Hyperspectral image classification based on clustering dimensionality reduction and multi-scale feature fusion-Reference-Cited by-同舟云学术

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Hyperspectral image classification based on clustering dimensionality reduction and multi-scale feature fusion

Published:2022-09-27 Issue:6 Volume:33 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Wang Cailing^ORCID,Song Xiaonan,Zhang Jing

Funder

National Natural Science Foundation of China

Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences and Peking Union Medical College

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-022-01340-8.pdf

Reference42 articles.

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2. Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N.M., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. (2013). https://doi.org/10.1109/MGRS.2013.2244672

3. Zhang, X., Sun, Y., Shang, K., Zhang, L., Wang, S.: Crop classification based on feature band set construction and object-oriented approach using hyperspectral images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4117–4128 (2016). https://doi.org/10.1109/JSTARS.2016.2577339

4. Zhang, L., Zhang, L., Tao, D., Huang, X., Du, B.: Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Trans. Geosci. Remote Sens. 52(8), 4955–4965 (2014). https://doi.org/10.1109/TGRS.2013.2286195

5. Hörig, B., Kühn, F., Oschütz, F., Lehmann, F.: HyMap hyperspectral remote sensing to detect hydrocarbons. Int. J. Remote Sens. 22(8), 1413–1422 (2001). https://doi.org/10.1080/01431160120909

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