面向轻量级交叉注意力卷积网络的SAR目标识别

doi:10.12305/j.issn.1001-506X.2025.09.08

Abstract

Abstract:

To address the issue of high computational resource consumption for synthetic aperture radar （SAR） aircraft target recognition network, a SAR aircraft target recognition method based on lightweight cross-attention convolutional neural network （LCA-CNN） is proposed. On one hand, the cross-attention mechanism is utilized to extract features of target, enabling the network to learn key classification representations efficiently from samples, thereby improving the accuracy of fine-grained aircraft recognition. On the other hand, the use of only convolutional layers and attention modules significantly reduces the overall number of network parameters. Comparative experiments on the SAR-AIRcraft-1.0 dataset with classical deep learning SAR image recognition algorithms demonstrate that the proposed method achieves higher average recognition accuracy with fewer parameters.

Key words: synthetic aperture radar (SAR), radar target recognition, convolutional neural network (CNN), cross attention mechanism

CLC Number:

TN 95

Mingyu JIANG, Shunsheng ZHANG, Siyao XIAO. SAR target recognition based on lightweight cross-attention convolutional neural network[J]. Systems Engineering and Electronics, 2025, 47(9): 2853-2861.

Figures/Tables 12

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References 31

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层类型	卷积核大小	核个数	输出维度	参数量
Conv+BN+ReLU+CBAM	3×3	16	112×112×16	480
Conv+BN+ReLU+CBAM	5×5	32	108×108×32	12 896
Conv+BN+ReLU+CBAM	7×7	64	102×102×64	100 544
Conv+BN+ReLU+CBAM	5×5	128	98×98×128	205 184
MaxPooling	2×2	—	49×49×128	—
Conv+BN+ReLU+CBAM	5×5	256	45×45×256	819 968
MaxPooling	2×2	—	22×22×256	—
Conv+BN+ReLU+CBAM	6×6	128	17×17×128	1 180 032
MaxPooling	2×2	—	8×8×128	—
Conv+BN+ReLU+BAM	5×5	64	4×4×64	204 992
Conv	4×4	7	1×1×7	7 175
总计	—	—	—	2 531 271

方法	参数量	显存占用/MB
AlexNet^[11]	57 032 519	245.252
VGG-16^[12]	134 289 223	500.046
ResNet-101^[13]	42 514 503	190.341
A-ConvNets^[16]	325 511	1.3122
ResNeXt-101（64×4d）^[14]	83 455 272	326.549
Swin Transformer^[26]	87 768 224	127.973
所提方法	2 557 374	10.288

CBAM	BAM	准确率
CBAM*0	BAM*0	0.7909
CBAM*7	BAM*0	0.8045
CBAM*0	BAM*7	0.8166
CBAM*6	BAM*1	0.8319
CBAM*5	BAM*2	0.8245
CBAM*4	BAM*3	0.8257
CBAM*1	BAM*6	0.8183

方法	平均识别准确率	A220	A330	A320/321	ARJ21	Boeing737	Boeing787	其他
AlexNet^[11]	0.670 6	0.68	0.98	0.77	0.64	0.54	0.60	0.81
VGG-16^[12]	0.724 0	0.72	0.96	1.00	0.66	0.61	0.66	0.84
ResNet-101^[13]	0.771 7	0.81	0.96	1.00	0.85	0.68	0.68	0.81
A-ConvNets^[16]	0.782 5	0.84	1.00	0.97	0.76	0.75	0.66	0.83
ResNeXt-101（64×4d）^[14]	0.781 9	0.79	0.96	0.97	0.83	0.74	0.70	0.81
Swin Transformer^[26]	0.279 4	0.49	0.21	0.00	0.02	0.40	0.22	0.22
所提方法	0.831 9	0.90	0.98	1.00	0.85	0.78	0.76	0.84

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SAR target recognition based on lightweight cross-attention convolutional neural network

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