A Novel Geometric Mean Feature Space Discriminant Analysis Method for Hyperspectral Image Feature Extraction-Reference-Cited by-同舟云学术

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A Novel Geometric Mean Feature Space Discriminant Analysis Method for Hyperspectral Image Feature Extraction

Published:2019-08-13 Issue:1 Volume:51 Page:515-542
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Li Li,Ge Hongwei,Gao Jianqiang,Zhang Yixin,Tong Yubing,Sun Jun

Funder

the Graduate Innovation Foundation of Jiangsu Province

the 111 Project and PAPD of Jiangsu Higher Education Institutions, China

a Project of Shandong Province Higher Educational Science and Technology Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

http://link.springer.com/content/pdf/10.1007/s11063-019-10101-0.pdf

Reference47 articles.

1. David L (2002) Hyperspectral image data analysis as a high dimensional signal processing problem. IEEE Signal Process Mag 19(1):17–28

2. Hosseini SA, Ghassemian H (2016) Hyperspectral data feature extraction using rational function curve fitting. Int J Pattern Recognit Artif Intell 30(01):1650001. https://doi.org/10.1142/S0218001416500014

3. Hosseini SA, Ghassemian H (2016) Rational function approximation for feature reduction in hyperspectral data. Remote Sens Lett 7(2):101–110

4. Imani M, Ghassemian H (2017) High-dimensional image data feature extraction by double discriminant embedding. Pattern Anal Appl 20(2):473–484

5. Imani M, Ghassemian H (2016) Binary coding based feature extraction in remote sensing high dimensional data. Inf Sci 342:191–208

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4. Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism;2023-07-18

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