Optimizing Hyperspectral Image Classification Through Swin Transformer Integration and CNN Feature Extraction
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69986-3_29
Reference20 articles.
1. Ayas, S., Tunc-Gormus, E.: SpectralSWIN: a spectral-swin transformer network forhyperspectral image classification. Int. J. Remote Sens. 43(11), 4025–4044 (2022). https://doi.org/10.1080/01431161.2022.2105668
2. Ding, L., Zhang, J., Bruzzone, L.: Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture. IEEE Trans. Geosci. Remote Sens. 58(8), 5367–5376 (2020). https://doi.org/10.1109/TGRS.2020.2964675
3. Farooque, G., Liu, Q., Sargano, A.B., Xiao, L.: Swin transformer with multiscale 3D atrous convolution for hyperspectral image classification. Eng. Appl. Artif. Intell. 126(PC), 107070 (2023). https://doi.org/10.1016/j.engappai.2023.107070
4. Gao, J., Gao, X., Wu, N., Yang, H.: Bi-directional LSTM with multi-scale dense attention mechanism for hyperspectral image classification. Multimedia Tools Appl. 81(17), 24003–24020 (2022). https://doi.org/10.1007/s11042-022-12809-z
5. He, X., Zhou, Y., Zhao, J., Zhang, D., Yao, R., Xue, Y.: Swin transformer embedding U-Net for remote sensing image semantic segmentation. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022). https://doi.org/10.1109/TGRS.2022.3144165
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