Abstract
Remote sensing scene classification (RSSC) has become a hot and challenging research topic in recent years due to its wide applications. Due to the development of convolutional neural networks (CNN), the data-driven CNN-based methods have achieved expressive performance in RSSC. However, the lack of labeled remote sensing scene images in real applications make it difficult to further improve their performance of classification. To address this issue, we propose a novel adaptive category-related pseudo labeling (ACPL) strategy for semi-supervised scene classification. Specifically, ACPL flexibly adjusts thresholds for different classes at each time step to let pass informative unlabeled data and their pseudo labels according to the model’s learning status. Meanwhile, our proposed ACPL dose not introduce additional parameters or computation. We apply ACPL to FixMatch and construct our model RSMatch. Experimental results on UCM data set have indicated that our proposed semi-supervised method RSMatch is superior to its several counterparts for RSSC.
This work was funded in part by the National Natural Science Foundation of China (No. 62171332) and the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tang, X., Ma, Q., Zhang, X., Liu, F., Ma, J., Jiao, L.: Attention consistent network for remote sensing scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 2030–2045 (2021). https://doi.org/10.1109/JSTARS.2021.3051569
Yang, Y., et al.: AR2Det: an accurate and real-time rotational one-stage ship detector in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2022). Art no. 5605414. https://doi.org/10.1109/TGRS.2021.3092433
Tang, X., et al.: An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022). Art no. 5609715. https://doi.org/10.1109/TGRS.2021.3106381
Cheng, G., Yang, C., Yao, X., Guo, L., Han, J.: When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Trans. Geosci. Remote Sens. 56(5), 2811–2821 (2018). https://doi.org/10.1109/TGRS.2017.2783902
Wang, Q., Liu, S., Chanussot, J., Li, X.: Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 57(2), 1155–1167 (2019). https://doi.org/10.1109/TGRS.2018.2864987
Liu, X., Zhou, Y., Zhao, J., Yao, R., Liu, B., Zheng, Y.: Siamese convolutional neural networks for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 16(8), 1200–1204 (2019). https://doi.org/10.1109/LGRS.2019.2894399
Guo, D., Xia, Y., Luo, X.: GAN-based semisupervised scene classification of remote sensing image. IEEE Geosci. Remote Sens. Lett. 18(12), 2067–2071 (2021). https://doi.org/10.1109/LGRS.2020.3014108
Zhang, J., Zhang, M., Pan, B., Shi, Z.: Semisupervised center loss for remote sensing image scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 1362–1373 (2020). https://doi.org/10.1109/JSTARS.2020.2978864
Gomez, P., Meoni, G.: MSMatch: semisupervised multispectral scene classification with few labels. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 11643–11654 (2021). https://doi.org/10.1109/JSTARS.2021.3126082
Li, Y., Zhang, H., Xue, X., et al.: Deep learning for remote sensing image classification: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 8(6), e1264 (2018)
Yang, X., Song, Z., King, I., et al.: A survey on deep semi-supervised learning. arXiv preprint arXiv:2103.00550 (2021)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Sohn, K., Berthelot, D., Carlini, N., et al.: FixMatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596–608 (2020)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems, vol. 17 (2004)
Sutskever, I., Martens, J., Dahl, G., et al.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139–1147. PMLR (2013)
Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Cubuk, E.D., Zoph, B., Shlens, J., et al.: RandAugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702–703 (2020)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Lin, W., Ma, J., Tang, X., Zhang, X., Jiao, L. (2022). RSMatch: Semi-supervised Learning with Adaptive Category-Related Pseudo Labeling for Remote Sensing Scene Classification. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-14903-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-14902-3
Online ISBN: 978-3-031-14903-0
eBook Packages: Computer ScienceComputer Science (R0)
