Volume 13 Issue 5
Sep.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(5): 974-984
WANG Bohong, SHEN Biao, MU Wenxing, et al. Research on super-resolution methods for radar targets based on bat-inspired spectrogram correlation and transformation models[J]. Journal of Radars, 2025, 14(2): 293–308. doi: 10.12000/JR24239
Citation: YU Yue, WANG Chen, SHI Jun, et al. Modeling and correction of label noise uncertainty for SAR ATR[J]. Journal of Radars, 2024, 13(5): 974–984. doi: 10.12000/JR24130
shu

Modeling and Correction of Label Noise Uncertainty for SAR ATR

DOI: 10.12000/JR24130 CSTR: 32380.14.JR24130
  • 1.

    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215000, China

  • 2.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 3.

    Sichuan Aerospace System Engineering Research Institute, Chengdu 610100, China

  • 4.

    School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  • 5.

    National Key Laboratory of Electromagnetic Space Security, Chengdu 610036, China

Funds:  The National Natural Science Foundation of China (62201375), Natural Science Foundation of Jiangsu Province (BK20220635), Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1762), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300756)
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    Abstract
  • The success of deep supervised learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) relies on a large number of labeled samples. However, label noise often exists in large-scale datasets, which highly influence network training. This study proposes loss curve fitting-based label noise uncertainty modeling and a noise uncertainty-based correction method. The loss curve is a discriminative feature to model label noise uncertainty using an unsupervised fuzzy clustering algorithm. Then, according to this uncertainty, the sample set is divided into different subsets: the noisy-label set, clean-label set, and fuzzy-label set, which are further used in training loss with different weights to correct label noise. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove that our method can deal with varying ratios of label noise during network training and correct label noise effectively. When the training dataset contains a small ratio of label noise (40%), the proposed method corrects 98.6% of these labels and trains the network with 98.7% classification accuracy. Even when the proportion of label noise is large (80%), the proposed method corrects 87.8% of label noise and trains the network with 82.3% classification accuracy.

     

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