Combining multi-mode representations and ResNet for SAR target recognition

被引：10
|
作者：
Shang, Shanshan [1 ]
Li, Guoping [2 ]
Wang, Guozhong [2 ]
机构：
[1] Shanghai Univ Engn Sci, Lib, Shanghai, Peoples R China
[2] Shanghai Univ Engn Sci, Inst Artificial Intelligence Ind, Shanghai 201620, Peoples R China
来源：
REMOTE SENSING LETTERS | 2021年 / 12卷 / 06期
基金：
国家重点研发计划;
关键词：
EMPIRICAL MODE DECOMPOSITION; SPARSE REPRESENTATION; CLASSIFICATION; IMAGES;
D O I：
10.1080/2150704X.2021.1910363
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
This letter combines the multi-mode representations extracted by bidimensional empirical mode decomposition (BEMD) and deep residual networks (ResNet) for synthetic aperture radar (SAR) target recognition. Bidimensional intrinsic mode functions (BIMFs) are generated by BEMD to describe the target characteristics in SAR images. A special ResNet is designed and trained for each layer of BIMFs. The decisions from different BIMFs are linearly fused using a random weight matrix. Typical test scenarios are designed on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to examine the proposed method. The results validate the validity and robustness of the method.
引用
收藏
页码：614 / 624
页数：11
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