A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network
A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network
复制标题
基于改进深度卷积神经网络的SAR图像目标识别新算法
DOI：
10.1007/s12559-018-9563-z
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发表时间：
2019-12-01
影响因子：
5.4
通讯作者：
Yang, Erfu
中科院分区：
文献类型：
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作者：
Gao, Fei;Huang, Teng;Yang, Erfu
为了利用生物启发的深度学习模型的自动特征提取能力，并增强目标特征的学习，我们提出了一种新的深度学习算法。这是基于使用改进的成本函数训练的深度卷积神经网络（DCNN），并与支持向量机（SVM）相结合。具体而言，类分离信息，这显着促进类内的紧凑性和类间的可分性在学习特征的过程中，被添加到一个改进的成本函数作为正则化项，以提高DCNN的特征提取能力。将增强的DCNN应用于SAR图像的特征学习，并利用SVM将特征映射为输出标签。使用基准SAR图像数据从运动和静止目标捕获和识别（MSTAR）数据库进行仿真实验。比较结果表明，我们所提出的方法的有效性，平均准确率为99%的10种类型的目标，包括变种和铰接目标。我们的结论是，我们提出的DCNN方法具有显着的潜力，可用于SAR图像目标识别，并可以作为一个新的基准研究界。
In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural network (DCNN) trained with an improved cost function, and combined with a support vector machine (SVM). Specifically, class separation information, which explicitly facilitates intra-class compactness and inter-class separability in the process of learning features, is added to an improved cost function as a regularization term, to enhance the DCNN's feature extraction ability. The enhanced DCNN is applied to learn the features of Synthetic Aperture Radar (SAR) images, and the SVM is utilized to map features into output labels. Simulation experiments are performed using benchmark SAR image data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Comparative results demonstrate the effectiveness of our proposed method, with an average accuracy of 99% on ten types of targets, including variants and articulated targets. We conclude that our proposed DCNN method has significant potential to be exploited for SAR image target recognition, and can serve as a new benchmark for the research community.