Spectral Spatial Neighborhood Attention Transformer for Hyperspectral Image Classification

References

• Ahmad, M., Khan, A.M., Mazzara, M., Distefano, S., Ali, M., and Sarfraz, M.S. 2022. “A fast and compact 3-D CNN for hyperspectral image classification.” IEEE Geoscience and Remote Sensing Letters, Vol. 19: pp. 1–19. doi:10.1109/LGRS.2020.3043710.
   Web of Science ®Google Scholar
• Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., and Zhou, Y. 2021. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
   Google Scholar
• Ouyang, E., Li, B., Hu, W., Zhang, G., Zhao, L., and Wu, J. 2023. “When multigranularity meets spatial–spectral attention: A hybrid transformer for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 61: pp. 1–18. doi:10.1109/TGRS.2023.3242978.
   Google Scholar
• Gao, K., Guo, W., Yu, X., Liu, B., Yu, A., and Wei, X. 2020. “Deep induction network for small samples classification of hyperspectral images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13: pp. 3462–3477. doi:10.1109/JSTARS.2020.3002787.
   Web of Science ®Google Scholar
• Guo, A.J., and Zhu, F. 2019. “A CNN-based spatial feature fusion algorithm for hyperspectral imagery classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 9):pp. 7170–7181. doi:10.1109/TGRS.2019.2911993.
   Web of Science ®Google Scholar
• Hassani, A., Walton, S., Li, J., Li, S., and Shi, H. 2023. Neighborhood attention transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6185–6194. arXiv preprint arXiv:2204.07143
   Google Scholar
• Haut, J., Paoletti, M., Paz-Gallardo, A., Plaza, J., Plaza, A., and Vigo-Aguiar, J. 2017. “Cloud implementation of logistic regression for hyperspectral image classification.” In Proc. 17th Int. Conf. Comput. Math. Methods Sci. Eng, Vol. 3: pp. 1063–2321. doi:10.1109/JMASS.2020.3019669.
   Google Scholar
• He, J., Zhao, L., Yang, H., Zhang, M., and Li, W. 2020. “HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 58(No. 1): pp. 165–178. doi:10.1109/TGRS.2019.2934760.
   Web of Science ®Google Scholar
• Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., and Chanussot, J. 2013. “Hyperspectral remote sensing data analysis and future challenges.” IEEE Geoscience and Remote Sensing Magazine, Vol. 1(No. 2): pp. 6–36. doi:10.1109/MGRS.2013.2244672.
   Web of Science ®Google Scholar
• van der Maaten, L., and Hinton, G. 2008. “Visualizing data using t-SNE.” The Journal of Machine Learning Research, Vol. 9 pp. 2579–2605.
   Web of Science ®Google Scholar
• Lee, H., and Kwon, H. 2017. “Going deeper with contextual CNN for hyperspectral image classification.” IEEE Transactions on Image Processing, Vol. 26(No. 10): pp. 4843–4855. doi:10.1109/TIP.2017.2725580.
   PubMed Web of Science ®Google Scholar
• Liu, P., Zhang, H., and Eom, K.B. 2017. “Active deep learning for classification of hyperspectral images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10(No. 2): pp. 712–724. doi:10.1109/JSTARS.2016.2598859.
   Web of Science ®Google Scholar
• Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., and Guo, B. 2021. “Swin transformer: Hierarchical vision transformer using shifted windows.” In Proceedings of the IEEE/CVF international conference on computer vision, pp.10012–10022.
   Google Scholar
• Özdemir, A.O.B., Gedik, B.E., and Çetin, C.Y.Y. 2014. “Hyperspectral classification using stacked autoencoders with deep learning.” 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4. doi:10.1109/WHISPERS.2014.8077532.
   Google Scholar
• Sahin, Y.E., Arisoy, S., and Kayabol, K. 2018. “Anomaly detection with Bayesian Gauss background model in hyperspectral images.” 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. doi:10.1109/SIU.2018.8404293.
   Google Scholar
• Sun, L., Zhao, G., Zheng, Y. and Wu, Z., 2022. Spectral–spatial feature tokenization transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–14.
   Google Scholar
• Tan, X., Gao, K., Liu, B., Fu, Y., and Kang, L. 2021. “Deep global-local transformer network combined with extended morphological profiles for hyperspectral image classification.” Journal of Applied Remote Sensing, Vol. 15(No. 03): pp. 038509. doi:10.1117/1.JRS.15.038509.
   Google Scholar
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., and Polosukhin, I. 2017. “Attention is all you need.” Advances in Neural Information Processing Systems, Vol. 30: pp. 30.
   Google Scholar
• Villa, A., Benediktsson, J.A., Chanussot, J., and Jutten, C. 2011. “Hyperspectral image classification with independent component discriminant analysis.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 49(No. 12):pp. 4865–4876. doi:10.1109/TGRS.2011.2153861.
   Web of Science ®Google Scholar
• Wang, G., Zheng, X., Cheng, L., Wan, X., and Guo, Z. 2021. “Hyperspectral image classification based on improved few shot learning.” In 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI), pp. 673–676. doi:10.1109/ICETCI53161.2021.9563257.
   Google Scholar
• Zhang, W. J., Yang, G., Lin, Y., Ji, C., and Gupta, M. M. 2018. "On Definition of Deep Learning," 2018 World Automation Congress (WAC), Stevenson, WA, USA, 2018, pp. 1–5, doi:10.23919/WAC.2018.8430387.
   Google Scholar
• Yang, X., Cao, W., Lu, Y., and Zhou, Y. 2022. “Hyperspectral Image Transformer Classification Networks.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–15. doi:10.1109/TGRS.2022.3171551.
   Web of Science ®Google Scholar
• Zhang, X., et al. 2022. “Spectral–spatial self-attention networks for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–15. doi:10.1109/TGRS.2021.3102143.
   Google Scholar
• Yang, J., Zhao, Y., Chan, J. C. W., and Yi, C. 2016. “Hyperspectral image classification using two-channel deep convolutional neural network.” In 2016 IEEE international geoscience and remote sensing symposium (IGARSS), pp. 5079–5082. doi:10.1109/IGARSS.2016.7730324.
   Google Scholar
• Ye, Q., Huang, P., Zhang, Z., Zheng, Y., Fu, L., and Yang, W. 2022. “Multiview learning with robust double-sided twin SVM.” IEEE Transactions on Cybernetics, Vol. 52(No. 12): pp. 12745–12758. doi:10.1109/TCYB.2021.3088519.
   PubMed Web of Science ®Google Scholar
• Ye, Q., Yang, J., Liu, F., Zhao, C., Ye, N., and Yin, T. 2018. “L1-norm distance linear discriminant analysis based on an effective iterative algorithm.” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 28(No. 1): pp. 114–129. doi:10.1109/TCSVT.2016.2596158.
   Web of Science ®Google Scholar
• Yu, C., Han, R., Song, M., Liu, C., and Chang, C.I. 2020. “A simplified 2D-3D CNN architecture for hyperspectral image classification based on spatial–spectral fusion.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13: pp. 2485–2501. doi:10.1109/JSTARS.2020.2983224.
   Web of Science ®Google Scholar
• Yu, H., Xu, Z., Zheng, K., Hong, D., Yang, H., and Song, M. 2022. “MSTNet: A multilevel spectral–spatial transformer network for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–13. doi:10.1109/TGRS.2022.3186400.
   Google Scholar
• Zhong, Y., Bi, T., Wang, J., Zeng, J., Huang, Y., Jiang, T., and Wu, S. 2022. “Spectral–spatial transformer network for hyperspectral image classification: A factorized architecture search framework.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–15. doi:10.1109/TGRS.2021.3115699.
   Web of Science ®Google Scholar
• Zhou, Y., Peng, J., and Chen, C.P. 2015. “Extreme learning machine with composite kernels for hyperspectral image classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8(No. 6): pp. 2351–2360. doi:10.1109/JSTARS.2014.2359965.
   Web of Science ®Google Scholar
• Zhang, Y., Liu, H., and Hu, Q. 2021. Transfuse: Fusing transformers and cnns for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24 (pp. 14–24). Springer International Publishing.
   Google Scholar
• Zhang, W.J., and Lin, Y. 2010. “On the principle of design of resilient systems –application to enterprise information systems.” Enterprise Information Systems, Vol. 4(No. 2):pp. 99–110. doi:10.1080/17517571003763380.
   Web of Science ®Google Scholar
• Zhang, J., Meng, Z., Zhao, F., Liu, H., and Chang, Z. 2022. “Convolution transformer mixer for hyperspectral image classification.” IEEE Geoscience and Remote Sensing Letters, Vol. 19: pp. 1–5. doi:10.1109/LGRS.2022.3208935.
   Web of Science ®Google Scholar