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Research Article

IDN: inner-class dense neighbours for semi-supervised learning-based remote sensing scene classification

ORCID Icon, , , , &
Pages 80-90 | Received 31 May 2022, Accepted 06 Dec 2022, Published online: 01 Jan 2023

References

  • Arazo, E., D. Ortego, P. Albert, N. E. O’Connor, and K. McGuinness. 2020. “Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning.” In 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 1–8. IEEE.
  • Berthelot, D., N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel. 2019. “Mixmatch: A Holistic Approach to Semi-Supervised Learning.” Advances in Neural Information Processing Systems 32.
  • Chapelle, O., and A. Zien. 2005. “Semi-Supervised Classification by Low Density Separation“. In Lawrence, Neil (Ed), International Workshop on Artificial Intelligence and Statistics, 57–64. PMLR.
  • Cheng, G., J. Han, and X. Lu. 2017. “Remote Sensing Image Scene Classification: Benchmark and State of the Art.” Proceedings of the IEEE 105 (10): 1865–1883. doi:10.1109/JPROC.2017.2675998.
  • Gu, X., and P. P. Angelov. 2019. “A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classification.” In INNS Big Data and Deep Learning conference, Sestri Levante, Italy, 257–266. Springer.
  • Han, W., R. Feng, L. Wang, and Y. Cheng. 2018. “A Semi-Supervised Generative Framework with Deep Learning Features for High-Resolution Remote Sensing Image Scene Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 145: 23–43. doi:10.1016/j.isprsjprs.2017.11.004.
  • Han, X., Y. Zhong, L. Cao, and L. Zhang. 2017. “Pre-Trained Alexnet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification.” Remote Sensing 9 (8): 848. doi:10.3390/rs9080848.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In IEEE conference on computer vision and pattern recognition, Las Vegas, Nevada, 770–778.
  • Huang, G., Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. 2017. “Densely Connected Convolutional Networks.” In IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii, 4700–4708.
  • Liu, Q., R. Hang, H. Song, and Z. Li. 2017. “Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (1): 117–126. doi:10.1109/TGRS.2017.2743243.
  • Maaten, L. V. D., and G. Hinton. 2008. “Visualizing Data using t-SNE.” Journal of Machine Learning Research 9 (Nov): 2579–2605.
  • Ma, A., Y. Wan, Y. Zhong, J. Wang, and L. Zhang. 2021. “SceneNet: Remote Sensing Scene Classification Deep Learning Network Using Multi-Objective Neural Evolution Architecture Search.” ISPRS Journal of Photogrammetry and Remote Sensing 172: 171–188. doi:10.1016/j.isprsjprs.2020.11.025.
  • Miyato, T., S.I. Maeda, M. Koyama, and S. Ishii. 2018. “Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (8): 1979–1993. doi:10.1109/TPAMI.2018.2858821.
  • Sohn, K., D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. Dogus Cubuk, A. Kurakin, and C.L. Li. 2020. “Fixmatch: Simplifying Semi-Supervised Learning with Consistency and Confidence.” Advances in Neural Information Processing Systems 33: 596–608.
  • Verma, V., K. Kawaguchi, A. Lamb, J. Kannala, A. Solin, Y. Bengio, and D. Lopez-Paz. 2022. “Interpolation Consistency Training for Semi-Supervised Learning.” Neural Networks 145: 90–106. doi:10.1016/j.neunet.2021.10.008.
  • Wang, S., Y. Guan, and L. Shao. 2020. “Multi-Granularity Canonical Appearance Pooling for Remote Sensing Scene Classification.” IEEE Transactions on Image Processing 29: 5396–5407. doi:10.1109/TIP.2020.2983560.
  • Yuan, Y., J. Fang, X. Lu, and Y. Feng. 2018. “Remote Sensing Image Scene Classification Using Rearranged Local Features.” IEEE Transactions on Geoscience and Remote Sensing 57 (3): 1779–1792. doi:10.1109/TGRS.2018.2869101.
  • Zhang, H., M. Cisse, Y. N. Dauphin, and D. Lopez-Paz. 2018. “Mixup: Beyond Empirical Risk Minimization.” In International Conference on Learning Representations, Vancouver, Canada, 1–13.
  • Zhu, Q., Y. Lei, X. Sun, Q. Guan, Y. Zhong, L. Zhang, and D. Li. 2022a. “Knowledge-Guided Land Pattern Depiction for Urban Land Use Mapping: A Case Study of Chinese Cities.” Remote Sensing of Environment 272: 112916. doi:10.1016/j.rse.2022.112916.
  • Zhu, Q., Y. Sun, Q. Guan, L. Wang, and W. Lin. 2022b. “A Weakly Pseudo-Supervised Decorrelated Subdomain Adaptation Framework for Cross-Domain Land-Use Classification.” IEEE Transactions on Geoscience and Remote Sensing.

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