Hyperspectral Image Classification for Vegetation Detection Using Lightweight Cascaded Deep Convolutional Neural Network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s12524-023-01754-5.pdf
Reference18 articles.
1. Bai, J., Ding, B., Xiao, Z., Jiao, L., Chen, H., & Regan, A. C. (2022). HIC based on deep attention graph convolutional network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16. https://doi.org/10.1109/TGRS.2021.3066485
2. Chang, Y.-L., Tan, T.-H., Lee, W.-H., Chang, L., Chen, Y.-N., Fan, K.-C., & Alkhaleefah, M. (2022). Consolidated convolutional neural network for HIC. Remote Sensing, 14(7), 1571. https://doi.org/10.3390/rs14071571.
3. Chen, Y., Wang, Y., Gu, Y., He, X., Ghamisi, P., & Jia, X. (2019). Deep learning ensemble for HIC. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6), 1882–1897. https://doi.org/10.1109/JSTARS.2019.2915259.
4. Diakite, Alou, G., & Fu, X. (2021). HIC using 3D 2D CNN. IET Image Processing, 15(5), 1083–1092. https://doi.org/10.1049/ipr2.12087.
5. Gao, H., Chen, Z., & Li, C. (2021). Sandwich convolutional neural network for HIC using spectral feature enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3006–3015. https://doi.org/10.1109/JSTARS.2021.3062872.
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