浏览全部资源
扫码关注微信
Published： 16 July 2024 ，
移动端阅览
王梓祺， 李阳， 张睿， 王家宝， 李允臣， 陈瑶. 2024. 小样本SAR图像分类方法综述. 中国图象图形学报， 29(07):1902-1920
Wang Ziqi， Li Yang， Zhang Rui， Wang Jiabao， Li Yunchen， Chen Yao. 2024. Few-shot SAR image classification： a survey. Journal of Image and Graphics， 29(07):1902-1920
合成孔径雷达（synthetic aperture radar，SAR）图像分类作为SAR图像应用的重要底层任务受到了广泛关注与研究。SAR图像分类是处理和分析遥感图像的重要手段，在环境监测、目标侦察和地质勘探等任务中发挥着关键作用，但是目前基于深度学习的SAR图像分类任务存在小样本问题。本文针对小样本SAR图像分类方法进行全面的论述和分析。1）介绍了SAR图像分类任务的重要性和早期的SAR图像分类方法，并阐述了小样本SAR图像分类任务的必要性。2）介绍了小样本SAR图像分类任务的定义、常用的数据集、评价指标和应用。3）整理了各类方法的贡献点和使用的数据集，将已有的小样本SAR图像分类方法分为基于迁移学习的方法、基于元学习的方法、基于度量学习的方法和综合性方法4类。根据分类总结了4类方法存在的缺陷，为后续工作提供了一定的参考。在统一的框架内测试了16种可见光数据集方法迁移到SAR图像数据集上的分类性能，并从分类精度和运行时间两个方面综合评估了小样本学习模型迁移效果。该项工作利用SAR图像分类通用数据集MSTAR（moving and stationary target acquisition and recognition）完成，极大地补充了小样本SAR图像分类任务的测评基准。4）对小样本SAR图像分类方法的发展趋势进行了展望，提出了未来可能的一些严峻挑战。
Few-shot synthetic aperture radar （SAR） image classification aims to use a small number of training samples to classify new SAR images and facilitate subsequent vision tasks further. In recent years， it has received widespread attention in the field of image processing， especially playing a crucial role in tasks such as environmental monitoring， target reconnaissance， and geological exploration. Moreover， the growth of deep learning has been promoting deep learning-based few-shot SAR image classification. In particular， the improvement of few-shot learning algorithm， such as the attention mechanism， transfer learning， and meta learning， has led to a qualitative leap in few-shot SAR image classification performance. However， a comprehensive review and analysis of state-of-the-art deep learning-based few-shot SAR image classification algorithms for different complex scenes need to be conducted. Thus， we dev
elop a systematic and critical review to explore the developments of few-shot SAR image classification in recent years. First， a comprehensive and systematic introduction of the few-shot SAR image classification field is presented from three aspects： 1） overview of early SAR image classification methods， 2） the existing dataset， and 3） the prevailing evaluation metrics. Then， the existing few-shot SAR image classification methods are categorized into four types： transfer learning， meta learning， metric learning， and comprehensive methods. The main contributions and the datasets used for each method are summarized. Therefore， we test the classification accuracy and runtime of 16 classic few-shot visible light image classification methods on the moving and stationary target acquisition and recognition （MSTAR） dataset. In this way， the evaluation benchmark for few-shot SAR image classification methods is supplemented for future research reference. Finally， the summary and challenges in the few-shot SAR image classification community are highlighted. In particular， some prospects are recommended further in the field of few-shot SAR image classification. First， starting from the classification criteria， SAR image classification methods can be divided into four categories based on the feature information used， whether manual sample labeling is required， technical methods， and processing objects. These traditional SAR image classification methods lay the foundation for subsequent few-shot SAR image classification methods. We briefly introduce the popular public datasets and prevailing evaluation metrics. The existing datasets for few-shot SAR image classification include MSTAR， OpenSARShip， COSMO-SkyMed， FuSAR-Ship， OpenSARUrban， and SAR-ACD. Among them， MSTAR is the most commonly used standard few-shot SAR image classification dataset. The evaluation indicators for method performance in few-shot SAR image classification tasks mainly include classification accuracy， precision， and recall. Precision and recall represent
two different indicators， which is why intuitively reflecting the performance of the model is difficult. Therefore， the harmonic mean of these two indicators has become a direct indicator for judging the performance of the model. In addition， few-shot learning commonly uses top 1 and top 5 as evaluation indicators. Second， few-shot SAR image classification methods based on deep learning can be divided into three categories： transfer learning， meta learning， and metric learning. Transfer learning methods quickly adapt to the new class image classification by using the association between similar tasks to assist the model after completing the pre-training on a large number of base class data. This type of method can effectively overcome the problem of insufficient training samples in the field of SAR images. Meta learning methods aim to enable models to learn by training a meta learner to evaluate the dataset learning process and gain learning experience. Then， the model utilizes the acquired learning experience to complete relevant classification tasks on the target dataset. Metric learning methods are an end-to-end training approach that utilizes data from each
K
-shot category to learn a feature embedding space. In this feature embedding space， the model can more effectively measure the similarity between samples. This type of method relatively reduces the difficulty of training feature extractors， making the structure of the model more flexible and able to quickly adapt to the task of identifying new classes. As a result of the different imaging principles between SAR images and visible light images， some comprehensive methods guided by physical knowledge and domain knowledge have also been used in SAR image classification tasks and achieved great results. Therefore， in addition to the above three classification methods， some methods that combine deep learning and SAR image characteristics have been applied to solve the problem of few-shot SAR image classification. We summarize the limitations o
f different few-shot SAR image classification algorithms and provide some recommendations for further research. Third， we tested the classification performance of 16 visible light dataset methods migrating to SAR image datasets within a unified framework and comprehensively evaluated the transfer effect of few-shot learning models from two aspects： classification accuracy and runtime. This work can effectively supplement the evaluation benchmark for few-shot SAR image classification tasks. The experiment found that the few-shot learning method based on metric learning achieved good performance in the field of SAR image classification without comprehensive methods. Finally， the review summarizes the future development trends and challenges of few-shot SAR image classification based on a summary of existing methods.
小样本学习SAR图像分类迁移学习元学习度量学习
few-shot learningSAR image classificationtransfer learningmeta learningmetric learning
Afrasiyabi A， Larochelle H， Lalonde J F and Gagné C. 2022. Matching feature sets for few-shot image classification//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 9004-9014 ［DOI： 10.1109/CVPR52688.2022.00881http://dx.doi.org/10.1109/CVPR52688.2022.00881］
Antonelli S， Avola D， Cinque L， Crisostomi D， Foresti G L， Galasso F， Marini M R， Mecca A and Pannone D. 2022. Few-shot object detection： a survey. ACM Computing Surveys， 54（11s）： #242 ［DOI： 10.1145/3519022http://dx.doi.org/10.1145/3519022］
Bertinetto L， Henriques J F， Torr P H S and Vedaldi A. 2019. Meta-learning with differentiable closed-form solvers//Proceedings of the 7th International Conference on Learning Representations. New Orleans， USA： OpenReview.net
Cai J L， Zhang Y T， Guo J Y， Zhao X， Lv J W and Hu Y X. 2022. ST-PN： a spatial transformed prototypical network for few-shot SAR image classification. Remote Sensing， 14（9）： #2019 ［DOI： 10.3390/RS14092019http://dx.doi.org/10.3390/RS14092019］
Cao C J， Cui Z Y， Cao Z J， Wang L Y and Yang J Y. 2021. An integrated counterfactual sample generation and filtering approach for SAR automatic target recognition with a small sample set. Remote Sensing， 13（19）： #3864 ［DOI： 10.3390/rs13193864http://dx.doi.org/10.3390/rs13193864］
Chen J K， Qiu X L， Ding C B and Wu Y R. 2022. SAR image classification based on spiking neural network through spike-time dependent plasticity and gradient descent. ISPRS Journal of Photogrammetry and Remote Sensing， 188： 109-124 ［DOI： 10.1016/j.isprsjprs.2022.03.021http://dx.doi.org/10.1016/j.isprsjprs.2022.03.021］
Chen L C and Fu D Y. 2022. Survey on machine learning methods for small sample data. Computer Engineering， 48（11）： 1-13
陈良臣， 傅德印. 2022. 面向小样本数据的机器学习方法研究综述. 计算机工程， 48（11）： 1-13 ［DOI： 10.19678/j.issn.1000-3428.0065347http://dx.doi.org/10.19678/j.issn.1000-3428.0065347］
Chen W Y， Liu Y C， Kira Z， Wang Y C F and Huang J B. 2019. A closer look at few-shot classification//Proceedings of the 7th International Conference on Learning Representations. New Orleans， USA： OpenReview.net
Chen Y， Meng H B， Wen X L， Ma P G， Qin Y X， Ma Z X and Liu Z Y. 2018. Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks. EURASIP Journal on Wireless Communications and Networking， 2018（1）： #127 ［DOI： 10.1186/s13638-018-1133-2http://dx.doi.org/10.1186/s13638-018-1133-2］
Chen Y B， Liu Z， Xu H J， Darrell T and Wang X L. 2021. Meta-baseline： exploring simple meta-learning for few-shot learning//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 9062-9071 ［DOI： 10.1109/ICCV48922.2021.00893http://dx.doi.org/10.1109/ICCV48922.2021.00893］
Dong C Q， Li W B， Huo J， Gu Z and Gao Y. 2020. Learning task-aware local representations for few-shot learning//Proceedings of the 29th International Joint Conference on Artificial Intelligence. Yokohama， Japan： IJCAI.org： 716-722 ［DOI： 10.24963/IJCAI.2020/100http://dx.doi.org/10.24963/IJCAI.2020/100］
Dong H W， Song K C， Wang Q， Yan Y H and Jiang P. 2022. Deep metric learning-based for multi-target few-shot pavement distress Classification. IEEE Transactions on Industrial Informatics， 18（3）： 1801-1810 ［DOI： 10.1109/TII.2021.3090036http://dx.doi.org/10.1109/TII.2021.3090036］
Feng K X and Chaspari T. 2023. Few-shot learning in emotion recognition of spontaneous speech using a Siamese neural network with adaptive sample pair formation. IEEE Transactions on Affective Computing， 14（2）： 1627-1633 ［DOI： 10.1109/TAFFC.2021.3109485http://dx.doi.org/10.1109/TAFFC.2021.3109485］
Finn C， Abbeel P， Levine S. 2017. Model-agnostic meta-learning for fast adaptation of deep networks//Proceedings of the 34th International Conference on Machine Learning. Sydney， Australia： JMLR.org： 1126-1135
Frikha A， Krompaß D， Köpken H G and Tresp V. 2021. Few-shot one-class classification via meta-learning//Proceedings of the 35th AAAI Conference on Artificial Intelligence， 33rd Conference on Innovative Applications of Artificial Intelligence， The 11th Symposium on Educational Advances in Artificial Intelligence. ［s.l.］： AAAI Press： 7448-7456 ［DOI： 10.1609/aaai.v35i8.16913http://dx.doi.org/10.1609/aaai.v35i8.16913］
Fu K， Zhang T F， Zhang Y， Wang Z R and Sun X. 2022. Few-shot SAR target classification via metalearning. IEEE Transactions on Geoscience and Remote Sensing， 60： #2000314 ［DOI： 10.1109/TGRS.2021.3058249http://dx.doi.org/10.1109/TGRS.2021.3058249］
Gao H H， Xiao J S， Yin Y Y， Liu T and Shi J G. 2022. A mutually supervised graph attention network for few-shot segmentation： the perspective of fully utilizing limited samples. IEEE Transactions on Neural Networks and Learning Systems， 35（4）： 4826-4838 ［DOI： 10.1109/TNNLS.2022.3155486http://dx.doi.org/10.1109/TNNLS.2022.3155486］
Ge Y Z， Liu H， Wang Y， Xu B L， Zhou Q and Shen F R. 2022. Survey on deep learning image recognition in dilemma of small samples. Journal of Software， 33（1）： 193-210
葛轶洲， 刘恒， 王言， 徐百乐， 周青， 申富饶. 2022. 小样本困境下的深度学习图像识别综述. 软件学报， 33（1）： 193-210 ［DOI： 10.13328/j.cnkijos.006342http://dx.doi.org/10.13328/j.cnkijos.006342］
Geng J， Deng X Y， Ma X R and Jiang W. 2020. Transfer learning for SAR image classification via deep joint distribution adaptation networks. IEEE Transactions on Geoscience and Remote Sensing， 58（8）： 5377-5392 ［DOI： 10.1109/TGRS.2020.2964679http://dx.doi.org/10.1109/TGRS.2020.2964679］
Gong S R， Xu S J， Zhou L F， Zhu J and Zhong S. 2022. Deformable atrous convolution nearshore SAR small ship detection incorporating mixed attention. Journal of Image and Graphics， 27（12）： 3663-3676
龚声蓉， 徐少杰， 周立凡， 朱杰， 钟珊. 2022. 融入混合注意力的可变形空洞卷积近岸SAR小舰船检测. 中国图象图形学报， 27（12）： 3663-3676 ［DOI： 10.11834/jig.210866http://dx.doi.org/10.11834/jig.210866］
Gordon J， Bronskill J， Bauer M， Nowozin S and Turner R E. 2019. Meta-learning probabilistic inference for prediction//Proceedings of the 7th International Conference on Learning Representations. New Orleans， USA： OpenReview.net
Guo J Y， Lei B， Ding C B and Zhang Y T. 2017. Synthetic aperture radar image synthesis by using generative adversarial nets. IEEE Geoscience and Remote Sensing Letters， 14（7）： 1111-1115 ［DOI： 10.1109/LGRS.2017.2699196http://dx.doi.org/10.1109/LGRS.2017.2699196］
Hou X Y， Ao W， Song Q， Lai J， Wang H P and Xu F. 2020. FUSAR-Ship： building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition. Science China Information Sciences， 63（4）： #140303 ［DOI： 10.1007/s11432-019-2772-5http://dx.doi.org/10.1007/s11432-019-2772-5］
Huang L Q， Liu B， Li B Y， Guo W W， Yu W H， Zhang Z H and Yu W X. 2018. OpenSARShip： a dataset dedicated to Sentinel-1 ship interpretation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 11（1）： 195-208 ［DOI： 10.1109/JSTARS.2017.2755672http://dx.doi.org/10.1109/JSTARS.2017.2755672］
Huang Z L， Dumitru C O， Pan Z， Lei B and Datcu M. 2021. Classification of large-scale high-resolution SAR images with deep transfer learning. IEEE Geoscience and Remote Sensing Letters， 18（1）： 107-111 ［DOI： 10.1109/LGRS.2020.2965558http://dx.doi.org/10.1109/LGRS.2020.2965558］
Huang Z L， Pan Z X and Lei B. 2020. What， where， and how to transfer in SAR target recognition based on deep CNNs. IEEE Transactions on Geoscience and Remote Sensing， 58（4）： 2324-2336 ［DOI： 10.1109/TGRS.2019.2947634http://dx.doi.org/10.1109/TGRS.2019.2947634］
Huang Z L， Yao X W， Liu Y， Dumitru C O， Datcu M and Han J W. 2022. Physically explainable CNN for SAR image classification. ISPRS Journal of Photogrammetry and Remote Sensing， 190： 25-37 ［DOI： 10.1016/j.isprsjprs.2022.05.008http://dx.doi.org/10.1016/j.isprsjprs.2022.05.008］
Jian Y R and Torresani L. 2022. Label hallucination for few-shot classification//Proceedings of the 36th AAAI Conference on Artificial Intelligence， 34th Conference on Innovative Applications of Artificial Intelligence， The 12th Symposium on Educational Advances in Artificial Intelligence. ［s.l.］： AAAI Press： 7005-7014 ［DOI： 10.1609/AAAI.v36i6.20659http://dx.doi.org/10.1609/AAAI.v36i6.20659］
Jiang W， Huang K， Geng J and Deng X Y. 2021. Multi-scale metric learning for few-shot learning. IEEE Transactions on Circuits and Systems for Video Technology， 31（3）： 1091-1102 ［DOI： 10.1109/TCSVT.2020.2995754http://dx.doi.org/10.1109/TCSVT.2020.2995754］
Kaul P， Xie W D and Zisserman A. 2022. Label， verify， correct： a simple few shot object detection method//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 14217-14227 ［DOI： 10.1109/CVPR52688.2022.01384http://dx.doi.org/10.1109/CVPR52688.2022.01384］
Keydel E R， Lee S W and Moore J T. 1996. MSTAR extended operating conditions： a tutorial//Proceedings of SPIE 2757. Algorithms for Synthetic Aperture Radar Imagery III. Orlando， USA： SPIE： 228-242 ［DOI： 10.1117/12.242059http://dx.doi.org/10.1117/12.242059］
Krizhevsky A， Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM， 60（6）： 84-90 ［DOI： 10.1145/3065386http://dx.doi.org/10.1145/3065386］
Lai J X， Yang S Q， Jiang G N， Wang X， Li Y X， Jia Z H， Chen X C， Liu J， Gao B B， Zhang W， Xie Y and Wang C J. 2022. Rethinking the metric in few-shot learning： from an adaptive multi-distance perspective//Proceedings of the 30th ACM International Conference on Multimedia. Lisboa， Portugal： ACM： 4021-4030 ［DOI： 10.1145/3503161.3547853http://dx.doi.org/10.1145/3503161.3547853］
Lang H T， Yang G A， Li C N and Xu J W. 2022. Multisource heterogeneous transfer learning via feature augmentation for ship classification in SAR imagery. IEEE Transactions on Geoscience and Remote Sensing， 60： #5228814 ［DOI： 10.1109/TGRS.2022.3178703http://dx.doi.org/10.1109/TGRS.2022.3178703］
LeCun Y， Bengio Y and Hinton G. 2015. Deep learning. Nature， 521（7553）： 436-444 ［DOI： 10.1038/nature14539http://dx.doi.org/10.1038/nature14539］
Li H R， Wang T Y and Wang S W. 2022b. Few-Shot SAR target classification combining both spatial and frequency information//Proceedings of 2022 IEEE Global Communications Conference. Rio de Janeiro， Brazil： IEEE： 480-485 ［DOI： 10.1109/GLOBECOM48099.2022.10001168http://dx.doi.org/10.1109/GLOBECOM48099.2022.10001168］
Li P， Zhao G P and Xu X H. 2022a. Coarse-to-fine few-shot classification with deep metric learning. Information Sciences， 610： 592-604 ［DOI： 10.1016/j.ins.2022.08.048http://dx.doi.org/10.1016/j.ins.2022.08.048］
Li W， Gao Y H， Zhang M M， Tao R and Du Q. 2023b. Asymmetric feature fusion network for hyperspectral and SAR image classification. IEEE Transactions on Neural Networks and Learning Systems， 34（10）： 8057-8070 ［DOI： 10.1109/TNNLS.2022.3149394http://dx.doi.org/10.1109/TNNLS.2022.3149394］
Li W B， Wang L， Xu J L， Huo J， Gao Y and Luo J B. 2019b. Revisiting local descriptor based image-to-class measure for few-shot learning//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 7253-7260 ［DOI： 10.1109/CVPR.2019.00743http://dx.doi.org/10.1109/CVPR.2019.00743］
Li W B， Wang Z Y， Yang X S， Dong C Q， Tian P Z， Qin T X， Huo J， Shi Y H， Wang L， Gao Y and Luo J B. 2023a. LibFewShot： a comprehensive library for few-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence， 45（12）： 14938-14955 ［DOI： 10.1109/TPAMI.2023.3312125http://dx.doi.org/10.1109/TPAMI.2023.3312125］
Li W B， Xu J L， Huo J， Wang L， Gao Y and Luo J B. 2019c. Distribution consistency based covariance metric networks for few-shot learning//Proceedings of the 33rd AAAI Conference on Artificial Intelligence， The 31st Innovative Applications of Artificial Intelligence Conference， The 9th AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu， USA： AAAI Press： 8642-8649 ［DOI： 10.1609/AAAI.v33i01.33018642http://dx.doi.org/10.1609/AAAI.v33i01.33018642］
Li X， Zhang G， Cui H， Hou S S， Wang S Y， Li X， Chen Y J， Li Z J and Zhang L. 2022c. 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 X M， Yu L Q， Fu C W， Fang M and Heng P A. 2020. Revisiting metric learning for few-shot image classification. Neurocomputing， 406： 49-58 ［DOI： 10.1016/J.NEUCOM.2020.04.040http://dx.doi.org/10.1016/J.NEUCOM.2020.04.040］
Li X X， Sun Z， Xue J H and Ma Z Y. 2021. A concise review of recent few-shot meta-learning methods. Neurocomputing， 456： 463-468 ［DOI： 10.1016/j.neucom.2020.05.114http://dx.doi.org/10.1016/j.neucom.2020.05.114］
Li X X， Yang X C， Ma Z Y and Xue J H. 2023c. Deep metric learning for few-shot image classification： a review of recent developments. Pattern Recognition， 138： #109381 ［DOI： 10.1016/j.patcog.2023.109381http://dx.doi.org/10.1016/j.patcog.2023.109381］
Li Y， Wang J B， Xu Y L， Li H， Miao Z and Zhang Y F. 2017. DeepSAR-Net： deep convolutional neural networks for SAR target recognition//Proceedings of the 2nd International Conference on Big Data Analysis （ICBDA）. Beijing， China： IEEE： 740-743 ［DOI： 10.1109/ICBDA.2017.8078734http://dx.doi.org/10.1109/ICBDA.2017.8078734］
Li Y B， Li X， Sun Q and Dong Q H. 2022e. SAR image classification using CNN embeddings and metric learning. IEEE Geoscience and Remote Sensing Letters， 19： #4002305 ［DOI： 10.1109/LGRS.2020.3022435http://dx.doi.org/10.1109/LGRS.2020.3022435］
Li Y Y， Peng C， Chen Y Q， Jiao L C， Zhou L H and Shang R H. 2019a. 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］
Li Z K， Liu M， Chen Y S， Xu Y M， Li W and Du Q. 2022d. Deep cross-domain few-shot learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing， 60： #5501618 ［DOI： 10.1109/TGRS.2021.3057066http://dx.doi.org/10.1109/TGRS.2021.3057066］
Liang K J， Rangrej S B， Petrovic V and Hassner T. 2022. Few-shot learning with noisy labels//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 9079-9088 ［DOI： 10.1109/CVPR52688.2022.00888http://dx.doi.org/10.1109/CVPR52688.2022.00888］
Lin Z， Ji K F， Kang M， Leng X G and Zou H X. 2017. Deep convolutional highway unit network for SAR target classification with limited labeled training data. IEEE Geoscience and Remote Sensing Letters， 14（7）： 1091-1095 ［DOI： 10.1109/LGRS.2017.2698213http://dx.doi.org/10.1109/LGRS.2017.2698213］
Liu D Y， Gao X Z and Shen Q M. 2020. Prototypical network for radar image recognition with few samples. Journal of Physics： Conference Series， 1634（1）： #012116 ［DOI： 10.1088/1742-6596/1634/1/012116http://dx.doi.org/10.1088/1742-6596/1634/1/012116］
Liu Q， Zhang X Y and Liu Y X. 2022. Few-shot SAR target recognition based on gated multi-scale matching network. Systems Engineering and Electronics， 44（11）： 3346-3356
刘旗， 张新禹， 刘永祥. 2022. 基于门控多尺度匹配网络的小样本SAR目标识别. 系统工程与电子技术， 44（11）： 3346-3356 ［DOI： 10.12305/j.issn.1001-506X.2022.11.08http://dx.doi.org/10.12305/j.issn.1001-506X.2022.11.08］
Liu Q F， Cao W M and He Z H. 2023a. Cycle optimization metric learning for few-shot classification. Pattern Recognition， 139： #109468 ［DOI： 10.1016/j.patcog.2023.109468http://dx.doi.org/10.1016/j.patcog.2023.109468］
Liu W， Bao Q， Sun Y and Mei T. 2023b. Recent advances of monocular 2D and 3D human pose estimation： a deep learning perspective. ACM Computing Surveys， 55（4）： #80 ［DOI： 10.1145/3524497http://dx.doi.org/10.1145/3524497］
Liu Y， Lei Y B， Fan J L， Wang F P， Gong Y C and Tian Q. 2021. Survey on image classification technology based on small sample learning. Acta Automatica Sinica， 47（2）： 297-315
刘颖， 雷研博， 范九伦， 王富平， 公衍超， 田奇. 2021. 基于小样本学习的图像分类技术综述. 自动化学报， 47（2）： 297-315 ［DOI： 10.16383/j.aas.c190720http://dx.doi.org/10.16383/j.aas.c190720］
Lu D， Cao L Y and Liu H W. 2019. Few-shot learning neural network for SAR target recognition//Proceedings of the 6th Asia-Pacific Conference on Synthetic Aperture Radar （APSAR）. Xiamen， China： IEEE： 1-4 ［DOI： 10.1109/APSAR46974.2019.9048517http://dx.doi.org/10.1109/APSAR46974.2019.9048517］
Oh J， Youm G Y and Kim M. 2021a. SPAM-net： A CNN-based SAR target recognition network with pose angle marginalization learning. IEEE Transactions on Circuits and Systems for Video Technology， 31（2）： 701-714 ［DOI： 10.1109/TCSVT.2020.2987346http://dx.doi.org/10.1109/TCSVT.2020.2987346］
Oh J， Yoo H， Kim C H and Yun S Y. 2021b. Boil： towards representation change for few-shot learning//Proceedings of the 9th International Conference on Learning Representations. ［s.l］： OpenReview.net
Pan S J and Yang Q. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering， 22（10）： 1345-1359 ［DOI： 10.1109/TKDE.2009.191http://dx.doi.org/10.1109/TKDE.2009.191］
Pan Z X， Bao X J， Zhang Y T， Wang B W， An Q Z and Lei B. 2019. Siamese network based metric learning for SAR target classification//Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium （IGARSS）. Yokohama， Japan： IEEE： 1342-1345 ［DOI： 10.1109/IGARSS.2019.8898210http://dx.doi.org/10.1109/IGARSS.2019.8898210］
Raghu A， Raghu M， Bengio S and Vinyals O. 2020. Rapid learning or feature reuse？ Towards understanding the effectiveness of MAML//Proceedings of the 8th International Conference on Learning Representations. Addis Ababa， Ethiopia： OpenReview.net
Rajasegaran J， Khan S， Hayat M， Khan F S and Shah M. 2021. Self-supervised knowledge distillation for few-shot learning//Proceedings of the 32nd British Machine Vision Conference. ［s.l］： BMVA Press： #179
Rostami M， Kolouri S， Eaton E and Kim K. 2019a. SAR image classification using few-shot cross-domain transfer learning//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 907-915 ［DOI： 10.1109/CVPRW.2019.00120http://dx.doi.org/10.1109/CVPRW.2019.00120］
Rostami M， Kolouri S， Eaton E and Kim K. 2019b. Deep transfer learning for few-shot SAR image classification. Remote Sensing， 11（11）： #1374 ［DOI： 10.3390/rs11111374http://dx.doi.org/10.3390/rs11111374］
Rusu A A， Rao D， Sygnowski J， Vinyals O， Pascanu R， Osindero S and Hadsell R. 2019. Meta-learning with latent embedding optimization//Proceedings of the 7th International Conference on Learning Representations. New Orleans， USA： OpenReview.net
Shang R H， Wang J M， Jiao L C， Yang X H and Li Y Y. 2022. Spatial feature-based convolutional neural network for PolSAR image classification. Applied Soft Computing， 123： #108922 ［DOI： 10.1016/j.asoc.2022.108922http://dx.doi.org/10.1016/j.asoc.2022.108922］
Shi Z H， Wu C W， Li C J， You Z Z， Wang Q and Ma C C. 2023. Object detection techniques based on deep learning for aerial remote sensing images： a survey. Journal of Image and Graphics， 28（9）： 2616-2643
石争浩， 仵晨伟， 李成建， 尤珍臻， 王泉， 马城城. 2023. 航空遥感图像深度学习目标检测技术研究进展. 中国图象图形学报， 28（9）： 2616-2643 ［DOI： 10.11834/jig.221085http://dx.doi.org/10.11834/jig.221085］
Singh R， Bharti V， Purohit V， Kumar A， Singh A K and Singh S K. 2021. MetaMed： few-shot medical image classification using gradient-based meta-learning. Pattern Recognition， 120： #108111 ［DOI： 10.1016/J.PATCOG.2021.108111http://dx.doi.org/10.1016/J.PATCOG.2021.108111］
Snell J， Swersky K and Zemel R. 2017. Prototypical networks for few-shot learning//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach， USA： Curran Associates Inc.： 4080-4090
Song Y S， Wang T， Cai P Y， Mondal S K and Sahoo J P. 2023. A comprehensive survey of few-shot learning： evolution， applications， challenges， and opportunities. ACM Computing Surveys， 55（13s）： #271 ［DOI： 10.1145/3582688http://dx.doi.org/10.1145/3582688］
Sun Q R， Liu Y Y， Chua T S and Schiele B. 2019. Meta-transfer learning for few-shot learning//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 403-412 ［DOI： 10.1109/CVPR.2019.00049http://dx.doi.org/10.1109/CVPR.2019.00049］
Sun X， Lv Y X， Wang Z R and Fu K. 2022. SCAN： Scattering characteristics analysis network for few-shot aircraft classification in high-resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing， 60： #5226517 ［DOI： 10.1109/TGRS.2022.3166174http://dx.doi.org/10.1109/TGRS.2022.3166174］
Sung F， Yang Y X， Zhang L， Xiang T， Torr P H S and Hospedales T M. 2018. Learning to compare： relation network for few-shot learning//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 1199-1208 ［DOI： 10.1109/CVPR.2018.00131http://dx.doi.org/10.1109/CVPR.2018.00131］
Tan X F， Li M， Zhang P， Wu Y and Song W Y. 2021. Deep triplet complex-valued network for PolSAR image classification. IEEE Transactions on Geoscience and Remote Sensing， 59（12）： 10179-10196 ［DOI： 10.1109/TGRS.2021.3053013http://dx.doi.org/10.1109/TGRS.2021.3053013］
Tang J X， Zhang F， Zhou Y S， Yin Q and Hu W. 2019. A fast inference networks for SAR target few-shot learning based on improved Siamese networks//Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium （IGARSS）. Yokohama， Japan： IEEE： 1212-1215 ［DOI： 10.1109/IGARSS.2019.8898180http://dx.doi.org/10.1109/IGARSS.2019.8898180］
Tang L F， Zhang H， Xu H and Ma J Y. 2023. Deep learning-based image fusion： a survey. Journal of Image and Graphics， 28（1）： 3-36
唐霖峰， 张浩， 徐涵， 马佳义. 2023. 基于深度学习的图像融合方法综述. 中国图象图形学报， 28（1）： 3-36 ［DOI： 10.11834/jig.220422http://dx.doi.org/10.11834/jig.220422］
Wang H， Chen X， Tian S Z and Chen D B. 2020. SAR image recognition based on few-shot learning. Computer Science， 47（5）： 124-128
汪航， 陈晓， 田晟兆， 陈端兵. 基于小样本学习的SAR图像识别. 计算机科学， 47（5）： 124-128 ［DOI： 10.11896/jsjkx.190400136http://dx.doi.org/10.11896/jsjkx.190400136］
Wang J J， Li W， Gao Y H， Zhang M M， Tao R and Du Q. 2023. Hyperspectral and SAR image classification via multiscale interactive fusion network. IEEE Transactions on Neural Networks and Learning Systems， 34（12）： 10823-10837 ［DOI： 10.1109/TNNLS.2022.3171572http://dx.doi.org/10.1109/TNNLS.2022.3171572］
Wang K and Zhang G. 2020. SAR target recognition via meta-learning and amortized variational inference. Sensors， 20（20）： #5966 ［DOI： 10.3390/s20205966http://dx.doi.org/10.3390/s20205966］
Wang L， Bai X R， Gong C and Zhou F. 2021. Hybrid inference network for few-shot SAR automatic target recognition. IEEE Transactions on Geoscience and Remote Sensing， 59（11）： 9257-9269 ［DOI： 10.1109/TGRS.2021.3051024http://dx.doi.org/10.1109/TGRS.2021.3051024］
Wang L， Bai X R and Zhou F. 2019. Few-shot SAR ATR based on conv-BiLSTM prototypical networks//Proceedings of the 6th Asia-Pacific Conference on Synthetic Aperture Radar （APSAR）. Xiamen， China： IEEE： 1-5 ［DOI： 10.1109/APSAR46974.2019.9048492http://dx.doi.org/10.1109/APSAR46974.2019.9048492］
Wang R F， Wang L， Li C， Huo C L and Chen J W. 2023. IIQ-CNN-based cross-domain change detection of SAR images. Journal of Image and Graphics， 28（7）： 2208-2220
王蓉芳， 王良， 李畅， 霍春雷， 陈佳伟. 2023. 整型推理量化CNN的SAR图像跨域变化检测. 中国图象图形学报， 28（7）： 2208-2220［DOI： 10.11834/jig.211159http://dx.doi.org/10.11834/jig.211159］
Wang Y Y， Wang C and Zhang H. 2018. Ship classification in high-resolution SAR images using deep learning of small datasets. Sensors， 18（9）： #2929 ［DOI： 10.3390/s18092929http://dx.doi.org/10.3390/s18092929］
Wang Z C， Fu X Y and Xia K W. 2022. Target classification for single-channel SAR images based on transfer learning with subaperture decomposition. IEEE Geoscience and Remote Sensing Letters， 19： #4003205 ［DOI： 10.1109/LGRS.2020.3027363http://dx.doi.org/10.1109/LGRS.2020.3027363］
Wei D， Li Y and Huang D. 2020. Overview on methods of land classification based on polarimetric SAR images. Computer Systems and Applications， 29（11）： 29-39.
魏丹， 李渊， 黄丹. 2020. 极化SAR图像地物分类方法综述. 计算机系统应用， 29（11）： 29-39 ［DOI： 10.15888/j.cnki.csa.007705http://dx.doi.org/10.15888/j.cnki.csa.007705］
Wu J Y， Zhao Z B， Sun C， Yan R Q and Chen X F. 2020. Few-shot transfer learning for intelligent fault diagnosis of machine. Measurement， 166： #108202 ［DOI： 10.1016/j.measurement.2020.108202http://dx.doi.org/10.1016/j.measurement.2020.108202］
Xie H Z， Yao H X， Zhou S C， Zhang S P and Sun W X. 2021. Efficient regional memory network for video object segmentation//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 1286-1295 ［DOI： 10.1109/CVPR46437.2021.00134http://dx.doi.org/10.1109/CVPR46437.2021.00134］
Xing M D， Xie Y Y， GAO Y X， Zhang J S， Liu J M and Wu Z X. 2022. Electromagnetic scattering characteristic extraction and imaging recognition algorithm： a review. Journal of Radars， 11（6）： 921-942
邢孟道， 谢意远， 高悦欣， 张金松， 刘嘉铭， 吴之鑫. 2022. 电磁散射特征提取与成像识别算法综述. 雷达学报， 11（6）： 921-942 ［DOI： 10.12000/JR22232http://dx.doi.org/10.12000/JR22232］
Xu H， Wang J X， Li H， Ouyang D Q and Shao J. 2021b. Unsupervised meta-learning for few-shot learning. Pattern Recognition， 116： #107951 ［DOI： 10.1016/j.patcog.2021.107951http://dx.doi.org/10.1016/j.patcog.2021.107951］
Xu J Y and Le H. 2022. Generating representative samples for few-shot classification//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 8993-9003 ［DOI： 10.1109/CVPR52688.2022.00880http://dx.doi.org/10.1109/CVPR52688.2022.00880］
Xu Y J and Lang H T. 2020. Distribution shift metric learning for fine-grained ship classification in SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 13： 2276-2285 ［DOI： 10.1109/JSTARS.2020.2991784http://dx.doi.org/10.1109/JSTARS.2020.2991784］
Xu Y J， Sun H， Chen J， Lei L， Ji K F and Kuang G Y. 2021a. Adversarial self-supervised learning for robust SAR target recognition. Remote Sensing， 13（20）： #4158 ［DOI： 10.3390/rs13204158http://dx.doi.org/10.3390/rs13204158］
Yang M J， Bai X R， Wang L and Zhou F. 2022c. Mixed loss graph attention network for few-shot SAR target classification. IEEE Transactions on Geoscience and Remote Sensing， 60： 1-13 ［DOI： 10.1109/TGRS.2021.3124336http://dx.doi.org/10.1109/TGRS.2021.3124336］
Yang M J， Jiao L C， Liu F， Hou B， Yang S Y， Zhang Y K and Wang J L. 2022b. Coarse-to-fine contrastive self-supervised feature learning for land-cover classification in SAR images with limited labeled data. IEEE Transactions on Image Processing， 31： 6502-6516 ［DOI： 10.1109/TIP.2022.3211472http://dx.doi.org/10.1109/TIP.2022.3211472］
Yang R， Xu X， Li X R， Wang L and Pu F L. 2020. Learning relation by graph neural network for SAR image few-shot learning//Proceedings of 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa， USA： IEEE： 1743-1746 ［DOI： 10.1109/IGARSS39084.2020.9323139http://dx.doi.org/10.1109/IGARSS39084.2020.9323139］
Yang Y J， Singha S and Mayerle R. 2022a. A deep learning based oil spill detector using Sentinel-1 SAR imagery. International Journal of Remote Sensing， 43（11）： 4287-4314 ［DOI： 10.1080/01431161.2022.210944http://dx.doi.org/10.1080/01431161.2022.210944］
Yao S Y， Kang Q， Zhou M C， Rawa M J and Abusorrah A. 2023. A survey of transfer learning for machinery diagnostics and prognostics. Artificial Intelligence Review， 56（4）： 2871-2922 ［DOI： 10.1007/s10462-022-10230-4http://dx.doi.org/10.1007/s10462-022-10230-4］
Yasir M， Wan J H， Xu M M， Sheng H， Zeng Z， Liu S W， Colak A T I and Hossain M S. 2023. Ship detection based on deep learning using SAR imagery： a systematic literature review. Soft Computing， 27（1）： 63-84 ［DOI： 10.1007/s00500-022-07522-whttp://dx.doi.org/10.1007/s00500-022-07522-w］
Yazdanpanah M， Rahman A A， Chaudhary M， Desrosiers C， Havaei M， Belilovsky E and Kahou S E. 2022. Revisiting learnable affines for batch norm in few-shot transfer learning//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 9099-9108 ［DOI： 10.1109/CVPR52688.2022.00890http://dx.doi.org/10.1109/CVPR52688.2022.00890］
Ye H J， Hu H X， Zhan D C and Sha F. 2020. Few-shot learning via embedding adaptation with set-to-set functions//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 8805-8814 ［DOI： 10.1109/CVPR42600.2020.00883http://dx.doi.org/10.1109/CVPR42600.2020.00883］
Ying Z L， Wang W Q， Xu Y and Li W B. 2023. Twin self-supervised learning method for small sample SAR images automatic target recognition. Journal of Signal Processing， 39（11）： 2080-2090
应自炉， 王文琪， 徐颖， 李文霸. 2023. 面向小样本SAR图像自动目标识别的孪生自监督学习方法. 信号处理， 39（11）： 2080-2090 ［DOI： 10.16798/j.issn.1003-0530.2023.11.017http://dx.doi.org/10.16798/j.issn.1003-0530.2023.11.017］
Ying Z L， Xuan C， Zhai Y K， Sun B， Li J W， Deng W B， Mai C Y， Wang F G， Labati R D， Piuri V and Scotti F. 2020. TAI-SARNET： deep transferred atrous-inception CNN for small samples SAR ATR. Sensors， 20（6）： #1724 ［DOI： 10.3390/s20061724http://dx.doi.org/10.3390/s20061724］
Yuan Z W， Tang C， Yang A X， Huang W D and Chen W. 2023. Few-shot remote sensing image scene classification based on metric learning and local descriptors. Remote Sensing， 15（3）： #831 ［DOI： 10.3390/RS15030831http://dx.doi.org/10.3390/RS15030831］
Zamir A R， Sax A， Shen W， Guibas L， Malik J and Savarese S. 2018. Taskonomy： disentangling task transfer learning//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 3712-3722 ［DOI： 10.1109/CVPR.2018.00391http://dx.doi.org/10.1109/CVPR.2018.00391］
Zeng Q J and Geng J. 2022. Task-specific contrastive learning for few-shot remote sensing image scene classification. ISPRS Journal of Photogrammetry and Remote Sensing， 191： 143-154 ［DOI： 10.1016/j.isprsjprs.2022.07.013http://dx.doi.org/10.1016/j.isprsjprs.2022.07.013］
Zhai Y K， Deng W B， Lan T， Sun B， Ying Z L， Gan J Y， Mai C Y， Li J W， Labati R D， Piuri V and Scotti F. 2020. MFFA-SARNET： deep transferred multi-level feature fusion attention network with dual optimized loss for small-sample SAR ATR. Remote Sensing， 12（9）： #1385 ［DOI： 10.3390/rs12091385http://dx.doi.org/10.3390/rs12091385］
Zhai Y K， Zhou W， Sun B， Li J W， Ke Q R， Ying Z L， Gan J Y， Mai C Y， Labati R D， Piuri V and Scotti F. 2022. Weakly contrastive learning via batch instance discrimination and feature clustering for small sample SAR ATR. IEEE Transactions on Geoscience and Remote Sensing， 60： #5204317 ［DOI： 10.1109/TGRS.2021.3066195http://dx.doi.org/10.1109/TGRS.2021.3066195］
Zhang G J， Luo Z P， Cui K W， Lu S J and Xing E P. 2023b. Meta-DETR： image-level few-shot detection with inter-class correlation exploitation. IEEE Transactions on Pattern Analysis and Machine Intelligence， 45（11）： 12832-12843 ［DOI： 10.1109/TPAMI.2022.3195735http://dx.doi.org/10.1109/TPAMI.2022.3195735］
Zhang L， Yang J and Zhang D. 2017. Domain class consistency based transfer learning for image classification across domains. Information Sciences， 418-419： 242-257 ［DOI： 10.1016/j.ins.2017.08.034http://dx.doi.org/10.1016/j.ins.2017.08.034.］
Zhang L B， Leng X G， Feng S J， Ma X J， Ji K F， Kuang G Y and Liu L. 2022. Domain knowledge powered two-stream deep network for few-shot SAR vehicle recognition. IEEE Transactions on Geoscience and Remote Sensing， 60： #5215315 ［DOI： 10.1109/TGRS.2021.3116349http://dx.doi.org/10.1109/TGRS.2021.3116349］
Zhang R， Yang Y X， Li Y， Wang J B， Li H and Miao Z. 2023a. Multi‐task few‐shot learning with composed data augmentation for image classification. IET Computer Vision， 17（2）： 211-221 ［DOI： 10.1049/cvi2.12150http://dx.doi.org/10.1049/cvi2.12150］
Zhang R， Yang Y X， Li Y， Wang J B， Miao Z， Li H and Wang Z Q. 2022. Self-supervised learning based few-shot remote sensing scene image classification. Journal of Image and Graphics， 27（11）： 3371-3381
张睿， 杨义鑫， 李阳， 王家宝， 苗壮， 李航， 王梓祺. 2022. 自监督学习下小样本遥感图像场景分类. 中国图象图形学报， 27（11）： 3371-3381 ［DOI： 10.11834/jig210486http://dx.doi.org/10.11834/jig210486］
Zhao J P， Zhang Z H， Yao W， Datcu M， Xiong H L and Yu W X. 2020. OpenSARUrban： a sentinel-1 SAR image dataset for urban interpretation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 13： 187-203 ［DOI： 10.1109/JSTARS.2019.2954850http://dx.doi.org/10.1109/JSTARS.2019.2954850］
Zhao K L， Jin X L and Wang Y Z. 2021. Survey on few-shot learning. Journal of Software， 32（2）： 349-369
赵凯琳， 靳小龙， 王元卓. 2021. 小样本学习研究综述. 软件学报， 32（2）： 349-369 ［DOI： 10.13328/j.cnki.jos.006138http://dx.doi.org/10.13328/j.cnki.jos.006138.］
Zhao P F， Huang L J， Xin Y， Guo J Y and Pan Z X. 2021. Multi-aspect SAR target recognition based on prototypical network with a small number of training samples. Sensors， 21（13）： #4333 ［DOI： 10.3390/s21134333http://dx.doi.org/10.3390/s21134333］
Zhao Y Q and Cheung N M. 2023. FS-BAN： born-again networks for domain generalization few-shot classification. IEEE Transactions on Image Processing， 32： 2252-2266 ［DOI： 10.1109/TIP.2023.3266172http://dx.doi.org/10.1109/TIP.2023.3266172］
Zhong C L， Mu X D， He X C， Wang J X and Zhu M. 2019. SAR target image classification based on transfer learning and model compression. IEEE Geoscience and Remote Sensing Letters， 16（3）： 412-416 ［DOI： 10.1109/LGRS.2018.2876378http://dx.doi.org/10.1109/LGRS.2018.2876378］
Zhu Z D， Lin K X， Jain A K and Zhou J Y. 2023. Transfer learning in deep reinforcement learning： a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence， 45（11）： 13344-13362 ［DOI： 10.1109/TPAMI.2023.3292075http://dx.doi.org/10.1109/TPAMI.2023.3292075］
相关文章
相关作者
相关机构
京公网安备11010802024621