Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-21
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص EN
Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition.
NSST was used to decompose an SAR image into multilevel representations.
These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target.
During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations.
The joint sparse representation could represent individual components independently while considering the inner correlations between different components.
Therefore, the precision of joint representation could be enhanced.
Finally, the target label of the test sample was determined according to the overall reconstruction error.
Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ding, Huijie& Lin, Arthur K. L.. 2020. Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1201844
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ding, Huijie& Lin, Arthur K. L.. Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data. Mathematical Problems in Engineering No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1201844
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ding, Huijie& Lin, Arthur K. L.. Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1201844
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1201844
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