Semi-supervised multiview feature selection with label learning for VHR remote sensing images

Xi Chen; Wei Liu; Fulin Su; Guofan Shao

doi:10.1109/IGARSS.2016.7729612

Semi-supervised multiview feature selection with label learning for VHR remote sensing images

Xi Chen
, Wei Liu
, Fulin Su
, Guofan Shao

School of Electronics and Information Engineering, Harbin Institute of Technology
Purdue University

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

6 Scopus citations

Abstract

The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and meaningful groups. Each feature group represents a view, and each view describes a data characteristic. Then features are evaluated and selected within each view. The experiments on a Worldview-2 VHR satellite image verify the effectiveness and practicability of the method, compared with traditional single-view algorithms.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2372-2375
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7729612
State	Published - 1 Nov 2016
Externally published	Yes
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Keywords

Semisupervised multiview feature selection
l-norm
view generation

Access to Document

10.1109/IGARSS.2016.7729612

Cite this

Chen, X., Liu, W., Su, F., & Shao, G. (2016). Semi-supervised multiview feature selection with label learning for VHR remote sensing images. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 2372-2375). Article 7729612 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729612

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title = "Semi-supervised multiview feature selection with label learning for VHR remote sensing images",

abstract = "The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and meaningful groups. Each feature group represents a view, and each view describes a data characteristic. Then features are evaluated and selected within each view. The experiments on a Worldview-2 VHR satellite image verify the effectiveness and practicability of the method, compared with traditional single-view algorithms.",

keywords = "Semisupervised multiview feature selection, l-norm, view generation",

author = "Xi Chen and Wei Liu and Fulin Su and Guofan Shao",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

year = "2016",

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doi = "10.1109/IGARSS.2016.7729612",

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

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Chen, X, Liu, W, Su, F & Shao, G 2016, Semi-supervised multiview feature selection with label learning for VHR remote sensing images. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7729612, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 2372-2375, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729612

Semi-supervised multiview feature selection with label learning for VHR remote sensing images. / Chen, Xi; Liu, Wei; Su, Fulin et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2372-2375 7729612 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

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

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N2 - The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and meaningful groups. Each feature group represents a view, and each view describes a data characteristic. Then features are evaluated and selected within each view. The experiments on a Worldview-2 VHR satellite image verify the effectiveness and practicability of the method, compared with traditional single-view algorithms.

AB - The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and meaningful groups. Each feature group represents a view, and each view describes a data characteristic. Then features are evaluated and selected within each view. The experiments on a Worldview-2 VHR satellite image verify the effectiveness and practicability of the method, compared with traditional single-view algorithms.

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KW - l-norm

KW - view generation

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Chen X, Liu W, Su F, Shao G. Semi-supervised multiview feature selection with label learning for VHR remote sensing images. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2372-2375. 7729612. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7729612

Semi-supervised multiview feature selection with label learning for VHR remote sensing images

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