Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09275-5.pdf
Reference47 articles.
1. Shi Z, Liu L, Zhai X, Jiang Z (2013) Efficient sparse unmixing analysis for hyperspectral imagery based on random projection. Neural Comput Appl 23(7–8):2281–2293. https://doi.org/10.1007/s00521-012-1179-8
2. Zhao J, Huang T, Zhou Z (2020) Hyperspectral image super-resolution using recursive densely convolutional neural network with spatial constraint strategy. Neural Comput Appl 32(18):14471–14481. https://doi.org/10.1007/s00521-019-04484-3
3. Krishna SL, Jeya IJS, Deepa SN (2022) Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification. Neural Comput Appl 34(21):19343–19376. https://doi.org/10.1007/s00521-022-07517-6
4. Gao H, Yang Y, Li C, Zhang X, Zhao J, Yao D (2019) Convolutional neural network for spectral-spatial classification of hyperspectral images. Neural Comput Appl 31(12):8997–9012. https://doi.org/10.1007/s00521-019-04371-x
5. Bandyopadhyay M (2021) Multi-stack hybrid CNN with non-monotonic activation functions for hyperspectral satellite image classification. Neural Comput Appl 33(21):14809–14822. https://doi.org/10.1007/s00521-021-06120-5
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