Hyperspectral image classification based on adaptive spectral feature decoupling with global local feature fusion network
Author:
Funder
the National Natural Science Foundation of China
the Key Technologies R&D Program of Henan Province of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12145-024-01415-2.pdf
Reference43 articles.
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2. Lu B, Dao PD, Liu J, He Y, Shang J (2020) Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens 12(16). https://doi.org/10.3390/rs12162659
3. Zhao J, Kechasov D, Rewald B, Bodner G, Verheul M, Clarke N, Clarke JL (2020) Deep learning in hyperspectral image reconstruction from single rgb images-a case study on tomato quality parameters. Remote Sens 12(19). https://doi.org/10.3390/rs12193258
4. Xue Q, Qi M, Li Z, Yang B, Li W, Wang F, Li Q (2021) Fluorescence hyperspectral imaging system for analysis and visualization of oil sample composition and thickness. Appl Opt 60(27):8349–8359. https://doi.org/10.1364/AO.432851
5. Luo B, Yang C, Chanussot J, Zhang L (2013) Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery. IEEE Trans Geosci Remote Sens 51(1):162–173. https://doi.org/10.1109/TGRS.2012.2198826
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