Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent th...

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Bibliographic Details
Main Authors: Fulin Luo, Hong Huang, Yule Duan, Jiamin Liu, Yinghua Liao
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Remote Sensing
Subjects:
hyperspectral imagery
dimensionality reduction
manifold learning
local geometric structure
marginal Fisher analysis
Online Access:https://www.mdpi.com/2072-4292/9/8/790

Internet

https://www.mdpi.com/2072-4292/9/8/790

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