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

Publisher's version (útgefin grein) 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...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Zhao, Bin, Ulfarsson, Magnus, Sveinsson, Jóhannes Rúnar, Chanussot, Jocelyn
Other Authors: Rafmagns- og tölvuverkfræðideild (HÍ), Faculty of Electrical and Computer Engineering (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
Classification
Deep mixture of factor analyzers
Dimensionality reduction
Feature extraction
Hyperspectral image
Mixtures of factor analyzers
Supervised mixtures of factor analyzers
Ljósfræði
Myndgreining (upplýsingatækni)
Iceland
Online Access:https://hdl.handle.net/20.500.11815/2386
https://doi.org/10.3390/rs12071179
Description
Summary:Publisher's version (útgefin grein) 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 representation of the Gaussians. DMFA is a deep version of MFA and consists of a two-layer MFA, i.e, samples from the posterior distribution at the first layer are input to an MFA model at the second layer. SMFA consists of single-layer MFA and exploits labeled information to extract features of HSI effectively. Based on these three FE methods, the paper also proposes a framework that automatically extracts the most important features for classification from an HSI. The overall accuracy of a classifier is used to automatically choose the optimal number of features and hence performs dimensionality reduction (DR) before HSI classification. The performance of MFA, DMFA, and SMFA FE methods are evaluated and compared to five different types of unsupervised and supervised FE methods by using four real HSIs datasets. This research was supported in part by the Doctoral Grants of the University of Iceland Research Fund and the Icelandic Research Fund under Grant 174075-05. Peer Reviewed

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