X, Zhang G, Cui H, Hou S S, Wang S Y, Li X, Chen Y J, Li Z J and Zhang L. 2022. MCANet: a joint semantic segmentation framework of optical and SAR images for land use classification. International Journal of Applied Earth Observation and Geoinformation, 106: 102638 [DOI: 10.1016/j.jag.2021.102638http://dx.doi.org/10.1016/j.jag.2021.102638]

Li Y Y, Peng C, Chen Y Q, Jiao L C, Zhou L H and Shang R H. 2019b. A deep learning method for change detection in synthetic aperture radar images. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 5751-5763 [DOI: 10.1109/tgrs.2019.2901945http://dx.doi.org/10.1109/tgrs.2019.2901945]

Lin Y, Li S T, Fang L Y and Ghamisi P. 2020. Multispectral change detection with bilinear convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 17(10): 1757-1761 [DOI: 10.1109/lgrs.2019.2953754http://dx.doi.org/10.1109/lgrs.2019.2953754]

Liu H C and Zhang L. 2020. Adaptive threshold change detection based on type feature for remote sensing image. Journal of Remote Sensing (Chinese), 24(6): 728-738

刘红超, 张磊. 2020. 面向类型特征的自适应阈值遥感影像变化检测. 遥感学报, 24(6): 728-738 [DOI: 10.11834/jrs.20208328http://dx.doi.org/10.11834/jrs.20208328]

Liu R C, Jiang D W, Zhang L L and Zhang Z T. 2020. Deep depthwise separable convolutional network for change detection in optical aerial images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 1109-1118 [DOI: 10.1109/jstars.2020.2974276http://dx.doi.org/10.1109/jstars.2020.2974276]

Liu T, Yang L X and Lunga D. 2021. Change detection using deep learning approach with object-based image analysis. Remote Sensing of Environment, 256: 112308 [DOI: 10.1016/j.rse.2021.112308http://dx.doi.org/10.1016/j.rse.2021.112308]

Liu Z G, Zhang Z W, Pan Q and Ning L B. 2022. Unsupervised change detection from heterogeneous data based on image translation. IEEE Transactions on Geoscience and Remote Sensing, 60: 4403413 [DOI: 10.1109/tgrs.2021.3097717http://dx.doi.org/10.1109/tgrs.2021.3097717]

Lu M, Chen J, Tang H J, Rao Y H, Yang P and Wu W B. 2016. Land cover change detection by integrating object-based data blending model of Landsat and MODIS. Remote Sensing of Environment, 184: 374-386 [DOI: 10.1016/j.rse.2016.07.028http://dx.doi.org/10.1016/j.rse.2016.07.028]

Lu N, Chen C, Shi W B, Zhang J W and Ma J F. 2020. Weakly supervised change detection based on edge mapping and SDAE network in high-resolution remote sensing images. Remote Sensing, 12(23): 3907 [DOI: 10.3390/rs12233907http://dx.doi.org/10.3390/rs12233907]

Luo X, Li X X, Wu Y X, Hou W M, Wang M, Jin Y W and Xu W B. 2021. Research on change detection method of high-resolution remote sensing images based on subpixel convolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 1447-1457 [DOI: 10.1109/jstars.2020.3044060http://dx.doi.org/10.1109/jstars.2020.3044060]

Luppino L T, Kampffmeyer M, Bianchi F M, Moser G, Serpico S B, Jenssen R and Anfinsen S N. 2022. Deep image translation with an affinity-based change prior for unsupervised multimodal change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 4700422 [DOI: 10.1109/tgrs.2021.3056196http://dx.doi.org/10.1109/tgrs.2021.3056196]

Lv N, Chen C, Qiu T and Sangaiah A K. 2018. Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in SAR images. IEEE Transactions on Industrial Informatics, 14(12): 5530-5538 [DOI: 10.1109/tii.2018.2873492http://dx.doi.org/10.1109/tii.2018.2873492]

Lyu H B, Lu H and Mou L C. 2016. Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sensing, 8(6): 506 [DOI: 10.3390/rs8060506http://dx.doi.org/10.3390/rs8060506]

Melis G, Kočiský T and Blunsom P. 2020. Mogrifier LSTM. arXiv: 1909.01792 [DOI: 10.48550/arXiv.1909.01792http://dx.doi.org/10.48550/arXiv.1909.01792]

Mesquita D B, dos Santos R F, Macharet D G, Campos M F M and Nascimento E R. 2020. Fully convolutional Siamese autoencoder for change detection in UAV aerial images. IEEE Geoscience and Remote Sensing Letters, 17(8): 1455-1459 [DOI: 10.1109/lgrs.2019.2945906http://dx.doi.org/10.1109/lgrs.2019.2945906]

Mou L C, Bruzzone L and Zhu X X. 2019. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 924-935 [DOI: 10.1109/tgrs.2018.2863224http://dx.doi.org/10.1109/tgrs.2018.2863224]

Niu X D, Gong M G, Zhan T and Yang Y L. 2019. A conditional adversarial network for change detection in heterogeneous images. IEEE Geoscience and Remote Sensing Letters, 16(1): 45-49 [DOI: 10.1109/lgrs.2018.2868704http://dx.doi.org/10.1109/lgrs.2018.2868704]

Papadomanolaki M, Vakalopoulou M and Karantzalos K. 2021. A deep multitask learning framework coupling semantic segmentation and fully convolutional LSTM networks for urban change detection. IEEE Transactions on Geoscience and Remote Sensing, 59(9): 7651-7668 [DOI: 10.1109/tgrs.2021.3055584http://dx.doi.org/10.1109/tgrs.2021.3055584]

Peng D F, Bruzzone L, Zhang Y J, Guan H Y and He P F. 2021. 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 [DOI: 10.1016/j.jag.2021.102465http://dx.doi.org/10.1016/j.jag.2021.102465]

Peng D F, Zhang Y J and Guan H Y. 2019. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11): 1382 [DOI: 10.3390/rs11111382http://dx.doi.org/10.3390/rs11111382]

Planinsic P and Gleich D. 2018. Temporal Change detection in SAR images using log cumulants and stacked autoencoder. IEEE Geoscience and Remote Sensing Letters, 15(2): 297-301 [DOI: 10.1109/lgrs.2017.2786344http://dx.doi.org/10.1109/lgrs.2017.2786344]

Qian J H, Xia M, Zhang Y H, Liu J and Xu Y Q. 2020. TCDNet: trilateral change detection network for Google earth image. Remote Sensing, 12(17): 2669 [DOI: 10.3390/rs12172669http://dx.doi.org/10.3390/rs12172669]

Radke R J, Andra S, Al-Kofahi O and Roysam B. 2005. Image change detection algorithms: a systematic survey. IEEE Transactions on Image Processing, 14(3): 294-307 [DOI: 10.1109/tip.2004.838698http://dx.doi.org/10.1109/tip.2004.838698]

Saha S, Bovolo F and Bruzzone L. 2019. Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 3677-3693 [DOI: 10.1109/tgrs.2018.2886643http://dx.doi.org/10.1109/tgrs.2018.2886643]

Saha S, Mou L C, Zhu X X, Bovolo F and Bruzzone L. 2021a. Semisupervised change detection using graph convolutional network. IEEE Geoscience and Remote Sensing Letters, 18(4): 607-611 [DOI: 10.1109/lgrs.2020.2985340http://dx.doi.org/10.1109/lgrs.2020.2985340]

Saha S, Solano-Correa Y T, Bovolo F and Bruzzone L. 2021b. Unsupervised deep transfer learning-based change detection for HR multispectral images. IEEE Geoscience and Remote Sensing Letters, 18(5): 856-860 [DOI: 10.1109/lgrs.2020.2990284http://dx.doi.org/10.1109/lgrs.2020.2990284]

Seydi S T and Hasanlou M. 2017. A new land-cover match-based change detection for hyperspectral imagery. European Journal of Remote Sensing, 50(1): 517-533 [DOI: 10.1080/22797254.2017.1367963http://dx.doi.org/10.1080/22797254.2017.1367963]

Shafique A, Cao G, Khan Z, Asad M and Aslam M. 2022. Deep learning-based change detection in remote sensing images: a review. Remote Sensing, 14(4): 871 [DOI: 10.3390/rs14040871http://dx.doi.org/10.3390/rs14040871]

Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651 [DOI: 10.1109/tpami.2016.2572683http://dx.doi.org/10.1109/tpami.2016.2572683]

Shi J, Zhang X, Liu X D and Lei Y. 2021a. Deep change feature analysis network for observing changes of land use or natural environment. Sustainable Cities and Society, 68: 102760 [DOI: 10.1016/j.scs.2021.102760http://dx.doi.org/10.1016/j.scs.2021.102760]

Shi N, Chen K M, Zhou G Y and Sun X. 2020a. A feature space constraint-based method for change detection in heterogeneous images. Remote Sensing, 12(18): 3057 [DOI: 10.3390/rs12183057http://dx.doi.org/10.3390/rs12183057]

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 [DOI: 10.1109/tgrs.2021.3085870http://dx.doi.org/10.1109/tgrs.2021.3085870]

Shi W Z, Zhang M, Ke H F, Fang X, Zhan Z and Chen S X. 2021b. Landslide recognition by deep convolutional neural network and change detection. IEEE Transactions on Geoscience and Remote Sensing, 59(6): 4654-4672 [DOI: 10.1109/tgrs.2020.3015826http://dx.doi.org/10.1109/tgrs.2020.3015826]

Shi W Z, Zhang M, Zhang R, Chen S X and Zhan Z. 2020b. Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sensing, 12(10): 1688 [DOI: 10.3390/rs12101688http://dx.doi.org/10.3390/rs12101688]

Shu Y J, Li W, Yang M L, Cheng P and Han S C. 2021. Patch-based change detection method for SAR images with label updating strategy. Remote Sensing, 13(7): 1236 [DOI: 10.3390/rs13071236http://dx.doi.org/10.3390/rs13071236]

Singh A. 1989. Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6): 989-1003 [DOI: 10.1080/01431168908903939http://dx.doi.org/10.1080/01431168908903939]

Song A, Choi J, Han Y and Kim Y. 2018. Change detection in hyperspectral images using recurrent 3D fully convolutional networks. Remote Sensing, 10(11): 1827 [DOI: 10.3390/rs10111827http://dx.doi.org/10.3390/rs10111827]

Song A and Kim Y. 2020. Transfer change rules from recurrent fully convolutional networks for hyperspectral unmanned aerial vehicle images without ground truth data. Remote Sensing, 12(7): 1099 [DOI: 10.3390/rs12071099http://dx.doi.org/10.3390/rs12071099]

Song A, Kim Y and Han Y. 2020. Uncertainty analysis for object-based change detection in very high-resolution satellite images using deep learning network. Remote Sensing, 12(15): 2345 [DOI: 10.3390/rs12152345http://dx.doi.org/10.3390/rs12152345]

Song K Q and Jiang J. 2021. AGCDetNet: an attention-guided network for building change detection in high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 4816-4831 [DOI: 10.1109/jstars.2021.3077545http://dx.doi.org/10.1109/jstars.2021.3077545]

Stephenson O L, Kohne T, Zhan E, Cahill B E, Yun S H, Ross Z E and Simons M. 2022. Deep learning-based damage mapping with InSAR coherence time series. IEEE Transactions on Geoscience and Remote Sensing, 60: 5207917 [DOI: 10.1109/tgrs.2021.3084209http://dx.doi.org/10.1109/tgrs.2021.3084209]

Sublime J and Kalinicheva E. 2019. Automatic post-disaster damage mapping using deep-learning techniques for change detection: case study of the Tohoku Tsunami. Remote Sensing, 11(9): 1123 [DOI: 10.3390/rs11091123http://dx.doi.org/10.3390/rs11091123]

Sui H G, Feng W Q, Li W Z, Sun K M and Xu C. 2018. Review of change detection methods for multi-temporal remote sensing imagery. Geomatics and Information Science of Wuhan University, 43(12): 1885-1898

眭海刚, 冯文卿, 李文卓, 孙开敏, 徐川. 2018. 多时相遥感影像变化检测方法综述. 武汉大学学报(信息科学版), 43(12): 1885-1898 [DOI: 10.13203/j.whugis20180251http://dx.doi.org/10.13203/j.whugis20180251]

Tang X, Zhang H Y, Mou L C, Liu F, Zhang X R, Zhu X X and Jiao L C. 2022. An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning. IEEE Transactions on Geoscience and Remote Sensing, 60: 5609715 [DOI: 10.1109/tgrs.2021.3106381http://dx.doi.org/10.1109/tgrs.2021.3106381]

Timilsina S, Aryal J and Kirkpatrick J B. 2020. Mapping urban tree cover changes using object-based convolution neural network (OB-CNN). Remote Sensing, 12(18): 3017 [DOI: 10.3390/rs12183017http://dx.doi.org/10.3390/rs12183017]

Tong G F, Li Y, Ding W L and Yue X Y. 2015. Review of remote sensing image change detection. Journal of Image and Graphics, 20(12): 1561-1571

佟国峰, 李勇, 丁伟利, 岳晓阳. 2015. 遥感影像变化检测算法综述. 中国图象图形学报, 20(12): 1561-1571 [DOI: 10.11834/jig.20151201http://dx.doi.org/10.11834/jig.20151201]

Verbesselt J, Hyndman R, Newnham G and Culvenor D. 2010a. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106-115 [DOI: 10.1016/j.rse.2009.08.014http://dx.doi.org/10.1016/j.rse.2009.08.014]

Verbesselt J, Hyndman R, Zeileis A and Culvenor D. 2010b. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114(12): 2970-2980 [DOI: 10.1016/j.rse.2010.08.003http://dx.doi.org/10.1016/j.rse.2010.08.003]

Voulodimos A, Doulamis N, Doulamis A and Protopapadakis E. 2018. Deep learning for computer vision: a brief review. Computational Intelligence and Neuroscience, 2018: 7068349 [DOI: 10.1155/2018/7068349http://dx.doi.org/10.1155/2018/7068349]

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 [DOI: 10.1016/j.jag.2021.102348http://dx.doi.org/10.1016/j.jag.2021.102348]

Wang M C, Zhang H M, Sun W W, Li S, Wang F Y and Yang G D. 2020a. A coarse-to-fine deep learning based land use change detection method for high-resolution remote sensing images. Remote Sensing, 12(12): 1933 [DOI: 10.3390/rs12121933http://dx.doi.org/10.3390/rs12121933]

Wang M Y, Tan K, Jia X P, Wang X and Chen Y. 2020b. A deep Siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images. Remote Sensing, 12(2): 205 [DOI: 10.3390/rs12020205http://dx.doi.org/10.3390/rs12020205]

Wang R F, Zhang J, Chen J W, Jiao L C and Wang M. 2019. Imbalanced learning-based automatic SAR images change detection by morphologically supervised PCA-Net. IEEE Geoscience and Remote Sensing Letters, 16(4): 554-558 [DOI: 10.1109/lgrs.2018.2878420http://dx.doi.org/10.1109/lgrs.2018.2878420]

Wang Y H, Gao L R, Chen Z C and Zhang B. 2020. Deep learning and superpixel-based method for high-resolution remote sensing image change detection. Journal of Image and Graphics, 25(6): 1271-1282

王艳恒, 高连如, 陈正超, 张兵. 2020. 结合深度学习和超像元的高分遥感影像变化检测. 中国图象图形学报, 25(6): 1271-1282 [DOI: 10.11834/jig.190319http://dx.doi.org/10.11834/jig.190319]

Wei D S, Hou D Y, Zhou X G and Chen J. 2021. Change detection using a texture feature space outlier index from mono-temporal remote sensing images and vector data. Remote Sensing, 13(19): 3857 [DOI: 10.3390/rs13193857http://dx.doi.org/10.3390/rs13193857]

Wiratama W, Lee J, Park S E and Sim D. 2018. Dual-dense convolution network for change detection of high-resolution panchromatic imagery. Applied Sciences, 8(10): 1785 [DOI: 10.3390/app8101785http://dx.doi.org/10.3390/app8101785]

Wu C, Chen H R X, Du B and Zhang L P. 2022a. Unsupervised change detection in multitemporal VHR images based on deep kernel PCA convolutional mapping network. IEEE Transactions on Cybernetics, 52(11): 12084-12098 [DOI: 10.1109/tcyb.2021.3086884http://dx.doi.org/10.1109/tcyb.2021.3086884]

Wu C, Du B, Cui X H and Zhang L P. 2017. A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion. Remote Sensing of Environment, 199: 241-255 [DOI: 10.1016/j.rse.2017.07.009http://dx.doi.org/10.1016/j.rse.2017.07.009]

Wu Y, Li J H, Yuan Y Z, Qin A K, Miao Q G and Gong M G. 2022b. Commonality autoencoder: learning common features for change detection from heterogeneous images. IEEE Transactions on Neural Networks and Learning Systems, 33(9): 4257-4270 [DOI: 10.1109/tnnls.2021.3056238http://dx.doi.org/10.1109/tnnls.2021.3056238]

Xiang S, Wang M, Jiang X F, Xie G Q, Zhang Z Q and Tang P. 2021. Dual-task semantic change detection for remote sensing images using the generative change field module. Remote Sensing, 13(16): 3336 [DOI: 10.3390/rs13163336http://dx.doi.org/10.3390/rs13163336]

Xu L, Jing W P, Song H B and Chen G S. 2019. High-resolution remote sensing image change detection combined with pixel-level and object-level. IEEE Access, 7: 78909-78918 [DOI: 10.1109/access.2019.2922839http://dx.doi.org/10.1109/access.2019.2922839]

Xu X C, Li B J, Liu X P, Li X and Shi Q. 2021. Mapping annual global land cover changes at a 30 m resolution from 2000 to 2015. National Remote Sensing Bulletin, 25(9): 1896-1916

许晓聪, 李冰洁, 刘小平, 黎夏, 石茜. 2021. 全球2000年-2015年30 m分辨率逐年土地覆盖制图. 遥感学报, 25(9): 1896-1916 [DOI: 10.11834/jrs.20211261http://dx.doi.org/10.11834/jrs.20211261]

Yang M J, Jiao L C, Liu F, Hou B and Yang S Y. 2019. Transferred deep learning-based change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 6960-6973 [DOI: 10.1109/tgrs.2019.2909781http://dx.doi.org/10.1109/tgrs.2019.2909781]

Yang M J, Jiao L C, Liu F, Hou B, Yang S Y and Jian M. 2022. DPFL-Nets: deep pyramid feature learning networks for multiscale change detection. IEEE Transactions on Neural Networks and Learning Systems, 33(11): 6402-6416 [DOI: 10.1109/tnnls.2021.3079627http://dx.doi.org/10.1109/tnnls.2021.3079627]

Yin R Y, He G J, Wang G Z, Long T F, Li H F, Zhou D J and Gong C J. 2022. Automatic framework of mapping impervious surface growth with long-term Landsat imagery based on temporal deep learning model. IEEE Geoscience and Remote Sensing Letters, 19: 2502605 [DOI: 10.1109/lgrs.2021.3135869http://dx.doi.org/10.1109/lgrs.2021.3135869]

Yu W J, Zhou W Q, Jing C B, Zhang Y J and Qian Y G. 2021. Quantifying highly dynamic urban landscapes: integrating object-based image analysis with Landsat time series data. Landscape Ecology, 36(7): 1845-1861 [DOI: 10.1007/s10980-020-01104-7http://dx.doi.org/10.1007/s10980-020-01104-7]

Yuan Y, Lin L, Liu Q S, Hang R L and Zhou Z G. 2022. SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classification. International Journal of Applied Earth Observation and Geoinformation, 106: 102651 [DOI: 10.1016/j.jag.2021.102651http://dx.doi.org/10.1016/j.jag.2021.102651]

Zerrouki Y, Harrou F, Zerrouki N, Dairi A and Sun Y. 2021. Desertification detection using an improved Variational Autoencoder-based approach through ETM-Landsat satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 202-213 [DOI: 10.1109/jstars.2020.3042760http://dx.doi.org/10.1109/jstars.2020.3042760]

Zhan T, Gong M G, Jiang X M and Zhang M Y. 2020. Unsupervised scale-driven change detection with deep spatial-spectral features for VHR images. IEEE Transactions on Geoscience and Remote Sensing, 58(8): 5653-5665 [DOI: 10.1109/tgrs.2020.2968098http://dx.doi.org/10.1109/tgrs.2020.2968098]

Zhan T, Gong M G, Liu J and Zhang P Z. 2018. Iterative feature mapping network for detecting multiple changes in multi-source remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 38-51 [DOI: 10.1016/j.isprsjprs.2018.09.002http://dx.doi.org/10.1016/j.isprsjprs.2018.09.002]

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 [DOI: 10.1109/tgrs.2022.3160007http://dx.doi.org/10.1109/tgrs.2022.3160007]

Zhang C, Wei S Q, Ji S P and Lu M. 2019a. Detecting large-scale urban land cover changes from very high resolution remote sensing images using CNN-based classification. ISPRS International Journal of Geo-Information, 8(4): 189 [DOI: 10.3390/ijgi8040189http://dx.doi.org/10.3390/ijgi8040189]

Zhang C X, Yue P, Tapete D, Jiang L C, Shangguan B Y, Huang L and Liu G C. 2020a. 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 [DOI: 10.1016/j.isprsjprs.2020.06.003http://dx.doi.org/10.1016/j.isprsjprs.2020.06.003]

Zhang H, Gong M G, Zhang P Z, Su L Z and Shi J. 2016a. Feature-level change detection using deep representation and feature change analysis for multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 13(11): 1666-1670 [DOI: 10.1109/lgrs.2016.2601930http://dx.doi.org/10.1109/lgrs.2016.2601930]

Zhang H Y, Lin M H, Yang G Y and Zhang L P. 2023. ESCNet: an end-to-end superpixel-enhanced change detection network for very-high-resolution remote sensing images. IEEE Transactions on Neural Networks and Learning Systems, 34(1): 28-42 [DOI: 10. 1109/tnnls.2021.3089332http://dx.doi.org/10.1109/tnnls.2021.3089332]

Zhang L, Hu X Y, Zhang M, Shu Z and Zhou H. 2021b. Object-level change detection with a dual correlation attention-guided detector. ISPRS Journal of Photogrammetry and Remote Sensing, 177: 147-160 [DOI: 10.1016/j.isprsjprs.2021.05.002http://dx.doi.org/10.1016/j.isprsjprs.2021.05.002]

Zhang L F, Wang S, Liu H L, Lin Y K, Wang J N, Zhu M, Gao L R and Tong Q X. 2021. From spectrum to spectrotemporal: research on time series change detection of remote sensing. Geomatics and Information Science of Wuhan University, 46(4): 451-468

张立福, 王飒, 刘华亮, 林昱坤, 王晋年, 朱曼, 高了然, 童庆禧. 2021. 从光谱到时谱——遥感时间序列变化检测研究进展. 武汉大学学报(信息科学版), 46(4): 451-468 [DOI: 10.13203/j.whugis20200666http://dx.doi.org/10.13203/j.whugis20200666]

Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459

张良培, 武辰. 2017. 多时相遥感影像变化检测的现状与展望. 测绘学报, 46(10): 1447-1459 [DOI: 10.11947/j.AGCS.2017.20170340http://dx.doi.org/10.11947/j.AGCS.2017.20170340]

Zhang M and Shi W Z. 2020. A feature difference convolutional neural network-based change detection method. IEEE Transactions on Geoscience and Remote Sensing, 58(10): 7232-7246 [DOI: 10.1109/tgrs.2020.2981051http://dx.doi.org/10.1109/tgrs.2020.2981051]

Zhang M Y, Xu G L, Chen K M, Yan M L and Sun X. 2019b. Triplet-based semantic relation learning for aerial remote sensing image change detection. IEEE Geoscience and Remote Sensing Letters, 16(2): 266-270 [DOI: 10.1109/lgrs.2018.2869608http://dx.doi.org/10.1109/lgrs.2018.2869608]

Zhang P Z, Ban Y F and Nascetti A. 2021a. Learning U-Net without forgetting for near real-time wildfire monitoring by the fusion of SAR and optical time series. Remote Sensing of Environment, 261: 112467 [DOI: 10.1016/j.rse.2021.112467http://dx.doi.org/10.1016/j.rse.2021.112467]

Zhang P Z, Gong M G, Su L Z, Liu J and Li Z Z. 2016b. Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 116: 24-41 [DOI: 10.1016/j.isprsjprs.2016.02.013http://dx.doi.org/10.1016/j.isprsjprs.2016.02.013]

Zhang T and Huang X. 2018. Monitoring of urban impervious surfaces using time series of high-resolution remote sensing images in rapidly urbanized areas: a case study of Shenzhen. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8): 2692-2708 [DOI: 10.1109/jstars.2018.2804440http://dx.doi.org/10.1109/jstars.2018.2804440]

Zhang X Z, Liu G, Zhang C, Atkinson P M, Tan X H, Jian X, Zhou X C and Li Y M. 2020b. Two-phase object-based deep learning for multi-temporal SAR image change detection. Remote Sensing, 12(3): 548 [DOI: 10.3390/rs12030548http://dx.doi.org/10.3390/rs12030548]

Zhao W, Wang Z R, Gong M G and Liu J. 2017. Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network. IEEE Transactions on Geoscience and Remote Sensing, 55(12): 7066-7080 [DOI: 10.1109/tgrs.2017.2739800http://dx.doi.org/10.1109/tgrs.2017.2739800]

Zhao Z M, Meng Y, Yue A Z, Huang Q Q, Kong Y L, Yuan Y, Liu X Y, Lin L and Zhang M M. 2016. Review of remotely sensed time series data for change detection. Journal of Remote Sensing, 20(5): 1110-1125

赵忠明, 孟瑜, 岳安志, 黄青青, 孔赟珑, 袁媛, 刘晓奕, 林蕾, 张蒙蒙. 2016. 遥感时间序列影像变化检测研究进展. 遥感学报, 20(5): 1110-1125 [DOI: 10.11834/jrs.20166170http://dx.doi.org/10.11834/jrs.20166170]

Zhong X, Feng W, Zhang Y L, Quan Y H, Huang W J and Xing M D. 2022. Diversity features collaboration technology for monitoring forests before and after hurricanes by remote sensing. National Remote Sensing Bulletin, 26(9): 1838-1848

钟娴, 冯伟, 张亚丽, 全英汇, 黄文江, 邢孟道. 2022. 基于多样性特征协同技术的飓风前后森林破坏遥感监测. 遥感学报, 26(9): 1838-1848 [DOI: 10.11834/jrs.20210230http://dx.doi.org/10.11834/jrs.20210230]

Zhou Q, Rover J, Brown J, Worstell B, Howard D, Wu Z T, Gallant A L, Rundquist B and Burke M. 2019. Monitoring landscape dynamics in Central U.S. grasslands with harmonized landsat-8 and Sentinel-2 time series data. Remote Sensing, 11(3): 328 [DOI: 10.3390/rs11030328http://dx.doi.org/10.3390/rs11030328]

Zitzlsberger G, Podhorányi M, Svatoň V, Lazecký M and Martinovič J. 2021. Neural network-based urban change monitoring with deep-temporal multispectral and SAR remote sensing data. Remote Sensing, 13(15): 3000 [DOI: 10.3390/rs13153000http://dx.doi.org/10.3390/rs13153000]

文章被引用时，请邮件提醒。

提交

光学信号Token引导的异源遥感变化检测网络

人工智能时代的遥感变化检测技术：继承、发展与挑战

面向高分辨率遥感影像变化检测的混合空间金字塔池化网络