SAR IMAGE RECONSTRUCTION AND TARGET EXTRACTION WITH UNDER-SAMPLED DATA VIA LOW-RANK AND SPARSITY MATRIX DECOMPOSITION

Min Li; Weibo Huo; Zhongyu Li; Junjie Wu; Jianyu Yang

doi:10.1109/IGARSS47720.2021.9555130

SAR IMAGE RECONSTRUCTION AND TARGET EXTRACTION WITH UNDER-SAMPLED DATA VIA LOW-RANK AND SPARSITY MATRIX DECOMPOSITION

Min Li
, Weibo Huo
, Zhongyu Li
, Junjie Wu
, Jianyu Yang

University of Electronic Science and Technology of China

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.

Original language	English
Pages	4564-4567
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9555130
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

Keywords

SAR
low-rank and sparsity matrix decomposition
robust PCA
sparse reconstruction
target detection

Access to Document

10.1109/IGARSS47720.2021.9555130

Cite this

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title = "SAR IMAGE RECONSTRUCTION AND TARGET EXTRACTION WITH UNDER-SAMPLED DATA VIA LOW-RANK AND SPARSITY MATRIX DECOMPOSITION",

abstract = "Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.",

keywords = "SAR, low-rank and sparsity matrix decomposition, robust PCA, sparse reconstruction, target detection",

author = "Min Li and Weibo Huo and Zhongyu Li and Junjie Wu and Jianyu Yang",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9555130",

language = "英语",

pages = "4564--4567",

}

TY - CONF

T1 - SAR IMAGE RECONSTRUCTION AND TARGET EXTRACTION WITH UNDER-SAMPLED DATA VIA LOW-RANK AND SPARSITY MATRIX DECOMPOSITION

AU - Li, Min

AU - Huo, Weibo

AU - Li, Zhongyu

AU - Wu, Junjie

AU - Yang, Jianyu

PY - 2021

Y1 - 2021

N2 - Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.

AB - Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.

KW - SAR

KW - low-rank and sparsity matrix decomposition

KW - robust PCA

KW - sparse reconstruction

KW - target detection

UR - https://www.scopus.com/pages/publications/85129829749

U2 - 10.1109/IGARSS47720.2021.9555130

DO - 10.1109/IGARSS47720.2021.9555130

M3 - 论文

AN - SCOPUS:85129829749

SP - 4564

EP - 4567

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

SAR IMAGE RECONSTRUCTION AND TARGET EXTRACTION WITH UNDER-SAMPLED DATA VIA LOW-RANK AND SPARSITY MATRIX DECOMPOSITION

Abstract

Conference

Keywords

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