Representation of BVMD features via multitask compressive sensing for SAR target classification

被引：10

作者：

Chen, Lin ^{[1
]}

Zhan, Peng ^{[1
]}

Li, Teng ^{[1
]}

Li, Xueqing ^{[1
]}

机构：

[1] Shandong Univ, Sch Software, Jinan 250100, Peoples R China

来源：

REMOTE SENSING LETTERS | 2020年 / 11卷 / 08期

关键词：

APERTURE RADAR IMAGES; RECOGNITION; REGION;

D O I：

10.1080/2150704X.2020.1773564

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

This letter develops a synthetic aperture radar (SAR) target classification method based on bidimensional variational mode decomposition (BVMD) and multitask compressive sensing (MTCS). BVMD is employed to decompose SAR images to exploit the time-frequency properties of the described targets. The MTCS is used to jointly classify the original SAR image and its BVMD components. So, the merits of BVMD and MTCS can be combined in the proposed method. Finally, based on the reconstruction errors, the target label can be decided. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used to set up experimental conditions to test the proposed method. By comparison with several reference methods from published works, the effectiveness and robustness of the proposed method can be confirmed.

引用

页码：807 / 816

页数：10

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