文献类型：期刊文献

英文题名：Data Augmentation Based on Attributed Scattering Centers to Train Robust CNN for SAR ATR

作者：Lv, Junta[1];Liu, Yue[2]

第一作者：Lv, Junta

通讯作者：Lv, JT[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, Henan, Peoples R China;[2]Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, Henan, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2019

卷号：7

起止页码：25459-25473

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20192407025752);Scopus(收录号：2-s2.0-85066834969);WOS:【SCI-EXPANDED(收录号:WOS:000467980600001)】；

语种：英文

外文关键词：Convolutional neural network (CNN); synthetic aperture radar (SAR); target recognition; attributed scattering center (ASC); data augmentation

摘要：Driven by the good classification performance of the convolutional neural network (CNN), this study proposes a CNN-based synthetic aperture radar (SAR) target recognition method. In this paper, a novel data augmentation algorithm is proposed via target reconstruction based on attributed scattering centers (ASC). The ASCs reflect the electromagnetic phenomenon of SAR targets, which can be used to reconstruct the target's characteristics. The sparse representation (SR) algorithm is first employed to extract the ASCs from a single SAR image. Afterward, some of the extracted ASCs are selected to reconstruct the target's image. By repeating the process, many new images can be generated as available training samples. In the classification stage, a CNN architecture is designed and trained by the augmented samples. For the test sample, it is also reconstructed using all the extracted ASCs thus relieving the interferences caused by the clutters or noises in the background. Finally, the reconstructed image from the test sample is classified based on the trained CNN. The reconstructed image from the ASCs can reduce the clutters and noises thus enhancing the image quality. More importantly, the generated new training samples could cover more operating conditions, which may probably occur in SAR target recognition. Therefore, the trained CNN can work more robustly in different situations. In the experiments, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to evaluate the performance of the proposed approach. This method could classify the 10 classes of targets with an accuracy of 99.48% under the standard operating condition (SOC). For the extended operating conditions like configuration variance, depression angle variance, noise corruption, and partial occlusion, the proposed method also displays superior performance over some baseline algorithms drawn from state-of-the-art literature.