Unsupervised and Supervised Feature Extraction Methods for Hyperspectral Images Based on Mixtures of Factor Analyzers

Unsupervised and Supervised Feature Extraction Methods for Hyperspectral Images Based on Mixtures of Factor Analyzers

This paper proposes three feature extraction (FE) methods based on density estimation for hyperspectral images (HSIs). The methods are a mixture of factor analyzers (MFA), deep MFA (DMFA), and supervised MFA (SMFA). The MFA extends the Gaussian mixture model to allow a low-dimensionality representat...

Full description

Bibliographic Details
Main Authors: Bin Zhao, Magnus O. Ulfarsson, Johannes R. Sveinsson, Jocelyn Chanussot
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
dimensionality reduction
feature extraction
hyperspectral image
mixtures of factor analyzers
supervised mixtures of factor analyzers
deep mixture of factor analyzers
Online Access:https://www.mdpi.com/2072-4292/12/7/1179

Internet

https://www.mdpi.com/2072-4292/12/7/1179

Similar Items