用模拟数据训练雷达目标识别模型，仅需每类一个标注样例就达99%以上准确率。
Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data
- 分层渐进对齐：从域级到类别级逐步缩小真实与模拟数据差距
- 仅用每类1个标签样本，两种设置下准确率分别达99.63%和98.91%
- 适合缺乏标注数据的雷达目标识别场景，尤其适用于模拟数据训练
近年来，利用合成孔径雷达（SAR）图像模拟数据训练自动目标识别（ATR）模型成为解决实测数据不足的可行方案。为弥合真实与模拟数据间的域差异，常采用无监督域自适应（UDA）技术构建ATR模型。然而，由于目标域缺乏标签数据，UDA面临巨大挑战。为此，本文提出一种半监督域自适应（SSDA）框架，通过渐进多层级对齐实现模拟数据辅助的SAR ATR。首先，提出渐进小波变换数据增强（PWTDA），通过分析双域图像小波分解子带差异，实现域级对齐，具体通过混合高频子带成分缩小域差距。其次，设计渐近实例-原型对齐（AIPA）策略，使源域实例靠近对应目标原型，实现类别级对齐。此外，通过挖掘单样本与多样本的强弱增强一致性，实现一致性对齐，提升模型泛化能力。在SAMPLE数据集上的大量实验表明，本方法在仅每类一个标签样本的两种常见设置下，识别准确率分别达到99.63%和98.91%，显著优于现有先进SSDA方法。
原文摘要 · Abstract (English)
Recently, an intriguing research trend for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery has arisen: using simulated data to train ATR models is a feasible solution to the issue of inadequate measured data. To close the domain gap that exists between the real and simulated data, the unsupervised domain adaptation (UDA) techniques are frequently exploited to construct ATR models. However, for UDA, the target domain lacks labeled data to direct the model training, posing a great challenge to ATR performance. To address the above problem, a semi-supervised domain adaptation (SSDA) framework has been proposed adopting progressive multi-level alignments for simulated data-aided SAR ATR. First, a progressive wavelet transform data augmentation (PWTDA) is presented by analyzing the discrepancies of wavelet decomposition sub-bands of two domain images, obtaining the domain-level alignment. Specifically, the domain gap is narrowed by mixing the wavelet transform high-frequency sub-band components. Second, we develop an asymptotic instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes, aiming to achieve category-level alignment. Moreover, the consistency alignment is implemented by excavating the strong-weak augmentation consistency of both individual samples and the multi-sample relationship, enhancing the generalization capability of the model. Extensive experiments on the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, indicate that our approach obtains recognition accuracies of 99.63% and 98.91% in two common experimental settings with only one labeled sample per class of the target domain, outperforming the most advanced SSDA techniques.
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