Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization
المؤلفون المشاركون
Zhai, Yikui
Deng, Wenbo
Gan, Junying
Piuri, Vincenzo
Zeng, Junying
Xu, Ying
Ke, Qirui
Sun, Bing
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-11-15
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model.
In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L2-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance.
Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets.
Secondly, the proposed MS-CNN framework with L2-Regularization is trained to extract robust features, in which the L2-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model.
Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples.
Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance.
Extensive experiments demonstrated that our proposed method could not only outperform other state-of-the-art methods on the public and extended MSTAR dataset but also obtain good performance on the random small datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhai, Yikui& Deng, Wenbo& Xu, Ying& Ke, Qirui& Gan, Junying& Sun, Bing…[et al.]. 2019. Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129652
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhai, Yikui…[et al.]. Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1129652
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhai, Yikui& Deng, Wenbo& Xu, Ying& Ke, Qirui& Gan, Junying& Sun, Bing…[et al.]. Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129652
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129652
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر