TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR
TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR
复制标题
DOI：
10.3390/s20061724
复制
发表时间：
2020-03-01
期刊：
影响因子：
3.9
通讯作者：
Scotti, Fabio
中科院分区：
文献类型：
--
作者：
Ying, Zilu;Xuan, Chen;Scotti, Fabio
由于合成孔径雷达（SAR）目标充满相干散斑噪声，传统的深度学习模型难以有效提取目标的关键特征，且计算复杂度较高。为了解决这个问题，提出了一种结合迁移学习的有效的轻量级卷积神经网络（CNN）模型，以更好地处理SAR目标识别任务。在这项工作中，我们首先提出了Atrous-Inception模块，它将atrous卷积和inception模块结合起来以获得丰富的全局感受野，同时严格控制参数量并实现轻量级网络架构。其次，利用迁移学习策略将光学、非光学、光学和非光学混合领域的先验知识有效迁移到SAR目标识别任务中，从而提高模型在小样本SAR目标数据集上的识别性能。最后，本文构建的模型在标准操作条件下在十种MSTAR数据集上验证了97.97%的识别率，达到了主流的目标识别率。同时，本文提出的方法在少量随机采样的SAR目标数据集上表现出很强的鲁棒性和泛化性能。
Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model's recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.