Hybrid 2D–3D convolution and pre-activated residual networks for hyperspectral image classification
Author:
Funder
Huzhou Municipal Science and Technology Bureau
Department of Education of Zhejiang Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03044-0.pdf
Reference24 articles.
1. Zhao, C., Wang, M., Feng, S.: A sparse and spectral smooth regularized low-rank tensor decomposition method for hyperspectral target detection. Int. J. Remote Sens. 43(12), 4608–4629 (2022)
2. Gao, H., Wang, M., Sun, X., Cao, X., et al.: Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space. Comput. Methods Progr. Biomed. 240, 107724 (2023)
3. Liu, G., Wang, L., Liu, D.: Hyperspectral image classification based on a least square bias constraint additional empirical risk minimization nonparallel support vector machine. Remote Sens. 14(17), 4263 (2022)
4. Wang, H., Celik, T.: Sparse representation-based hyperspectral image classification. Sign. Image Video Process. 12(5), 1009–1017 (2018)
5. Tan, X., Xue, Z., Yu, X., Sun, Y., et al.: Hyperspectral image classification with deep 3D capsule network and Markov random field. IET Image Process. 16(1), 79–91 (2022)
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