Tensor based dimension reduction for polarimetric SAR data

Mingliang Tao; Feng Zhou; Zijing Zhang

doi:10.1109/IGARSS.2014.6947058

Tensor based dimension reduction for polarimetric SAR data

Mingliang Tao, Feng Zhou, Zijing Zhang

Xidian University

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

4 Scopus citations

Abstract

With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2802-2805
Number of pages	4
ISBN (Electronic)	9781479957750
DOIs	https://doi.org/10.1109/IGARSS.2014.6947058
State	Published - 4 Nov 2014
Externally published	Yes
Event	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada Duration: 13 Jul 2014 → 18 Jul 2014

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/Territory	Canada
City	Quebec City
Period	13/07/14 → 18/07/14

Keywords

dimension reduction
independent component analysis
radar polarimetry
synthetic aperture radar
tensor decomposition

Access to Document

10.1109/IGARSS.2014.6947058

Cite this

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title = "Tensor based dimension reduction for polarimetric SAR data",

abstract = "With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.",

keywords = "dimension reduction, independent component analysis, radar polarimetry, synthetic aperture radar, tensor decomposition",

author = "Mingliang Tao and Feng Zhou and Zijing Zhang",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",

year = "2014",

month = nov,

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doi = "10.1109/IGARSS.2014.6947058",

language = "英语",

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

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

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Tao, M, Zhou, F & Zhang, Z 2014, Tensor based dimension reduction for polarimetric SAR data. in International Geoscience and Remote Sensing Symposium (IGARSS)., 6947058, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 2802-2805, Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, Canada, 13/07/14. https://doi.org/10.1109/IGARSS.2014.6947058

Tensor based dimension reduction for polarimetric SAR data. / Tao, Mingliang; Zhou, Feng; Zhang, Zijing.
International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc., 2014. p. 2802-2805 6947058 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

TY - GEN

T1 - Tensor based dimension reduction for polarimetric SAR data

AU - Tao, Mingliang

AU - Zhou, Feng

AU - Zhang, Zijing

PY - 2014/11/4

Y1 - 2014/11/4

N2 - With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.

AB - With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.

KW - dimension reduction

KW - independent component analysis

KW - radar polarimetry

KW - synthetic aperture radar

KW - tensor decomposition

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U2 - 10.1109/IGARSS.2014.6947058

DO - 10.1109/IGARSS.2014.6947058

M3 - 会议稿件

AN - SCOPUS:84911457692

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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EP - 2805

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014

Y2 - 13 July 2014 through 18 July 2014

ER -

Tensor based dimension reduction for polarimetric SAR data

Abstract

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Conference

Keywords

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