噪声伪标签容忍的半监督SAR目标识别

doi:10.12305/j.issn.1001-506X.2025.06.08

Abstract

Abstract:

To address the challenge of limited recognition accuracy due to noise pseudo-labels in semi-supervised synthetic aperture radar (SAR) automatic target recognition (ATR) with scarce labeled training samples, a noise pseudo-label tolerant semi-supervised SAR ATR method is proposed. The proposed method includes two stages. In the first stage, high-reliability pseudo-labels are generated and selected by combining residual network (ResNet) and multi-classifier fusion, so as to enrich the labeled training dataset. In the second stage, a robust consistency learning network with noise pseudo-label tolerant characteristics is constructed based on WideResNet backbone to implement ATR with high accuracy, in which a noise pseudo-label smoothing mechanism is designed as well as a piecewise noise pseudo-label tolerant loss function. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) SAR dataset. The experimental results demonstrate that the proposed method achieves an average recognition accuracy of 93.37% across 10-class targets with only five labeled training samples for each class, which significantly enhances recognition performance and generalization ability.

Key words: synthetic aperture radar (SAR), automatic target recognition (ATR), semi-supervised, deep learning (DL), pseudo-label

CLC Number:

TP391

Xinzheng ZHANG, Mengke YAN, Xiaolin ZHU. Noise pseudo-label tolerant semi-supervised SAR target recognition[J]. Systems Engineering and Electronics, 2025, 47(6): 1796-1805.

Figures/Tables 9

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类别	样本数量(俯仰角为17°)	样本数量(俯仰角为15°)
2S1	299	274
BMP2	233	195
BRDM2	298	274
BTR60	256	195
BTR70	233	196
D7	299	274
T62	299	273
T72	232	196
ZIL131	299	274
ZSU234	299	274
总计	2 425	2 747

算法	每类标签样本数量
算法	5	10	20	40
Fixmatch^[32]	30.65	93.41	96.51	98.19
MGAN^[37]	—	—	85.23	90.82
SCA^[38]	—	95.22	98.23	99.85
HDLM^[39]	72.37	88.16	95.17	97.85
EUAPS^[40]	88.65	98.58	99.84	99.86
本文方法	93.37	99.33	99.86	99.90

伪标签预测	噪声平滑模块	分段损失函数	样本数量
伪标签预测	噪声平滑模块	分段损失函数	5	10
－	－	－	63.26	95.13
√	－	－	91.75	98.56
－	√	－	76.41	98.47
－	－	√	81.03	98.39
√	√	√	93.37	99.33

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Noise pseudo-label tolerant semi-supervised SAR target recognition

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