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随着人工智能技术与遥感大数据的紧密结合，深度学习技术在多时相遥感影像变化检测中展现出卓越的性能，推动了传统方法向智能化方向的转变，以更加自动化、精细化和智能化的方式解决多领域的地表时空变化检测问题。旨在综述基于深度学习的遥感影像变化检测方法，探讨智能化技术在变化检测方法中的应用潜力。通过介绍深度学习变化检测的通用框架，分析基于像素、面向对象和面向场景变化检测方法的研究进展，讨论数据质量、模型设计和实用性方面的挑战。研究结果表明，深度学习技术显著提高了变化检测的精度和鲁棒性，但需解决多源异构数据处理、模型泛化能力提升和实际应用效率等问题。未来遥感变化检测技术的发展将聚焦于多源数据融合、超分辨率技术和语义变化检测等关键技术，以实现遥感变化检测技术的智能化和精细化发展。
Abstract：With the seamless integration of artificial intelligence technology and remote sensing big data, deep learning techniques have demonstrated exceptional performance in image changing detection of multi-temporal remote sensing, propelling traditional methods towards an intelligent direction. This shift enables a more automated, refined, and intelligent approach to address surface spatiotemporal changing detection issues across various domains. Deep learning-based remote sensing image changing detection methods are reviewed and the potential applications of intelligent technologies in changing detection methodologies are explored. General framework of deep learning change detection is introduced. Then the research progress of pixel-based, object-oriented, and scene-oriented changing detection methods are analyzed, as well as challenges in data quality, model design, and practicality are discussed. Results indicate that deep learning technology has significantly improved the accuracy and robustness of changing detection, but issues such as multi-source heterogeneous data processing, enhancement of model generalization capabilities, and practical application efficiency need to be addressed. Future development of remote sensing changing detection technologies will focus on key technologies such as multi-source data fusion, super-resolution techniques, and semantic change detection, to achieve intelligent and refined development of remote sensing changing detection technology.
[1] 杨彬，毛银，陈晋，等.深度学习的遥感变化检测综述：文献计量与分析[J].遥感学报，2023,27(9):1988-2005.
[2] 葛楚婷.融合多特征的高分辨率遥感影像变化检测[D].南京：南京信息工程大学，2023.
[3] 张敏.基于深度学习的遥感影像变化检测方法研究[D].武汉：武汉大学，2020.
[4] 柳思聪，都科丞，郑永杰，等.人工智能时代的遥感变化检测技术：继承、发展与挑战[J].遥感学报，2023,27(9):1975-1987.
[5] 耿婵.基于深度学习的多尺度变化检测研究与应用[J].测绘与空间地理信息，2022,45(7):97-99.
[6] LIU T,YANG L X,LUNGA D.Change Detection Using Deep Learning Approach with Object-based Image Analysis[J].Remote Sensing of Environment,2021,256:112308.
[7] 胡凡.基于特征学习的高分辨率遥感图像场景分类研究[D].武汉：武汉大学，2017.
[8] ZHANG M,SHI W Z.A Feature Difference Convolutional Neural Network-based Change Detection Method[J].IEEE Transaction on Geoscience and Remote Sensing,2020,58(10):7232-7246.
[9] GUO H N,SHI Q,MARINONI A,et al.Deep Building Footprint Update Network a Semi-supervised Method for Updating Existing Building Footprint from Bi-temporal Remote Sensing Images[J].Remote Sensing of Environment,2021,264:112589.
[10] ZHENG Z,ZHONG Y F,WANG J J,et al.Building Damage Assessment for Rapid Disaster Response with a Deep Object-based Semantic Change Detection Framework from Natural Disasters to Man-made Disasters [J].Remote Sensing of Environment,2021,265:112636.
[11] LV N,CHEN C,QIU T,et al.Deep Learning and Super Pixel Feature Extraction Based on Contractive Autoenco-der for Change Detection in SAR Images[J].IEEE Transactions on Industrial Informatics,2018,14(12):5530-5538.
[12] 王光辉.融合时空谱信息的遥感影像深度学习变化检测方法研究[D].北京：中国矿业大学，2023.
[13] TIAN S Q,ZHONG Y F,ZHENG Z,et al.Large-scale Deep Learning Based Binary and Semantic Change Detection in Ultra High Resolution Remote Sensing Imagery:From Benchmark Datasets to Urban Application[J].ISPRS Journal of Photogrammetry and Remote Sensing,2022,193:164-186.
[14] PENG D F,ZHANG Y J,GUAN H Y.End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++[J].Remote Sensing,2019,11(11):1382.
[15] 马永军，张艺，王广来，等.改进UNet++的遥感影像森林变化检测方法[J].森林与环境学报，2024,44(3):317-327.
[16] 唐朋宇，刘超，孙健飞.基于改进的UNet++的遥感影像建筑物变化检测[J].九江学院学报(自然科学版),2024,39(1):41-45.
[17] MOU L C,ZHU X X.A Recurrent Convolutional Neural Network for Land Cover Change Detection in Multispectral Images[C]// GARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.Valencia:IEEE,2018:4363-4366.
[18] 冯炜明，张新长，孙颖，等.融合Transformer结构的高分辨率遥感影像变化检测网络[J].测绘通报，2022(8):36-40.
[19] WEN Y D,ZHANG K P,LI Z F,et al.A Discriminative Feature Learning Approach for Deep Face Recognition[C]//Proceedings of the European Conference on Computer Vision(ECCV).Amsterdam:Springer,2016:499-515.
[20] ZHAN Y,FU K,YAN M L,et al.Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images[J].IEEE Geoscience and Remote Sensing Letters,2017,14(10):1845-1849.
[21] MUELLER J W,THYAGARAJAN A.Siamese Recurrent Architectures for Learning Sentence Similarity[EB/OL].[2024-05-10].https://people.csail.mit.edu/jonasmueller/info/SentenceSimilarity(AAAI%202016).pdf.
[22] WANG Q,ZHONG X D,CHEN G Z,et al.Change Detection Based on Faster R-CNN for High-resolution Remote Sensing Images[J].Remote Sensing Letters,2018,9(10):923-932.
[23] ZHANG P Z,GONG M G,SU L Z,et al.Change Detection Based on Deep Feature Representation and Mapping Transformation for Multi-spatial-resolution Remote Sen-sing Images[J].ISPRS Journal of Photogrammetry and Remote Sensing,2016,16:24-41.
[24] 李斌.融合时空特征的多任务学习变化检测方法和应用研究[D].北京：中国矿业大学，2023.
[25] 马惠，刘波，杜世宏.多任务学习孪生网络的遥感影像多类变化检测[J].自然资源遥感，2024,36(1):77-85.
[26] 杨芷娟.基于孪生网络的遥感影像变化检测的研究[D].重庆：重庆工商大学，2023.
[27] 刘宣广，李蒙蒙，汪小钦，等.基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测[J].遥感学报，2024,28(2):437-454.
[28] 王晓雯，戴晨光，张振超，等.孪生高分辨率卷积神经网络遥感影像变化检测方法[J].测绘与空间地理信息，2023,46(5):70-73.
[29] GONG M G,NIU X D,ZHAO T,et al.A Coupling Translation Network for Change Detection in Heterogeneous Images[J].International Journal of Remote Sensing,2019,40(9):3647-3672.
[30] SUN K,ZHAO Y,JIANG B R,et al.High-resolution Representations for Labeling Pixels and Regions[EB/OL].(2019-04-09)[2024-05-11].https://arxiv.org/abs/1904.04514.
[31] ZHANG C X,YUE P,DEODATO T,et al.A Deeply Supervised Image Fusion Network for Change Detection in High Resolution Bi-temporal Remote Sensing Images[J].ISPRS Journal of Photogrammetry and Remote Sensing,2020,166:183-200.
[32] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature Pyramid Networks for Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:936-944.
[33] JI S P,WEI S Q,LU M.Fully Convolutional Networks 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.
[34] REN C J,WANG X Y,GAO J,et al.Unsupervised Change Detection in Satellite Images with Generative Adversarial Network[J].IEEE Transactions on Geoscience and Remote Sensing,2020,59(12):10047-10061.
[35] 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.
[36] CHEN P H,GUO L,ZHANG X R,et al.Attention-guided Siamese Fusion Network for Change Detection of Remote Sensing Images[J].Remote Sensing,2021,13(22):4597.
[37] JIANG Y,HU L,ZHANG Y M,et al.WRICNet:A Weighted Rich-scale Inception Coder Network for Remote Sensing Image Change Detection[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-13.
[38] ZHANG C,SARGENT I,PAN X,et al.Joint Deep Learning for Land Cover and Land Use Classification[J].Remote Sensing of Environment,2019,221:173-187.
[39] 邵攀，石卫超，秦道龙，等.集成CNN和Transformer的通道交互多层级融合变化检测[J].测绘科学，2024,49(5):110-121.
[40] 史文洁，戴晨光，赵莹，等.堆叠降噪自编码器的影像特征融合变化检测[J].测绘科学技术学报，2021,38(4):391-397.
[41] LU H B,LU H,MOU L C.Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection[J].Remote Sensing,2016,8(6):506.
[42] LI Y Y,PENG C,CHEN Y Q.A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(8):5751-5763.
[43] ZHU B,GAO H M,WANG X.Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology[C]// 2018 IEEE 3rd International Conference on Image,Vision and Computing (ICIVC).Chongqing:IEEE,2018:55-59.
[44] XU L,JING W P,SONG H B,et al.High-resolution Remote Sensing Image Change Detection Combined with Pixel-level and Object-level[J].IEEE Access,2019,7:78909-78918.
[45] DAUDT R C,LE SAUX B,BOULCH A.Fully Convolutional Siamese Networks for Change Detection[C]// 2018 25th IEEE International Conference on Image Processing.Athens:IEEE,2018:4063-4067.
[46] 廖娟.多尺度多维特征的高分辨率遥感影像变化检测方法研究[D].西安：长安大学，2022.
[47] 刘统飞.基于高分辨率遥感影像空-谱特征联合的地表覆盖变化检测方法研究[D].西安：西安理工大学，2020.
[48] 徐俊峰，蔡晓娜，张保明，等.利用慢特征分析进行多尺度融合的高分辨率影像变化检测[J].测绘科学技术学报，2019,36(1):62-68.
[49] 王嫣然.融合像元级与对象级的高分辨率遥感影像建筑物变化检测[D].武汉：武汉大学，2019.
[50] 雷鸣，田卫新，任东，等.基于对象分类的遥感影像森林变化检测方法[J].森林与环境学报，2019,39(6):641-646.
[51] YU W J,ZHOU W Q,JING C B,et al.Quantifying Highly Dynamic Urban Landscapes Integrating Object-based Image Analysis with Landsat Time Series Data[J].Landscape Ecology,2020,36:1845-1861.
[52] WANG X X,XIAO X M,ZOU Z H,et al.Tracking Annual Changes of Coastal Tidal Flats in China During 1986-2016 Through Analyses of Landsat Images with Google Earth Engine[J].Remote Sensing of Environment,2020,238:110987.
[53] TAYLOR R B,TULBURE M G,BROICH M.Evaluating Static and Dynamic Landscape Connectivity Modelling Using a 25-year Remote Sensing Time Series[J].Landscape Ecology,2018,33(4):625-640.
[54] 杜培军，柳思聪.融合多特征的遥感影像变化检测[J].遥感学报，2012,16(4):663-677.
[55] ACHANTA R,SHAJI A,SMITH K,et al.SLIC Super-pixels Compared to State-of-the-art Superpixel Methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2281.
[56] ZHANG S X,MA Z H,ZHANG G,et al.Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift[J].Symmetry,2020,12(3):427.
[57] NAZIR U,WANG H,TAJ M.Survey of Image Based Graph Neural Networks[EB/OL].(2021-06-11)[2024-05-12].https://arxiv.org/abs/2106.06307.
[58] HAPP P N,FERREIRA R S,BENTES C,et al.Multire-solution Segmentation:A Parallel Approach for High Resolution Image Segmentation in Multicore Architectures[EB/OL].[2024-06-11].https://www.isprs.org/proceedings/XXXVIII/4-C7/pdf/Happ_143.pdf.
[59] ZHENG Z,WAN Y,ZHANG Y J,et al.CLNet:Cross-layer Convolutional Neural Network for Change Detection in Optical Remote Sensing Imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2021,175:247-267.
[60] WANG W J,ZHAO Z M,ZHU H Q.Object-oriented Change Detection Method Based on Multi-scale and Multi-feature Fusion:Joint Urban Remote Sensing Event[C]//2009 Joint Urban Remote Sensing Event.Shanghai:IEEE,2019:1-5.
[61] ZHENG Z F,CAO J N,LV Z Y,et al.Spatial-spectral Feature Fusion Coupled with Multi-scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images[J].Remote Sensing,2019,11(16):1903.
[62] 冯文卿，眭海刚，涂继辉，等.联合像素级和对象级分析的遥感影像变化检测[J].测绘学报，2017,46(9):1147-1155.
[63] ZHAO S J,CHEN H,ZHANG X L,et al.RS-Mamba for Large Remote Sensing Image Dense Prediction[EB/OL].(2024-04-03)[2024-05-03].https://arxiv.org/abs/2404.02668.
[64] 王勇.基于主题模型和深度学习的高分辨率遥感影像场景变化检测研究[D].武汉：武汉大学，2019.
[65] 郭庆乐.多时相遥感图像变化检测及趋势分析[D].哈尔滨：哈尔滨工业大学，2018.
[66] HE P F,ZHAO X W,SHI Y L,et al.Unsupervised Change Detection from Remotely Sensed Images Based on Multi-scale Visual Saliency Coarse-to-Fine Fusion[J].Remote Sensing,2021,13(4):630.
[67] 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.
[68] MOU L C,SCHMITT M,WANG Y Y,et al.A CNN for the Identification of Corresponding Patches in SAR and Optical Imagery of Urban Scenes[C]//2017 Joint Urban Remote Sensing Event (JURSE).Dubai:IEEE,2017:1-4.
[69] MAGGIORI E,TARABALKA Y,CHARPIAT G,et al.Convolutional Neural Networks for Large-scale Remote Sensing Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(2):645-657.
[70] SHELHAMER E,LONG J,DARRELL T.Fully Convolutional Networks for Semantic Segmentation[EB/OL].(2016-05-20)[2024-06-11].https://arxiv.org/pdf/1605.06211.
[71] 张涵，秦昆，毕奇，等.注意力引导的三维卷积网络用于遥感场景变化检测[J].应用科学学报，2021,39(2):272-280.
[72] 李长城，何海清，章李乐，等.基于Xception模型的遥感影像场景变化检测[J].江西科学，2022,40(3):568-573.
[73] SAKURADA K,SHIBUYA M,WANG W M.Weakly Supervised Silhouette-based Semantic Scene Change Detection[C]//2020 IEEE International Conference on Robo-tics and Automation.Paris:IEEE,2020:6861-6867.
[74] 陈洪瑞轩.深度特征学习驱动的高分辨率遥感影像变化检测研究[D].武汉：武汉大学，2022.
[75] CAO Y X,HUANG X.A Full-level Fused Cross-task Transfer Learning Method for Building Change Detection Using Noise-robust Pretrained Networks on Crowdsourced Labels[J].Remote Sensing of Environment,2023,284:113371.
[76] ZHENG X T,CHEN X M,LU X Q,et al.Unsupervised Change Detection by Cross-resolution Difference Learning[J].IEEE Transactions on Geoscience and Remote Sen-sing,2021,60:1-16.
[77] DU B,WANG Y,WU C,et al.Unsupervised Scene Change Detection via Latent Dirichlet Allocation and Multivariate Alteration Detection[J].IEEE Journal of Selec-ted Topics in Applied Earth Observations and Remote Sensing,2018,11(12):4676-4689.
[78] ZHANG L L,XIE Y X,LUAN X D,et al.Multi-source Heterogeneous Data Fusion:International Conference on Artificial Intelligence and Big Data[C]//2018 International Conference on Artificial Intelligence and Big Data (ICAIBD).Chengdu:IEEE,2018:47-51.
[79] WANG X,YI J B,GUO J,et al.A Review of Image Super-resolution Approaches Based on Deep Learning and Applications in Remote Sensing[J].Remote Sensing,2022,14(21):5423.
[80] GONG M G,YANG H L,ZHANG P Z.Feature Learning and Change Feature Classification Based on Deep Learning for Ternary Change Detection in SAR Images[J].ISPRS Journal of Photogrammetry and Remote Sensing,2017,129:212-225.
[81] ZHENG Z,ZHONG Y F,WANG J J,et al.Foreground-aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery[C]//2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Seattle:IEEE,2020:4095-4104.
[82] SUDIPAN S,BOVOLO F,BRUZZONE L.Unsupervised Deep Change Vector Analysis for Multiple-change Detection in VHR Images[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(6):3677-3693.
[83] ZHU Q Q,GUO X,LI Z Q,et al.A Review of Multi-class Change Detection for Satellite Remote Sensing Imagery[J].Geo-spatial Information Science,2024,27(1):1-15.
[84] ZHAO W,WANG Z R,GONG M G,et al.Discriminative Feature Learning for Unsupervised Change Detection in Heterogeneous Images Based on a Coupled Neural Network[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(12):7066-7080.
[85] LIU Z G,ZHANG Z W,PAN Q.Unsupervised Change Detection from Heterogeneous Data Based on Image Translation[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-13.
[86] LV Z G,HUANG H T,SUN W W.Novel Enhanced UNet for Change Detection Using Multimodal Remote Sensing Image[J].IEEE Geoscience and Remote Sensing Letters,2023,20:1-5.
[87] DONG S J,WANG L B,BO D,et al.Change CLIP Remote Sensing Change Detection with Multimodal Vision-language Representation Learning[J].ISPRS Journal of Photogrammetry and Remote Sensing,2024,208:53-69.
[88] TONDEWAD M P S,DALE M M P.Remote Sensing Image Registration Methodology Review and Discussion[J].Procedia Computer Science,2020,171:2390-2399.
基本信息:
中图分类号:TP751;TP18
引用信息:
[1]姜凯悦,赵竹新,马璐,等.基于深度学习的遥感影像变化检测方法进展综述[J].无线电工程,2025,55(02):343-356.