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国土资源遥感  2020, Vol. 32 Issue (2): 33-39    DOI: 10.6046/gtzyyg.2020.02.05
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于变分模态分解的SAR图像目标识别方法
周光宇1, 刘邦权1, 张亶2
1.宁波财经学院数字技术与工程学院,宁波 315175
2.浙江大学计算机科学与技术学院,杭州 310058
Target recognition in SAR images based on variational mode decomposition
Guangyu ZHOU1, Bangquan LIU1, Dan ZHANG2
1. College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
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摘要 

为提升合成孔径雷达(synthetic aperture Radar,SAR)目标识别性能,提出基于变分模态分解算法(variational mode decomposition,VMD)的SAR图像目标识别方法。首先采用二维变分模态分解算法(bidimensional VMD,BVMD)对SAR图像进行分解,从而获得多模态的表示; 然后采用联合稀疏表示对SAR图像的多模态特征进行同时表征; 最后基于最小重构误差的原则判定目标类别。在MSTAR数据集上对提出方法进行性能测试,结果显示,在标准操作条件(standard operating condition,SOC)下对10类目标的识别率达到99.24%,在型号差异、俯仰角差异、噪声干扰条件下的性能也优于现有几类方法,证实了方法的有效性。

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周光宇
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关键词 合成孔径雷达目标识别变分模态分解联合稀疏表示    
Abstract

In order to improve synthetic aperture Radar (SAR) target recognition performance, the authors propose a method based on variational mode decomposition (VMD). First, the bidimensional VMD (BVMD) is employed to decompose SAR images, thus obtaining multi-mode representations. Afterwards, the joint sparse representation is employed to represent the multiple modes. Finally, the target label is determined based on the minimum reconstruction error. The proposed method was tested on the MSTAR dataset. It could achieve a recognition rate of 99.24% on 10 classes of targets under the standard operating condition (SOC). In addition, its performance outperforms some other SAR target recognition methods under configuration variance, depression angle variance, and noise corruption. The results have confirmed the validity of the proposed method.

Key wordssynthetic aperture Radar    target recognition    variational mode decomposition    joint sparse representation
收稿日期: 2019-06-14      出版日期: 2020-06-18
ZTFLH:  TP753  
基金资助:浙江省自然科学项目“测地度量的快速估算及其应用”(LY19F020001)
作者简介: 周光宇(1963-),男,硕士,副教授,研究方向为嵌入式系统,图像处理,传感网络。Email: happyday_zh@163.com。
引用本文:   
周光宇, 刘邦权, 张亶. 基于变分模态分解的SAR图像目标识别方法[J]. 国土资源遥感, 2020, 32(2): 33-39.
Guangyu ZHOU, Bangquan LIU, Dan ZHANG. Target recognition in SAR images based on variational mode decomposition. Remote Sensing for Land & Resources, 2020, 32(2): 33-39.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.02.05      或      http://www.gtzyyg.com/CN/Y2020/V32/I2/33
Fig.1  基于变分模态分解的SAR目标识别方法流程
Fig.2  10类目标的光学图像
类别 训练集图像数量/幅 测试集图像数量/幅
BMP2 233 195
BTR70 233 196
T72 232 196
T62 299 273
BRDM2 298 274
BTR60 256 195
ZSU23/4 299 274
D7 299 274
ZIL131 299 274
2S1 299 274
Tab.1  SOC条件实验设置
类别 BMP2 BTR70 T72 T62 BDRM2 BTR60 ZSU23/4 D7 ZIL131 2S1
BMP2 0.985 0 0.005 0.010 0 0 0 0 0 0
BTR70 0 1.000 0 0 0 0 0 0 0 0
T72 0 0 0.985 0 0.005 0 0.005 0 0 0.005
T62 0 0.004 0 0.989 0 0 0 0 0 0.007
BDRM2 0 0 0 0 0.996 0 0 0 0.004 0
BTR60 0 0 0 0.010 0 0.990 0 0 0 0
ZSU23/4 0 0 0 0 0 0 0.993 0 0 0.007
D7 0.004 0.004 0 0 0.007 0 0 0.985 0 0
ZIL131 0 0 0 0 0 0 0 0 1.0 0
2S1 0.004 0 0 0 0 0 0 0 0.003 0.993
Tab.2  SOC条件下本文方法的混淆矩阵
方法类型 平均识别率/% 平均时间消耗/ms
本文方法 99.24 75.4
SVM 96.94 69.5
SRC 97.26 62.7
CNN 99.08 78.6
单演信号法 99.10 80.1
Tab.3  SOC条件下平均识别率和平均时间消耗
类别 训练集图像数量/幅 测试集图像数量/幅
BMP2 233(Sn_9563) 196(Sn_9566)
196(Sn_c21)
BTR70 233(Sn_c71) 196(Sn_c71)
T72 232(Sn_132) 195(Sn_812)
191(Sn_s7)
Tab.4  型号差异下实验设置
方法类型 平均识别率/%
本文方法 97.82
SVM 94.58
SRC 95.72
CNN 96.07
单演信号法 97.14
Tab.5  型号差异下平均识别率
类型 俯仰角 2S1 BDRM2 ZSU23/4
训练集图像
数量/幅
17° 299 298 299
测试集图像 30° 288 287 288
数量/幅 45° 303 303 303
Tab.6  俯仰角变化下实验设置
Fig.3  俯仰角变化条件下平均识别率
Fig.4  各类方法在噪声干扰下的性能
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