Hyperspectral image classification using K-plane clustering and kernel principal component analysis-Reference-Cited by-同舟云学术

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Hyperspectral image classification using K-plane clustering and kernel principal component analysis

Published:2023-05-10 Issue:30 Volume:82 Page:47387-47403
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Mirzaei Sayeh^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15437-3.pdf

Reference35 articles.

1. Binol, H (2018) Ensemble learning based multiple kernel principal component analysis for dimensionality reduction and classification of hyperspectral imagery. Math Probl Eng https://doi.org/10.1155/2018/9632569

2. Bioucas-Dias, JM, Plaza, A, Dobigeon, N, Parente, M, Du, Q, Gader, P, Chanussot, J (2012) Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J Select Top Appl Earth Observ Remote Sens https://doi.org/10.1109/JSTARS.2012.2194696

3. Bradley, PS, Mangasarian, OL (2000) K-Plane Clustering. J Glob Optim https://doi.org/10.1023/A:1008324625522

4. Cai, W, Chen, S, Zhang, D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn https://doi.org/10.1016/j.patcog.2006.07.011

5. Camps-Valls, G, Bruzzone, L (2005) Kernel-based methods for hyperspectral image classification. IEEE Trans Geosci Remote Sens https://doi.org/10.1109/TGRS.2005.846154

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1. Robust local K-proximal plane clustering based on L2,1-norm minimization;International Journal of Machine Learning and Cybernetics;2024-06-11

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