Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 8; p. 790
• Main Authors: Luo, Fulin, Huang, Hong, Duan, Yule, Liu, Jiamin, Liao, Yinghua
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.08.2017
• Subjects: dimensionality reduction; hyperspectral imagery; local geometric structure; manifold learning; marginal Fisher analysis

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...