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Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

机译：基于具有少量训练样本的原型网络的多方面SAR目标识别

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摘要

At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.
机译：目前，合成孔径雷达（SAR）自动目标识别（ATR）已经深入研究和广泛用于军事和平民。 SAR图像对成像地质的方位角方面非常敏感;不同方面的同一目标不同。因此，多方面SAR图像序列包含用于分类和识别的更多信息，这需要可靠且坚固的多方面目标识别方法。如今，SAR目标识别方法主要基于深度学习。然而，SAR数据集通常可以获得昂贵，特别是对于某个目标。难以获得足够的样品进行深度学习模型培训。本文提出了一种基于原型网络的多方面SAR目标识别方法。此外，还引入了多任务学习和多级特征融合的方法，以提高少量训练样本的情况下的识别精度。通过使用MSTAR数据集的实验已经证明，我们的方法的识别准确性可以接近所有样品的累积水平，我们的方法可以应用于其他羽毛提取模型，以处理小型样本问题。

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