Virtual Reconstruction of Adaptive Spectral and Spatial Features Based on CNN for HSI Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27440-4_15
Reference19 articles.
1. Khader, A., Yang, J., Xiao, L.: NMF-DuNet: Nonnegative matrix factorization inspired deep unrolling networks for hyperspectral and multispectral image fusion. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing, pp. 1–17 (2022)
2. Hamouda, M., Ettabaa, K.S., Bouhlel, M.S.: Framework for automatic selection of kernels based on convolutional neural networks and ckmeans clustering algorithm. Int. J. Image Graph. 19(04), 1950019 (2019)
3. Wang, L., Wang, L., Wang, H., Wang, X., Bruzzone, L.: “SPCNet: A subpixel convolution-based change detection network for hyperspectral images with different spatial resolutions. IEEE Trans. Geosci. Remote Sensing 60 1–1 (2022)
4. Fu, H., et al.: A novel band selection and spatial noise reduction method for hyperspectral image classification. IEEE Trans. Geosci. Remote Sensing 60, 1–13 (2022)
5. Hamouda, M., Ettabaa, K.S., Bouhlel, M.S.: Smart feature extraction and classification of hyperspectral images based on convolutional neural networks. IET Image Proc. 14(10), 1999–2005 (2020)
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