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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition
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Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition

机译：用于SAR目标识别的轻量级两条卷积神经网络

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摘要

This letter proposes a lightweight two-stream convolutional neural network (CNN) for synthetic aperture radar (SAR) target recognition. Specifically, the two-stream CNN first extracts low-level features by three alternating convolution layers and max-pooling layers. Then two streams are followed to extract local and global features. One stream uses global maximum pooling to extract local features with the greatest response; the other uses large-stride convolution kernels to extract global features. Finally, the two streams are combined for target recognition. Therefore, the two-stream CNN can learn rich multilevel features to achieve high recognition accuracy for SAR target recognition. Moreover, compared to other popular CNNs, the two-stream CNN is very lightweight. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) data set demonstrate that the proposed method not only can improve the recognition accuracy but also reduce the number of parameters of the model dramatically.
机译：这封信提出了一种用于合成孔径雷达（SAR）目标识别的轻量级两条卷积神经网络（CNN）。具体地，双流CNN首先通过三个交替的卷积层和最大池层提取低电平特征。然后遵循两个流提取本地和全局功能。一条流使用全局最大池来提取最大响应的本地特征;另一个使用大步卷积内核提取全局功能。最后，两条流组合用于目标识别。因此，两流CNN可以学习丰富的多级特征，以实现SAR目标识别的高识别精度。此外，与其他流行的CNN相比，两流CNN非常重量轻。对移动和静止目标采集和识别（MSTAR）数据集的实验结果表明，所提出的方法不仅可以提高识别准确性，而且显着降低了模型的参数数量。

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