SAR Automatic Target Recognition Based on Multiview Deep Learning Framework
SAR Automatic Target Recognition Based on Multiview Deep Learning Framework
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DOI：
10.1109/tgrs.2017.2776357
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发表时间：
2018-04-01
影响因子：
8.2
通讯作者：
Yeo, Tat-Soon
中科院分区：
文献类型：
--
作者：
Pei, Jifang;Huang, Yulin;Yeo, Tat-Soon
利用深度神经网络从合成孔径雷达(SAR)图像中学习和提取有价值的特征用于合成孔径雷达自动目标识别(ATR)是一种可行且有前景的方法。然而，利用有限的原始SAR图像来有效地训练深度神经网络是非常困难的。本文提出了一种基于多视点深度学习框架的合成孔径雷达自动目标识别方法。基于多视角合成孔径雷达ATR模式，我们首先提出了一种灵活的方法来生成充足的多视角合成孔径雷达数据，从而在不需要大量原始合成孔径雷达图像的情况下保证了网络训练所需的大量输入。然后，采用了一种独特的包含多输入并行网络拓扑的深卷积神经网络。该网络将不同视角的输入SAR图像的特征逐层学习，同时将学习到的不同视角的特征逐步融合到不同的层次。因此，该框架能够达到较好的识别性能，并且只需要少量的原始SAR图像来生成网络训练样本。在运动和静止目标采集与识别数据集上的实验结果表明了该框架的优越性。
It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep learning framework was employed. Based on the multiview SAR ATR pattern, we first present a flexible mean to generate adequate multiview SAR data, which can guarantee a large amount of inputs for network training without needing many raw SAR images. Then, a unique deep convolutional neural network containing a parallel network topology with multiple inputs is adopted. The features of input SAR images from different views will be learned by the proposed network layer by layer; meanwhile, the learned features from the distinct views are fused in different layers progressively. Therefore, the proposed framework is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation. Experimental results have shown the superiority of the proposed framework based on the Moving and Stationary Target Acquisition and Recognition data set.