Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification

Uddin, Md Palash, Mamun, Md Al, Afjal, Masud Ibn and Hossain, Md Ali 2021, Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification, International journal of remote sensing, vol. 42, no. 1, pp. 286-321, doi: 10.1080/01431161.2020.1807650.

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Title Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification
Author(s) Uddin, Md PalashORCID iD for Uddin, Md Palash orcid.org/0000-0003-1100-3584
Mamun, Md Al
Afjal, Masud Ibn
Hossain, Md Ali
Journal name International journal of remote sensing
Volume number 42
Issue number 1
Start page 286
End page 321
Total pages 36
Publisher Taylor & Francis
Place of publication Abingdon, Eng.
Publication date 2021
ISSN 0143-1161
1366-5901
Language eng
DOI 10.1080/01431161.2020.1807650
Indigenous content off
Field of Research 0406 Physical Geography and Environmental Geoscience
0909 Geomatic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30149456

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