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Cross-domain transfer learning algorithm for few-shot ship recognition in remote-sensing images

  • role： First author第一作者
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China

  • Email：chj247@hdu.edu.cn
  • Introduction：陈华杰， 研究方向为机器学习和图像处理。E-mail： chj247@hdu.edu.cn
CHEN Huajie,  
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China

LYU Danni,  
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China

ZHOU Xiao,  
  • Affiliation：

    Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China

LIU Jun

реферат

Cross-domain transfer learning aims to utilize public datasets as source data to improve the recognition accuracy of target data, breaking through the limitation that the category space between source data and target data must be consistent.For the few-shot remote-sensing ship recognition task, existing cross-domain transfer learning algorithms have the disadvantages of transfer category restriction and negative transfer effect. Therefore, a cross-domain transfer learning algorithm based on source data correlation sorting was proposed to solve the above problems.First, the target data were added reversely into the source domain recognition task. According to the variation of the source data recognition accuracy before and after the target data were added, various source data were classified into strong/weak/negative correlation samples, and only the strong correlation samples would be selected. Then, the self-supervised joint learning strategy was adopted to introduce the auxiliary self-supervised angle prediction branch into the classification network in the target domain. The selected strong correlation source samples were added but only into the training of the self-supervised branch, which avoided changing the main classification network structure.Randomly selecting 65 category samples as the source data from miniImageNet and conducting comparative experiments on few-shot ship targets in remote-sensing images yields the following results: 1) When Resnet18 is chosen as the classification network, the performance of the proposed algorithm is better than that of the Fine-tune algorithm, which is widely used in cross-domain transfer learning. Moreover, compared with the recognition algorithm, which only uses the main classification network, the proposed algorithm improves the recognition accuracy of target data from 78.89% to 96.48%. 2) Using different networks to sort correlations for the source data, the selected strong correlation source samples are not exactly the same and their degree of category coincidence is close to 60%. However, they are all helpful to the classification task of the target domain. At the end of this paper, through visualizing the extracted target features, it is verified that the target features extracted by using the proposed algorithm are more abundant and have higher generalization ability.The proposed algorithm has two main advantages. First, the weak/negative correlation source samples are eliminated by correlation sorting, which can avoid the occurrence of negative transfer effect. Second, by introducing the self-supervised angle prediction branch, the information of the strong correlation source samples is fully utilized and the features with more generalization ability are extracted while maintaining the structural integrity of the main classification network.

ключеви́че слова́

remote sensing;Ship recognition;few-shot learning;Cross-domain transfer learning;Correlation sorting;self-supervised learning

References

  1. 1.
    Chen J H and Wang X L. 2022. Multi-graph convolutional network for a remote sensing image few shot classification. National Remote Sensing Bulletin, 26（10）: 2029-2042
  2. 2.
    Demirci M F and Kacka S. 2016. Object recognition by distortion-free graph embedding and random forest//Proceedings of the 2016 IEEE Tenth International Conference on Semantic Computing. Laguna Hills: IEEE: 17-23
  3. 3.
    Gidaris S, Bursuc A, Komodakis N, Pérez Pérez P and Cord M. 2019. Boosting few-shot visual learning with self-supervision//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE: 8058-8067
  4. 4.
    Hayat S, Kun S, Zuo T T, Yu Y, Tu T Y and Du Y T. 2018. A deep learning framework using convolutional neural network for multi-class object recognition//Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing. Chongqing: IEEE: 194-198
  5. 5.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778 [DOI: 10.1109/CVPR.2016.90].
  6. 6.
    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
  7. 7.
    Li H. 2012. Statistical Learning Method. Beijing: Tsinghua University Press: 78-86
  8. 8.
    Liu Z K, Yuan L, Weng L B and Yang Y P. 2017. A high resolution optical satellite image dataset for ship recognition and some new baselines//Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. ‏Porto: SciTePress: 324-331
  9. 9.
    Long M S, Cao Y, Cao Z J, Wang J M and Jordan M I. 2019. Transferable representation learning with deep adaptation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12): 3071-3085
  10. 10.
    Long M S, Zhu H, Wang J M and Jordan M I. 2017. Deep transfer learning with joint adaptation networks//Proceedings of the 34th International Conference on Machine Learning. Sydney: JMLR.org: 2208-2217
  11. 11.
    Lu J B, Xi Z M, Yuan X H, Yu G S and Zhang M M. 2011. Ship target recognition using high resolution range profiles based on FMT and SVM//Proceedings of 2011 IEEE CIE International Conference on Radar. Chengdu: IEEE: 1299-1302
  12. 12.
    Pei Z Y, Cao Z J, Long M S and Wang J M. 2018. Multi-adversarial domain adaptation//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. New Orleans: AAAI: 3934-3941
  13. 13.
    Sun Y F, Zheng L, Yang Y, Tian Q and Wang S J. 2018. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline)//Proceedings of the 15th European Conference on Computer Vision. Munich: Springer: 501-518
  14. 14.
    Tan B, Song Y Q, Zhong E H and Yang Q. 2015. Transitive transfer learning//Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney: ACM: 1155-1164
  15. 15.
    Tan B, Zhang Y, Pan S J and Yang Q. 2017. Distant domain transfer learning//Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco: AAAI : 2604-2610
  16. 16.
    Wu T H, Wang T W and Liu Y Q. 2021. Real-time vehicle and distance detection based on improved Yolo v5 network//Proceedings of the 2021 3rd World Symposium on Artificial Intelligence. Guangzhou: IEEE: 24-28
  17. 17.
    Yosinski J, Clune J, Bengio Y and Lipson H. 2014. How transferable are features in deep neural networks?//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal: MIT Press: 3320-3328
  18. 18.
    Zhao K L, Jin X L and Wang Y Z. 2021. Survey on few-shot learning. Journal of Software, 32(2): 349-369
  19. 19.
    Zhong Q, Chen L and Qian Y T. 2020. Few-shot learning for remote sensing image retrieval with MAML//Proceedings of 2020 IEEE International Conference on Image Processing. Abu Dhabi: IEEE: 2446-2450
  20. 20.
    Zhuang F Z, Qi Z Y, Duan K Y, Xi D B, Zhu Y C, Zhu H S, Xiong H and He Q. 2021. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1): 43-76

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