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Research Article

Semi-supervised hierarchical Transformer for hyperspectral Image classification

ORCID Icon, , ORCID Icon, ORCID Icon &
Pages 21-50 | Received 03 Jul 2023, Accepted 10 Nov 2023, Published online: 27 Dec 2023

References

  • Abdi, H., and L. J. Williams. 2010. “Principal Component Analysis.” Wiley Interdisciplinary Reviews Computational Statistics 2 (4): 433–459. https://doi.org/10.1002/wics.101.
  • Ahmad, M., S. Shabbir, S. Kumar Roy, D. Hong, W. Xin, J. Yao, A. Mehmood Khan, M. Mazzara, S. Distefano, and J. Chanussot. 2021. “Hyperspectral Image Classification–Traditional to Deep Models: A Survey for Future Prospects.” Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:968–999. https://doi.org/10.1109/JSTARS.2021.3133021.
  • Berthelot, D., N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, C. Raffel, and K. Sohn. 2020. “Remixmatch: Semi–Supervised Learning with Distribution Alignment and Augmentation Anchoring”. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia. April 30.
  • Berthelot, D., N. Carlini, I. Goodfellow, A. Oliver, N. Papernot, and C. Raffel. 2019. “MixMatch: A Holistic Approach to Semi-Supervised Learning”. In 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, vol. 32, Vancouver, BC, Canada. December 8, 2019–December 14, 2019.
  • Cascante-Bonilla, P., F. Tan, Q. Yanjun, and V. Ordonez. 2021. “Curriculum Labeling: Revisiting Pseudo–Labeling for Semi–Supervised Learning.” Association for the Advancement of Artificial Intelligence 35 (8): 6912–6920. https://doi.org/10.1609/aaai.v35i8.16852.
  • Chen, Y., M. Mancini, X. Zhu, and Z. Akata. 2022. “Semi–Supervised and Unsupervised Deep Visual Learning: A Survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence 1–23. https://doi.org/10.1109/TPAMI.2022.3201576.
  • Chong, Y., Y. Ding, Q. Yan, and S. Pan. 2020. “Graph–Based Semi–Supervised Learning: A Review.” Neurocomputing 408:216–230. https://doi.org/10.1016/j.neucom.2019.12.130.
  • Cubuk, E. D., B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. 2019. “Autoaugment: Learning Augmentation Strategies from Data.” In 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, United states. pp. 113–123. June 16, 2019–June 20, 2019. https://doi.org/10.1109/CVPR.2019.00020.
  • Cubuk, E. D., B. Zoph, J. Shlens, and Q. V. Le. 2020. “Randaugment: Practical Automated Data Augmentation with a Reduced Search Space.” In 34th Conference on Neural Information Processing Systems, pp. 3008–3017. December 6, 2020–December 12, 2020.
  • dAscoli, S., H. Touvron, M. L. Leavitt, A. S. Morcos, G. Biroli, and L. Sagun. 2021. “Convit: Improving Vision Transformers with Soft Convolutional Inductive Biases.” In 38th International Conference on Machine Learning, ICML 2021, Virtual, Online. 139:2286–2296. July 18, 2021–July 24, 2021.
  • Ding, Y., X. Zhao, Z. Zhang, W. Cai, N. Yang, and Y. Zhan. 2021. “Semi–Supervised Locality Preserving Dense Graph Neural Network with ARMA Filters and Context–Aware Learning for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 60:1–12. https://doi.org/10.1109/TGRS.2021.3100578.
  • Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, et al. 2020. “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.” In 9th International Conference on Learning Representations, ICLR 2021, Virtual, Online. May 3, 2021–May 7, 2021.
  • Engelen, V., E. Jesper, and H. H., Hoos. 2020. “A Survey on Semi–Supervised Learning.” Machine Learning 109 (2): 373–440. https://doi.org/10.1007/s10994-019-05855-6.
  • Gan, Y., H. Zhu, W. Guo, X. Guangwei, and G. Zou. 2022. “Deep Semi–Supervised Learning with Contrastive Learning and Partial Label Propagation for Image Data.” Knowledge-Based Systems 245:108602. Art. No. 108602. https://doi.org/10.1016/j.knosys.2022.108602.
  • Ghiasi, G., T.-Y. Lin, and Q. V. Le. 2018. “Dropblock: A Regularization Method for Convolutional Networks.” In 32nd Conference on Neural Information Processing Systems, NeurIPS 2018, Montreal, QC, Canada. pp. 10727–10737. December 2, 2018–December 8, 2018.
  • 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. https://doi.org/10.1016/j.isprsjprs.2017.11.004.
  • Hang, Renlong, Liu, Qingshan, Hong, Danfeng, and Ghamisi, Pedram 2019b. “Cascaded Recurrent Neural Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 57 (8): 5384–5394.
  • Hang, R., Q. Liu, D. Hong, and P. Ghamisi. 2019a. “Cascaded Recurrent Neural Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 57 (8): 5384–5394. https://doi.org/10.1109/TGRS.2019.2899129.
  • Hao, W., and S. Prasad. 2017. “Semi–Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification.” IEEE Transactions on Image Processing 27 (3): 1259–1270. https://doi.org/10.1109/TIP.2017.2772836.
  • Haut, J. M., M. E. Paoletti, J. Plaza, A. Plaza, and L. Jun. 2019. “Hyperspectral image classification using random occlusion data augmentation.” IEEE Geoscience & Remote Sensing Letters 16 (11): 1751–1755. https://doi.org/10.1109/LGRS.2019.2909495.
  • Hong, D., L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot. 2020. “Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 59 (7): 5966–5978. https://doi.org/10.1109/TGRS.2020.3015157.
  • Hong, D., Z. Han, J. Yao, L. Gao, B. Zhang, A. Plaza, and J. Chanussot. 2021. “SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers.” IEEE Transactions on Geoscience and Remote Sensing 60:1–15. https://doi.org/10.1109/TGRS.2021.3130716.
  • Jia, S., S. Jiang, Z. Lin, L. Nanying, X. Meng, and Y. Shiqi. 2021. “A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples.” Neurocomputing 448:179–204. https://doi.org/10.1016/j.neucom.2021.03.035.
  • Kjell, O. N., S. Sikström, K. Kjell, and H. Andrew Schwartz. 2022. “Natural Language Analyzed with AI-Based Transformers Predict Traditional Subjective Well-Being Measures Approaching the Theoretical Upper Limits in Accuracy.” Scientific Reports 12 (1): 3918. https://doi.org/10.1038/s41598-022-07520-w.
  • Lee, D.-H. 2013. “Pseudo–Label: The Simple and Efficient Semi–Supervised Learning Method for Deep Neural Networks.” In Workshop on challenges in representation learning, ICML, Atlanta, Georgia, USA. 3:896.
  • Liang, H., W. Bao, X. Shen, and X. Zhang. 2022. “HSI-Mixer: Hyperspectral Image Classification Using the SpectralSpatial Mixer Representation from Convolutions.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2022.3200145.
  • Liu, B., K. Gao, Y. Anzhu, W. Guo, R. Wang, and X. Zuo. 2020. “Semisupervised Graph Convolutional Network for Hyperspectral Image Classification.” Journal of Applied Remote Sensing 14 (2): 1. Art. No. 026516. https://doi.org/10.1117/1.JRS.14.026516.
  • Liu, Z., H. Han, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, et al. 2022. “Swin Transformer V2: Scaling Up Capacity and Resolution.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, United states. pp. 12009–12019. June 19, 2022–June 24, 2022.
  • Liu, Z., Y. Lin, Y. Cao, H. Han, Y. Wei, Z. Zhang, S. Lin, and B. Guo. 2021. “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows.” In 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021, Virtual, Online, Canada. pp. 9992–10002. October 11, 2021–October 17, 2021.
  • Mou, L., L. Xiaoqiang, L. Xuelong, and X. Xiang Zhu. 2020. “Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 58 (12): 8246–8257. https://doi.org/10.1109/TGRS.2020.2973363.
  • Patel, H., and K. P. Upla. 2022. “A Shallow Network for Hyperspectral Image Classification Using an Autoencoder with Convolutional Neural Network.” Multimedia Tools & Applications 81 (1): 695–714. https://doi.org/10.1007/s11042-021-11422-w.
  • Pouyanfar, S., S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. Presa Reyes, M.-L. Shyu, S.-C. Chen, and S. S. Iyengar. 2018. “A Survey on Deep Learning: Algorithms, Techniques, and Applications.” ACM Computing Surveys 51 (5): 1–36. https://doi.org/10.1145/3234150.
  • Qin, A., Z. Shang, J. Tian, Y. Wang, T. Zhang, and Y. Yan Tang. 2018. “Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 16 (2): 241–245. https://doi.org/10.1109/LGRS.2018.2869563.
  • Roy, S. K., A. Deria, D. Hong, B. Rasti, A. Plaza, and J. Chanussot. 2023. “Multimodal Fusion Transformer for Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 61:1–20. https://doi.org/10.1109/TGRS.2023.3286826.
  • Roy, S. K., S. Manna, T. Song, and L. Bruzzone. 2020. “Attention–Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 59 (9): 7831–7843. https://doi.org/10.1109/TGRS.2020.3043267.
  • Seydgar, M., S. Rahnamayan, P. Ghamisi, and A. Asilian Bidgoli. 2022. “Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo–Label Generation Framework.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 60:1–18. https://doi.org/10.1109/TGRS.2022.3195924.
  • Shiqi, Y., S. Jia, and X. Chunyan. 2017. “Convolutional Neural Networks for Hyperspectral Image Classification.” Neurocomputing 219:88–98. https://doi.org/10.1016/j.neucom.2016.09.010.
  • Shutao, L., W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. Atli Benediktsson. 2019. “Deep Learning for Hyperspectral Image Classification: An Overview.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 57 (9): 6690–6709. https://doi.org/10.1109/TGRS.2019.2907932.
  • Sohn, K., D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. Dogus Cubuk, A. Kurakin, and L. Chun-Liang. 2020. “Fixmatch: Simplifying Semi–Supervised Learning with Consistency and Confidence.” In 34th Conference on Neural Information Processing Systems, NeurIPS 2020, Virtual, Online. 33:596–608. December 6, 2020–December 12, 2020.
  • Song, Z., X. Yang, X. Zenglin, and I. King. 2022. “Graph–Based Semi–Supervised Learning: A Comprehensive Review.” IEEE Transactions on Neural Networks and Learning Systems 34 (11): 8174–8194. https://doi.org/10.1109/TNNLS.2022.3155478.
  • Sun, L., G. Zhao, Y. Zheng, W. Zebin, Y. Ban, X. Li, and B. Zhang. 2022. “SpectralSpatial Feature Tokenization Transformer for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 60:1–16. https://doi.org/10.1109/TGRS.2022.3231215.
  • Touvron, H., M. Cord, A. El-Nouby, J. Verbeek, and H. Jégou. 2022. “Three Things Everyone Should Know About Vision Transformers.” In 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel. 13684 LNCS:497–515. October 23, 2022–October 27, 2022.
  • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. “Attention is All You Need.” In 31st Annual Conference on Neural Information Processing Systems, NIPS 2017, Long Beach, CA, United states. 30:5999–6009. December 4, 2017–December 9, 2017.
  • Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou. 2010. “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.” Journal of Machine Learning Research 11 (12): 3371–3408.
  • Wang, J., F. Gao, J. Dong, and D. Qian. 2020. “Adaptive Dropblock–Enhanced Generative Adversarial Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 59 (6): 5040–5053. https://doi.org/10.1109/TGRS.2020.3015843.
  • Wang, J., F. Gao, J. Dong, S. Zhang, and D. Qian. 2022. “Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 15:1823–1836. https://doi.org/10.1109/JSTARS.2022.3146167.
  • Wang, C., X. Miaozhong, Y. Jiang, G. Zhang, H. Cui, L. Litao, and D. Li. 2022. “Translution-SNet: A Semisupervised Hyperspectral Image Stripe Noise Removal Based on Transformer and CNN.” IEEE Transactions on Geoscience & Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3182745.
  • Wang, W., H. Ting, X. Wang, J. Li, M. Huang, and A. Plaza. 2023. “BFRNet: Bidimensional Feature Representation Network for Remote Sensing Images Classification.” IEEE Transactions on Geoscience & Remote Sensing 61:1–16. https://doi.org/10.1109/TGRS.2023.3335484.
  • Wang, J., L. Wei, Y. Gao, M. Zhang, R. Tao, and D. Qian 2022. “Hyperspectral and SAR Image Classification via Multiscale Interactive Fusion Network.” IEEE Transactions on Neural Networks and Learning Systems 34 (12): 10823–10837. https://doi.org/10.1109/TNNLS.2022.3171572.
  • Wang, J., L. Wei, Y. Wang, R. Tao, and D. Qian. 2023. “Representation-Enhanced Status Replay Network for Multisource Remote-Sensing Image Classification.” IEEE Transactions on Neural Networks and Learning Systems 1–13. https://doi.org/10.1109/TNNLS.2023.3286422.
  • Wang, J., L. Wei, M. Zhang, R. Tao, J. Chanussot, and A. Plaza. 2023. “Remote-Sensing Scene Classification via Multistage Self-Guided Separation Network.” IEEE Transactions on Geoscience & Remote Sensing 61:1–16. https://doi.org/10.1109/TGRS.2023.3335484.
  • Wei, L., S. Prasad, and J. E. Fowler. 2013. “Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields.” IEEE Geoscience and Remote Sensing Letters 11 (1): 153–157. https://doi.org/10.1109/LGRS.2013.2250905.
  • Wei, L., J. Wang, Y. Gao, M. Zhang, R. Tao, and B. Zhang. 2022. “Graph-Feature-Enhanced Selective Assignment Network for Hyperspectral and Multispectral Data Classification.” IEEE Transactions on Geoscience and Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3166252.
  • Weng, Z., X. Yang, L. Ang, W. Zuxuan, and Y.-G. Jiang. 2022. “Semi–Supervised Vision Transformers.” In17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 13690 LNCS:605–620. October 23, 2022–October 27, 2022. https://doi.org/10.1007/978-3-031-20056-4_35.
  • Wolf, T., L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, et al. 2020. “Transformers: State-Of-The-Art Natural Language Processing.” In 2020 System Demonstrations of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Virtual, Online, pp. 38–45. November 16, 2020–November 20, 2020.
  • Won, M., K. Choi, and X. Serra. 2021. “Semi–supervised music tagging Transformer.” In Proceedings of the 22nd ISMIR Conference, Online. November 7–12, 2021.
  • Xiang, H., T. Zhou, and Y. Peng. 2022. “Semisupervised Deep Learning Using Consistency Regularization and Pseudolabels for Hyperspectral Image Classification.” Journal of Applied Remote Sensing 16 (2): Art. No. 026513. https://doi.org/10.1117/1.JRS.16.026513.
  • Xiao, T., M. Singh, E. Mintun, T. Darrell, P. Dollar, and R. Dirshick. 2021. “Early Convolutions Help Transformers See Better.” In 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual, Online, 34:30392–30400. December 6, 2021–December 14, 2021.
  • Xiong, Y., X. Kele, Y. Dou, Y. Zhao, and Z. Gao. 2021. “WRMatch: Improving FixMatch with Weighted Nuclear–Norm Regularization for Few–Shot Remote Sensing Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 60:1–14. https://doi.org/10.1109/TGRS.2021.3121765.
  • Yang, X., W. Cao, L. Yao, and Y. Zhou. 2022. “Hyperspectral Image Transformer Classification Networks.” IEEE Transactions on Geoscience & Remote Sensing 60:1–15. https://doi.org/10.1109/TGRS.2022.3171551.
  • Yang, H., Y. Haoyang, D. Hong, X. Zhen, Y. Wang, and M. Song. 2022. “Hyperspectral Image Classification Based on Multi-Level Spectral-Spatial Transformer Network.” In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Rome, Italy. pp. 1–4. September 13–16, 2022.
  • Yang, A., L. Min, Y. Ding, D. Hong, L. Yilong, and H. Yujie. 2023. “GTFN: GCN and Transformer Fusion Network with Spatial-Spectral Features for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 61:1–15. https://doi.org/10.1109/TGRS.2023.3314616.
  • Yang, X., Z. Song, I. King, and X. Zenglin 2021. “A Survey on Deep Semi–Supervised Learning.” ArXiv: 2103.00550.
  • Zhang, Z., P. Cui, and W. Zhu. 2022. “Deep Learning on Graphs: A Survey.” IEEE Transactions on Knowledge and Data Engineering 34 (1): 249–270. https://doi.org/10.1109/TKDE.2020.2981333.
  • Zhang, B., Y. Wang, W. Hou, W. Hao, J. Wang, M. Okumura, and T. Shinozaki. 2021. “Flexmatch: Boosting Semi–Supervised Learning with Curriculum Pseudo Labeling.” In 35th Conference on Neural Information Processing Systems, NeurIPS 2021, Virtual, Online, 34:18408–18419. December 6, 2021–December 14, 2021.
  • Zhengzhong, T., H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and L. Yinxiao. 2022. “Maxvit: Multi-axis vision transformer.” In 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 13684:459–479. Springer. October 23, 2022–October 27, 2022.
  • Zhenyu, L., S. Liang, Q. Yang, and B. Du. 2022. “Evolving Block–Based Convolutional Neural Network for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 60:1–21. https://doi.org/10.1109/TGRS.2022.3160513.
  • Zhong, Z., L. Jonathan, Z. Luo, and M. Chapman. 2017. “SpectralSpatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework.” IEEE Transactions on Geoscience and Remote Sensing 56 (2): 847–858. https://doi.org/10.1109/TGRS.2017.2755542.
  • Zhong, Z., L. Ying, M. Lingfei, L. Jonathan, W.-S. Zheng, T. Jiang, and S. Wu. 2021. “Spectral–Spatial Transformer Network for Hyperspectral Image Classification: A Factorized Architecture Search Framework.” IEEE Transactions on Geoscience and Remote Sensing 60:1–15. https://doi.org/10.1109/TGRS.2022.3225267.
  • Zu, B., L. Yafang, L. Jianqiang, H. Ziping, H. Wang, and W. Panpan. 2023a. “Cascaded Convolution-Based Transformer with Densely Connected Mechanism for SpectralSpatial Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 61:1–19. https://doi.org/10.1109/TGRS.2023.3275871.
  • Ziping, H., K. Xia, P. Ghamisi, H. Yuhen, S. Fan, and Z. Baokai. 2022. “HyperVitgan: Semisupervised Generative Adversarial Network with Transformer for Hyperspectral Image Classification.” Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:6053–6068. https://doi.org/10.1109/JSTARS.2022.3192127.
  • Zu, B., H. Wang, L. Jianqiang, H. Ziping, L. Yafang, and Z. Yin. 2023b. “Weighted Residual Self-Attention Graph-Based Transformer for Spectral–Spatial Hyperspectral Image Classification.” International Journal of Remote Sensing 44 (3): 852–877. https://doi.org/10.1080/01431161.2023.2171744.

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