遥感智能变化检测的深度学习方法：演变与发展趋势

doi:10.11947/j.AGCS.2025.20240417

测绘学报 ›› 2025, Vol. 54 ›› Issue (8): 1347-1370.doi: 10.11947/j.AGCS.2025.20240417

• 综述 • 下一篇

遥感智能变化检测的深度学习方法：演变与发展趋势

张继贤¹(), 顾海燕²(), 倪欢³, 李海涛², 杨懿², 丁少鹏⁴, 隋淞蔓⁴

^1.莫干山地信实验室，浙江　湖州　313299
^2.中国测绘科学研究院，北京　100830
^3.南京信息工程大学，江苏　南京　210044
^4.山东科技大学，山东　青岛　266590

收稿日期:2024-10-10 修回日期:2025-07-26 出版日期:2025-09-16 发布日期:2025-09-16
通讯作者: 顾海燕 E-mail:zhangjx@casm.ac.cn;guhy@casm.ac.cn
作者简介:张继贤（1965—），男，博士，研究员，研究方向为摄影测量与遥感、地理信息系统、资源与环境遥感监测。E-mail：zhangjx@casm.ac.cn
基金资助:
2025年度浙江省尖兵科技计划项目(2025C01073)

Deep learning methods for remote sensing intelligent change detection: evolution and development

Jixian ZHANG¹(), Haiyan GU²(), Huan NI³, Haitao LI², Yi YANG², Shaopeng DING⁴, Songman SUI⁴

^1.Moganshan Geospatial Information Laboratory, Huzhou 313299, China
^2.Chinese Academy of Surveying and Mapping, Beijing 100830, China
^3.Nanjing University of Information Science and Technology, Nanjing 210044, China
^4.Shandong University of Science and Technology, Qingdao 266590, China

Received:2024-10-10 Revised:2025-07-26 Online:2025-09-16 Published:2025-09-16
Contact: Haiyan GU E-mail:zhangjx@casm.ac.cn;guhy@casm.ac.cn
About author:ZHANG Jixian (1965—), male, PhD, researcher, majors in photogrammetry and remote sensing, geographic information system, resources and environment remote sensing monitoring. E-mail: zhangjx@casm.ac.cn
Supported by:
2025 Zhejiang Vanguard S&T Program Project(2025C01073)

摘要/Abstract

摘要：

多模态遥感对地观测和深度学习技术的快速发展，拓展了遥感变化检测的数据维度和方法维度，为更加自动化、精细化和智能化的变化检测奠定了基础。本文聚焦深度学习的变化检测，面向变化特征表达和网络学习策略两个基本科学问题，详细梳理了变化检测研究的演变过程。变化特征表达层面，呈现4个方面的研究趋势，即局部、全局到时空联合特征表达，单一模态到多模态特征表达，轻量级模型到大模型特征表达，以及二值到多类别语义特征表达；网络学习层面，呈现全监督、弱/半监督到无监督变化检测的发展趋势。在此基础上，探讨了当前基于深度学习的变化检测所面临的挑战，并结合当前人工智能技术的发展趋势，指出了图文融合、生成式、人机协同模式3个发展方向，以期为理论方法及应用研究提供方向及思路，助力提升遥感变化检测的智能化能力与应用水平。

关键词: 变化检测, 深度学习, 多模态, 语义变化, 人机协同

Abstract:

The rapid development of multimodal remote sensing and deep learning technologies has expanded the data and method dimensions of remote sensing change detection, laying the foundation for more automated, refined, and intelligent change detection. This article focuses on change detection based on deep learning, addressing two fundamental scientific issues: change feature expression and network learning strategies, and detailing the evolution of change detection research. In terms of change feature expression, there are four research trends: from local to global and spatiotemporal integration, from single modality to multimodality, from lightweight models to large models, and from binary to multi-category semantic feature expression. In terms of network learning, there is a development trend from fully supervised to weak/semi-supervised to unsupervised change detection. Based on this, the article discusses the current challenges faced by deep learning-based change detection and, in conjunction with the development trends of artificial intelligence technology, points out three development directions: text-image fusion, generative, and human-computer collaborative modes. This aims to provide direction and ideas for theoretical methods and application research, and to enhance the intelligence and application level of remote sensing change detection.

Key words: change detection, deep learning, multi-modal, semantic change, human-computer collaboration

中图分类号:

P237

张继贤, 顾海燕, 倪欢, 李海涛, 杨懿, 丁少鹏, 隋淞蔓. 遥感智能变化检测的深度学习方法：演变与发展趋势[J]. 测绘学报, 2025, 54(8): 1347-1370.

Jixian ZHANG, Haiyan GU, Huan NI, Haitao LI, Yi YANG, Shaopeng DING, Songman SUI. Deep learning methods for remote sensing intelligent change detection: evolution and development[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(8): 1347-1370.

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类型	含义	代表模型	优点	缺点
基于局部特征的变化检测	关注图像中的小区域或局部区域特征，捕捉细节和近邻像素的相互作用	CNN VGG ResNet HRNet-V2 GCN	对局部细微的变化敏感，不易受到整体图像的影响。对光照和视角变化不敏感，具有一定稳健性	缺乏对全局上下文和大范围结构的理解，易受到噪声干扰
基于全局特征的变化检测	关注图像的整体布局和特征，用于监测大范围的变化区域	GAS-Net^[8] VGG16-Res Net18-Transformer^[9] CNN-Transformer^[10-13] ELGC-Net^[14] M AFGNet^[15] PGCFN^[16] CSAGC^[17] CSI-Net^[18] TCD-Net^[19] TransY-Net^[20] LAaI^[21] CF-GCN^[22] EATDer^[23] SLDDNet^[24] GLCL^[25] LGP-Net^[26] MSPSNet^[27] DESSN^[28] D2AGCN^[29] ICIF-Net^[30] HyperNet^[31] SNU-PS^[6]	能捕获宽广的上下文信息，有助于理解整体结构	易忽略局部细节信息，导致对细微变化的检测效果不佳
基于时空联合特征的变化检测	不仅考虑空间上的特征组合，还结合了时间维度上的变化，适用于处理动态变化的数据	AMTNet^[32] MSDFFN^[33] MTCD^[34] TriTF^[35] CCLNet^[36] L-UNet^[37] AMMF+谱约束策略^[38] LSTM+TGLSTM^[39] ML-EDAN^[40] FDINet^[41] CSDBF^[42] EGRCNN^[43] HMCNet^[44]	时空联合模型擅长捕捉随时间变化的模式，有效去除伪变化，适合处理视频和多时相图像等数据	模型复杂度较高，训练和解释性方面存在挑战

模态类型	细分类型	含义	代表方法	优点	缺点
跨模态变化检测	—	变化前和变化后遥感图像之间存在成像模式、分辨率差异。挖掘不同模态图像间的结构关系，以发现变化信息	SR-GCAE^[53] ESR-DMNet^[54] URCNet^[55] FD-MCD^[56] SDIR^[1] FD-MCD^[51] PRBCD-Net^[52]	顾及了变化前后所获取的遥感图像潜在的模态差异问题	未充分利用多模态数据(遥感图像、三维数字表面模型、文本数据等)的协同表达优势
多模态融合变化检测	二维、三维信息融合变化检测	协同二维遥感图像与三维数字表面模型的变化特征表达	文献[57] 文献[58] 文献[59]	充分利用了遥感图像和三维数字表面模型的协同表达优势，有助于变化发现	未顾及变化前后可能出现的遥感图像模态差异问题
多模态融合变化检测	图文融合变化检测	协同遥感图像与文本、问答机制的变化特征表达	ChangeCLIP^[60] CDVQA^[61]	充分发挥了现有图文协同的大模型优势，有助于变化特征表达	预训练大模型的依赖程度高

模型	类型	含义	代表方法	优点	缺点
轻量级模型	数据融合	融合双时相影像输入语义分割分类网络中，进行特征学习与分类	波段直接叠加波段差异增强	直接进行特征学习与分类	对影像匹配要求高
	数据转换	通过域转换方法，将输入影像转换为特征描述相似的影像	GAN^[67] 特征转换^[68]	减少因影像差异造成的结果误差	过度依赖转换效果
	孪生组合	采用孪生网络实现变化检测	Transformer	共享权值，提取深度特征图	伪孪生网络不能共享特征，增加了特征获取难度
大模型	提示学习	利用视觉编码器提取多尺度特征	SAM^[71-72]	提取多尺度特征，具有提取语义特征的能力	依赖提示信息等先验知识，全局学习能力弱
大模型	全局学习	充分挖掘全局空间上下文信息及时空关系	Mamba^[73] Change Mamba^[74]	具有全局特征建模能力，能够更加精确地检测时空变化	依赖时空建模方法，下游迁移速度慢

语义变化检测	含义	优点	缺点
分类后比较	先利用深度学习网络对两期影像进行分类，再比较分类结果得到语义变化检测结果	没有变化样本的情况下，容易实现	依赖分类结果的误差传递
多任务学习	同时实现二值和语义变化检测	同时提取不同时相的语义特征，实现二值和语义变化检测	样本不平衡会降低性能
差异特征表达	利用两个结构不同的网络模型提取多尺度空间信息，精确识别和定位变化区域	能够提高模型对复杂变化场景的识别能力	挖掘复杂场景的差异特征是面临的挑战
语义推理	利用语义推理方法分析变化区域的语义信息	不仅可以定位变化发生的位置，还能提供变化的语义信息	依赖语义推理模块性能

类型	含义	代表模型	优点	缺点
卷积神经网络全监督变化检测	利用深度学习中的卷积神经网络结构，在有标签的训练数据上进行训练，实现对变化区域的准确检测和描述	MSP-CD^[88] CNN^[85-87] ReCNN^[86] KPConv^[89] BEDRNet^[90]	自动特征提取，泛化能力强，能进行多任务学习	有过拟合风险，模型解释性差，对变化类型敏感
注意力网络全监督变化检测	利用注意力机制进行训练，能自动识别并聚焦于变化区域，提高变化检测的精度和效率	SEDANet^[93] WNet^[15] VcT^[94] GAS-Net^[47] GeoFormer^[95] MANet^[96] MSDFFN^[33] Conv TransNet^[97] DSAMNet^[98] ADHR-CDNet^[99] FSANet^[47] CBAMUNet+++^[100]	注意力机制可以捕捉长距离依赖，能够适应不同场景和条件下的变化检测，提高模型的泛化能力	过度依赖注意力突出的区域，忽略了其他潜在重要的变化信号
语义变化网络全监督变化检测	不仅检测变化区域，还识别变化前后的语义类别	SCanFormer^[102] DEFO-MT LSCD^[103] JFRNet^[104] SGSLN^[105]	提供了变化区域的详细语义分析	在有限的变化样本条件下，语义信息的利用仍然是挑战

遥感智能变化检测的深度学习方法：演变与发展趋势

Deep learning methods for remote sensing intelligent change detection: evolution and development

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类型	含义	代表方法	优点	缺点
半监督变化检测方法	设计有效的推理机制，利用标注数据获取高质量伪标签	ISCDNet^[106] DCENet^[107] FPA^[45] ECPS^[108] Obj-SiamNet^[109] ICDA-CD^[110]	利用样本共性特征提高对伪标签的判别能力	模型结构较复杂，难以通过训练准确进行特征迁移
半监督变化检测方法	生成式深度学习网络实现对未标注数据的泛化	SemiBuildingChange^[111] SemiCDNet^[112] IAugCDNet^[82] MDF-LSR-Net^[113]	利用生成对抗网络进行特征对齐，实现对未标注数据泛化	网络结构设计多为两阶段，需要进行过程控制，避免误差积累
弱监督变化检测方法	利用模糊聚类，进行变化区域标注	CS-WSCDNet^[114] WSLCD^[73] SDCDNet^[115]	采用区域判别的方法进行精确的像素级标注标签	进行渐近迭代优化，但难以准确获得像素标注

类型	含义	代表方法	优点	缺点
基于聚类分析的无监督变化检测	利用深度学习网络提取高维度特征表达，引入传统的聚类分析方法，在特征空间进行二值聚类，以发现变化信息	SGDNNs^[125] PSGM^[126] 文献[127] 文献[42] UCDFormer^[128]	具备一定可解释性	传统聚类方法与深度学习网络的高效、协同优化过程方法仍有待探讨
基于自监督学习的无监督变化检测	将自监督学习思想引入变化检测研究的产物	UA-GSSL^[129] TD-SSCD^[130] SRF^[62] 文献[53] HyperNet^[31] 文献[57]	提升了无监督变化检测精度	训练过程复杂，难以实现端到端的训练
面向异构数据的无监督变化检测	面向变化前后数据模态存在差异情景，利用带有标签的源域数据以及不含有标签的目标域数据训练网络模型，实现目标域数据变化检测	文献[131] GLCL^[25] HiCD^[132] 文献[133] MDCTNet^[5] 文献[134] BGAAEs^[135] CIT^[65]	消除了变化前后数据异构现象的影响，拓展了变化检测方法的应用范围，保证了时效性	精度仍然无法与变化前后数据同质情景相比拟

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