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Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery.
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2017
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2015
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2015
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2015
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2015
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2016
Cited alongside, same era.
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2016
Cited alongside, same era.
2017
Later among the works it cites.
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2018
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2018
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2018
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2018
Later among the works it cites.
H. Gao, S. Lin, Y. Yang, C. Li, and M. Yang, “Convolution Neural Network Based on Two-Dimensional Spectrum for Hyperspectral Image Classification,” 2018. [Online]. Available: https://www.hindawi.com/journals/js/2018/8602103/
2018
Later among the works it cites.
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2018
Later among the works it cites.
X. Yang, Y. Ye, X. Li, R. Y. K. Lau, X. Zhang, and X. Huang, “Hyperspectral Image Classification With Deep Learning Models,” IEEE Transactions on Geoscience and Remote Sensing , vol. 56, no. 9, pp. 5408–5423, Sep. 2018
2018
Later among the works it cites.
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2018
Later among the works it cites.
——, “Rotationally invariant time-frequency scattering transforms,” Journal of Fourier Analysis and Applications , To Appear, 2020
2020
Closest in time.
“Scattering transforms in python,” https://github.com/ilyakava/pyfst
2020
Closest in time.
J. Acquarelli, E. Marchiori, L. M. C. Buydens, T. Tran, and T. van Laarhoven, “Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting,” Remote Sensing , vol. 10, no. 7, p. 1156, Jul. 2018. [Online]. Available: https://www.mdpi.com/2072-4292/10/7/1156
2072
Closest in time.
H. Liang and Q. Li, “Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features,” Remote Sensing , vol. 8, no. 2, p. 99, Jan. 2016. [Online]. Available: https://www.mdpi.com/2072-4292/8/2/99
2072
Closest in time.