文献类型：期刊文献

英文题名：Exploiting multi-level deep features via joint sparse representation with application to SAR target recognition

作者：Lv, Junya[1]

第一作者：Lv, Junya

通讯作者：Lv, JY[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, 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：2020

卷号：41

期号：1

起止页码：320-338

外文期刊名：INTERNATIONAL JOURNAL OF REMOTE SENSING

收录：;EI(收录号：20192907194566);Scopus(收录号：2-s2.0-85068918675);WOS:【SCI-EXPANDED(收录号:WOS:000476204200001)】；

语种：英文

外文关键词：Convolutional neural networks - Multi-task learning - Synthetic aperture radar

摘要：In order to improve synthetic aperture radar (SAR) target recognition performance, this paper proposes a novel method using multi-level deep features. The multi-level deep features are learned by the convolutional neural network (CNN), which are capable of describing the target characteristics from different aspects. In order to make full use of the discrimination contained in the multi-level deep features, the joint sparse representation (JSR) is used as the basic classifier, which performs the multi-task learning to jointly classify the multi-level deep features. It could not only represent each feature properly but also consider the correlations between different levels of features. Based on the solutions from JSR, the target label is classified as the training class with the minimum reconstruction error. By fully exploiting the discriminative information contained in the multi-level deep features, the proposed method could effectively enhance SAR target recognition performance. The moving and stationary target acquisition and recognition (MSTAR) dataset is employed in the experiments. The results show that the proposed method could achieve a significantly high recognition rate of 99.38% for classifying 10 classes of targets under the standard operating condition (SOC), which is higher than those from some reference methods drawn from current literatures. Under different types of extended operating conditions (EOCs), the overall performance of the proposed method keeps superior over the reference methods. In addition, the outlier rejection capability of the proposed method is also better than the compared methods. All these experimental results validate the high effectiveness of the proposed method.