基于小波散射激励与SimAM的轻量化抗噪卷积神经网络的目标识别

doi:10.12305/j.issn.1001-506X.2026.07.06

Abstract

Abstract:

Aiming at the problems of large deployment resources consumption and strong speclcle noise of synthetic aperture radar image recognition network, a lightweight noise-resistant convolutional neural network （CNN） based on wavelet scattering excitation and simple attention module （WSS） for radar target recognition method is proposed. Firstly, the noise interference is reduced by using the WSS module to allow the model to learn autonomously from the noisy image so that the model gradually focuses on the scattering center of the aircraft. Secondly, the simple attention module （SimAM） is utilized for further noise immunity and the feature maps are dynamically computed for each position. Finally, the full connectivity layer is utilized for the classification output, which has high recognition accuracy performance while achieving a significant reduction in the number of parameters. The experimental results under different noise conditions show that compared with the existing network models, WSS-CNN achieves higher accuracy in the SAR image recognition task, and it has superior performance in complex noise environments.

Key words: synthetic aperture radar （SAR）, convolutional neural network （CNN）, speckle noise, wavelet scattering excitation, simple attention module （SimAM）

CLC Number:

TN 95

Chang LIU, Lingyu WANG, Lang XIA, Yuefeng LI, Xin LIN, Yanyang LIU, Penghui HUANG. Target recognition of lightweight noise-resistant CNN based on wavelet scattering excitation and SimAM[J]. Systems Engineering and Electronics, 2026, 48(7): 2184-2194.

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类别	训练集	测试集	总计
A220	278	31	309
A320/321	1 719	52	1 771
A330	3 270	460	3 730
ARJ21	825	362	1 187
Boeing737	2 007	550	2 557
Boeing787	2 191	454	2 645
Other	3 223	1 041	4 264

名称	参数
批量大小	30
优化器	Adam
初始学习率	0.0001
训练次数	100
权重衰减	0.0001

WSS模块	SimAM模块	准确率
×	×	0.8366
×	√	0.8576
√	×	0.8634
√	√	0.8701

模型	平均识别率	A220	A320/321	A330	ARJ21	Boeing737	Boeing787	Other
LCA-CNN	0.756 6	0.73	0.96	0.45	0.63	0.72	0.79	0.81
MFCNN	0.845 8	0.83	1.00	1.00	0.76	0.78	0.85	0.90
ResNet-101	0.830 8	0.84	0.98	1.00	0.88	0.81	0.81	0.82
ShuffleNetV2	0.835 9	0.80	1.00	1.00	0.82	0.80	0.81	0.87
Wavelet-SRNet	0.824 7	0.85	1.00	1.00	0.76	0.76	0.76	0.88
WSS-CNN	0.870 1	0.85	1.00	1.00	0.86	0.85	0.84	0.90

层类型	卷积核	输出通道数	参数量
Conv/BN/Relu/Maxpool	3×3	16	464
WSA	—	16	0
Conv/BN/Relu/Maxpool	3×3	32	4 672
Conv/BN/Relu/Avgpool	3×3	64	18 560
Conv/BN/Relu/Maxpool	3×3	128	73 980
Conv/BN/Relu	3×3	128	147 712
SimAM/Dropout	—	128	0
Conv/BN/Relu/Maxpool	3×3	256	295 424
FC	—	7	7 175
总计	—	—	547 991

Target recognition of lightweight noise-resistant CNN based on wavelet scattering excitation and SimAM

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Figures/Tables 17

References 39

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模型	参数量	显存占用/MB
LCA-CNN	2 557 374	10.288
MFCNN	49 612 031	197.79
ResNet-101	42 514 503	171.20
ShuffleNetV2	1 260 779	13.26
Wavelet-SRNet	960 685	11.86
WSS-CNN	547 991	10.42

方法	平均识别准确率
方法	无噪声	$a = 2$	$a = 5$
LCA-CNN	0.806 1	0.756 6	0.714 3
MFCNN	0.863 4	0.845 8	0.828 8
ResNet-101	0.839 6	0.830 8	0.796 1
ShuffleNetV2	0.846 7	0.835 9	0.806 1
Wavelet-SRNet	0.830 8	0.824 7	0.817 9
WSS-CNN	0.877 5	0.870 1	0.851 5

散斑噪声程度	梯度幅度值
散斑噪声程度	WSS+conv1	conv1
$a = 2$	2.5012	2.1019
$a = 5$	1.8508	1.4751

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