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为从宏观上对深度学习在遥感图像分类任务中的研究现状与前沿进展进行深度分析，从Web of Science核心数据库中筛选2014年1月—2023年12月遥感图像分类任务与深度学习的相关文章，得到符合要求的文章2 732篇；再利用VOSviewer和CiteSpace软件对相关文献进行文献计量学分析。结果表明，研究期间深度学习在遥感图像分类任务中的发文量呈上升趋势，发文量最多的作者是武汉大学张良培，机构是中国科学院；共被引次数最多的期刊是《IEEE Transactions on Geoscience and Remote Sensing》；文献是《Deep Residual Learning for Image Recognition》。通过文献计量学分析，将深度学习在遥感图像分类任务中的研究现状可视化，并预测未来潜在的研究趋势，为研究人员提供了多元化的视角。
Abstract：In order to deeply analyze the research status and frontier progress of deep learning in remote sensing image classification from a macro perspective, we screened articles related to deep learning in remote sensing image classification from January 2014 to December 2023 in the core database of Web of Science, and obtained a total of 2 732 articles meeting the requirements. Then, we used VOSviewer and CiteSpace software to conduct bibliometric analysis of relevant literatures. The results show that in the past decade, the number of publications of deep learning in remote sensing image classification has shown an upward trend. Among them, Zhang Liangpei of Wuhan University has the largest number of articles and the Chinese Academy of Sciences has the largest number of articles published. The most cited journal is IEEE Transactions on Geoscience and Remote Sensing, and the most cited literature is Deep Residual Learning for Image Recognition. Through bibliometric analysis, we visualized the research status of deep learning in remote sensing image classification, and predicted potential research trends in the future, providing a diversified perspective for researchers.
[1]Cheng G,Xie X,Han J,et al. Remote Sensing Image Scene Classification Meets Deep Learning:Challenges,Methods,Benchmarks,and Opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,13:3735-3756
[2]李晓斌，江碧涛，王生进.光学遥感图像场景分类技术综述和比较[J].无线电工程，2019,49(4):265-271
[3]Chi M,Plaza A,Benediktsson J A,et al. Big Data for Remote Sensing:Challenges and Opportunities[J]. Proceedings of the IEEE,2016,104(11):2207-2219
[4]彭程，王莉，王安邦，等.基于ResNet18和随机森林的遥感图像复杂场景分类方法[J].山东农业大学学报(自然科学版)，2024,55(3):376-384
[5]Swain M J,Ballard D H. Indexing via Color Histograms[M].Active Perception and Robot Vision,Springer,1992
[6]Tomasi C. Histograms of Oriented Gradients[J]. Computer Vision Sampler,2012,1:1-6
[7]Pietikäinen M. Local Binary Patterns[J]. Scholarpedia,2010,5(3):9775
[8]Lowe D G. Distinctive Image Features from Scale-invariant Keypoints[J]. International Journal of Computer Vision,2004,60(2):91-110
[9]Wang S T,Ai Z T,Dou W B,et al. Research on Multi-source Remote Sensing Image Classification Based on SVM Different Kernel Functions[J]. Journal of Henan Polytechnic University(Natural Science),2011,30(3):304-309
[10]Vishwanathan S V M,Murty M N. SSVM:A Simple SVM Algorithm[C]//Proceedings of the 2002 International Joint Conference on Neural Networks,2002
[11]Oshiro T M,Perez P S,Baranaukas J A. How Many Trees in a Random Forest?[C]. International Workshop on Machine Learning and Data Mining in Pattern Recognition. Berlin,Heidelberg:Springer Berlin Heidelberg,2012
[12]Resende P A A,Drummond A C. A Survey of Random Forest Based Methods for Intrusion Detection Systems[J].ACM Computing Surveys,2018,51(3):1-36
[13]杨忠骥.基于决策树的遥感影像分类方法研究[J].农家参谋，2020(16):293
[14]Charbuty B,Abdulazeez A. Classification Based on Decision Tree Algorithm for Machine Learning[J]. Journal of Applied Science and Technology Trends,2021,2(1):20-28
[15]Chen L,Li S,Bai Q,et al. Review of Image Classification Algorithms Based on Convolutional Neural Networks[J].Remote Sensing,2021,13(22):4712
[16]Song J,Gao S,Zhu Y,et al. A Survey of Remote Sensing Image Classification Based on CNNs[J]. Big Earth Data,2019,3(3):232-254
[17]Zhen L,Qiong L. A New Feature Selection Method for Internet Traffic Classification Using ML[J]. Physics Procedia,2012,33:1338-1345
[18]Lecun Y,Bengio Y,Hinton G. Deep Learning[J]. Nature,2015,521(7553):436
[19]夏文生.基于迁移学习和残差注意力网络的遥感图像分类研究[D].南昌:东华理工大学，2023
[20]Krizhevsky A,Sutskever I,Hinton G E. Image Net Classification with Deep Convolutional Neural Networks[J]. Commun ACM,2017,60(6):84-90
[21]Simonyan K,Zisserman A. Very Deep Convolutional Networks for Large-scale Image Recognition[J]. ar Xiv preprint arXiv:14091556,2014
[22]Szeged Y C,Liu W,Jia Y,et al. Going Deeper with Convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015
[23]He K,Zhang X,Ren S,et al. Deep Residual Learning for Image Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016
[24]Huang G,Liu Z,Laurens V D M,et al. Densely Connected Convolutional Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017
[25]Zhao M,Zhong S,Fu X,et al. Deep Residual Shrinkage Networks for Fault Diagnosis[J]. IEEE Transactions on Industrial Informatics,2019,16(7):4681-4690
[26]Hu J,Shen L,Sun G. Squeeze-and-excitation Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018
[27]Wang Q,Wu B,Zhu P,et al. ECA-Net:Efficient Channel Attention for Deep Convolutional Neural Networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020
[28]Woo S,Park J,Lee J Y,et al. Cbam:Convolutional Block Attention Module[C]//Proceedings of the European Conference on Computer Vsion,2018
[29]张立亭，夏文生，罗亦泳，等.改进双重注意力机制结合ResNet50的遥感图像分类[J].测绘科学，2023,48(4):98-105
[30]Zhou J,Gu X,Gong H,et al. Intelligent Classification of Maize Straw Types from UAV Remote Sensing Images Using Dense Net 201 Deep Transfer Learning Algorithm[J]. Ecological Indicators,2024,166:112331
[31]申传庆，王凯，王文杰.基于ResNet-UNet的地表覆盖自动分类技术研究[J].地理空间信息，2023,21(6):21-23
[32]王威，李希杰，王新.ADC-CPANet:一种局部—全局特征融合的遥感图像分类方法[J].遥感学报，2024,28(10):2661-2672
[33]Huang J,Yuan X,Lam C T,et al. F3net:Feature Filtering Fusing Network for Change Detection of Remote Sensing Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2024,17:10621-10635
[34]覃现，郝容，邢鹏，等.基于深度学习的国土资源遥感影像分类模型[J].地理空间信息，2024,22(10):34-37
[35]Liu B,Chen X,Xue Z,et al. Depth Feature Extraction for Hyperspectral Image Small Sample Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2025,63:1-12
[36]Liu X,Ng A H M,Lei F,et al. Hyperspectral Image Classification Using a Multi-scale CNN Aarchitecture with Asymmetric Convolutions from Small to Large Kernels[J]. Remote Sensing,2025,17(8):1461
[37]马佩坤，李艳，黄小赛，等.卷积神经网络与MRF相结合的高光谱图像分类[J].地理空间信息，2020,18(1):67-72
[38]汪晓洲，石翠萍，杨焜，等.基于深度学习的场景遥感图像分类方法研究[J].齐齐哈尔大学学报(自然科学版)，2021,37(5):11-15
[39]田艳玲，张维桐，张锲石，等.图像场景分类技术综述[J].电子学报，2019,47(4):915-926
[40]李学龙，史建华，董永生，等.场景图像分类技术综述[J].中国科学:信息科学，2015,45(7):827-848
[41]Ma L,Liu Y,Zhang X,et al. Deep Learning in Remote Sensing Applications:A Meta-analysis and Review[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2019,152:166-177
[42]刘小波，尹旭，刘海波，等.深度迁移学习在高光谱遥感图像分类中的研究现状与展望[J].青岛科技大学学报(自然科学版)，2019,40(3):1-11
[43]Li J,Bhatti U A,Nawaz S A,et al. Remote Sensing Image Classification:A Comprehensive Review and Applications[J].Deep Learning for Multimedia Processing Applications,2024,23:18-47
[44]Chavda S,Goyani M. Scene Level Image Classification:A Literature Review[J]. Neural Processing Letters,2023,55(3):2471-2520
[45]Eck N J V,Waltman L. Software Survey:VOSviewer,a Computer Program for Bibliometric Mapping[J]. Scientometrics,2010,84(2):523-538
[46]Chen C. CiteSpace II:Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature[J].Journal of the American Society for Information Science and Technology,2006,57(3):359-377
[47]张裕，杨海涛，袁春慧.遥感图像分类方法综述[J].兵器装备工程学报，2018,39(8):108-112
基本信息:
中图分类号:G353.1;TP18;TP751;P237
引用信息:
[1]夏文生,夏晓婧.深度学习在遥感图像分类中的文献计量学分析[J].地理空间信息,2026,24(09):18-24.
基金信息:
江西省教育厅科学技术研究资助项目（GJJ2403803）; 赣东学院院长基金资助项目（12224000613）
2026-09-28
2026-09-28