(Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification

Bin Zhao; Johannes R. Sveinsson; Magnus O. Ulfarsson; Jocelyn Chanussot

doi:10.1109/IGARSS.2019.8898932

(Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification

Bin Zhao, Johannes R. Sveinsson, Magnus O. Ulfarsson, Jocelyn Chanussot

Faculty of Electrical and Computer Engineering

University of Iceland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents four dimensionality reduction methods, supervised mixtures of factor analyzers (SMFA), semi-supervised mixtures of factor analyzers (S²MFA), supervised deep mixtures of factor analyzers (SDMFA) and semi-supervised deep mixtures of factor analyzers (S²DMFA), for hyperspectral image (HSI) classification. The performance of SMFA, S²MFA, SDMFA, and S²DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to more conventional methods like probabilistic principal component analysis, factor analysis, mixtures of factor analyzers and deep mixtures of factor analyzers and it is shown that the proposed methods give better results.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	887-890
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898932
Publication status	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Bibliographical note

Other keywords

(semi-) supervised deep mixture of factor analyz-ers
(semi-) supervised mixtures of factor analyzers
Dimensionality reduction
classification
factor analysis
hyperspectral image

Access to Document

10.1109/IGARSS.2019.8898932

Cite this

Zhao, B., Sveinsson, J. R., Ulfarsson, M. O., & Chanussot, J. (2019). (Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 887-890). Article 8898932 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8898932

Zhao, Bin ; Sveinsson, Johannes R. ; Ulfarsson, Magnus O. et al. / (Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification. 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 887-890 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{42678f29355e4d1bb034b8e61ad3ef25,

title = "(Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification",

abstract = "This paper presents four dimensionality reduction methods, supervised mixtures of factor analyzers (SMFA), semi-supervised mixtures of factor analyzers (S2MFA), supervised deep mixtures of factor analyzers (SDMFA) and semi-supervised deep mixtures of factor analyzers (S2DMFA), for hyperspectral image (HSI) classification. The performance of SMFA, S2MFA, SDMFA, and S2DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to more conventional methods like probabilistic principal component analysis, factor analysis, mixtures of factor analyzers and deep mixtures of factor analyzers and it is shown that the proposed methods give better results.",

keywords = "(semi-) supervised deep mixture of factor analyz-ers, (semi-) supervised mixtures of factor analyzers, Dimensionality reduction, classification, factor analysis, hyperspectral image",

author = "Bin Zhao and Sveinsson, \{Johannes R.\} and Ulfarsson, \{Magnus O.\} and Jocelyn Chanussot",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

month = jul,

doi = "10.1109/IGARSS.2019.8898932",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "887--890",

booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",

address = "United States",

}

Zhao, B, Sveinsson, JR, Ulfarsson, MO & Chanussot, J 2019, (Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8898932, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 887-890, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8898932

(Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification. / Zhao, Bin; Sveinsson, Johannes R.; Ulfarsson, Magnus O. et al.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 887-890 8898932 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - (Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification

AU - Zhao, Bin

AU - Sveinsson, Johannes R.

AU - Ulfarsson, Magnus O.

AU - Chanussot, Jocelyn

PY - 2019/7

Y1 - 2019/7

N2 - This paper presents four dimensionality reduction methods, supervised mixtures of factor analyzers (SMFA), semi-supervised mixtures of factor analyzers (S2MFA), supervised deep mixtures of factor analyzers (SDMFA) and semi-supervised deep mixtures of factor analyzers (S2DMFA), for hyperspectral image (HSI) classification. The performance of SMFA, S2MFA, SDMFA, and S2DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to more conventional methods like probabilistic principal component analysis, factor analysis, mixtures of factor analyzers and deep mixtures of factor analyzers and it is shown that the proposed methods give better results.

AB - This paper presents four dimensionality reduction methods, supervised mixtures of factor analyzers (SMFA), semi-supervised mixtures of factor analyzers (S2MFA), supervised deep mixtures of factor analyzers (SDMFA) and semi-supervised deep mixtures of factor analyzers (S2DMFA), for hyperspectral image (HSI) classification. The performance of SMFA, S2MFA, SDMFA, and S2DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to more conventional methods like probabilistic principal component analysis, factor analysis, mixtures of factor analyzers and deep mixtures of factor analyzers and it is shown that the proposed methods give better results.

KW - (semi-) supervised deep mixture of factor analyz-ers

KW - (semi-) supervised mixtures of factor analyzers

KW - Dimensionality reduction

KW - classification

KW - factor analysis

KW - hyperspectral image

UR - https://www.scopus.com/pages/publications/85077685361

U2 - 10.1109/IGARSS.2019.8898932

DO - 10.1109/IGARSS.2019.8898932

M3 - Conference contribution

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 887

EP - 890

BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

Zhao B, Sveinsson JR, Ulfarsson MO, Chanussot J. (Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 887-890. 8898932. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8898932

(Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms for Hyperspectral Images Classification

Abstract

Publication series

Conference

Bibliographical note

Other keywords

Access to Document

Fingerprint

Cite this