Synthetic aperture radar image classification : a survey
العناوين الأخرى
تصنيف صور الرادار ذي الفجوة المركبة : مراجعة لدراسة سابقة
المؤلفون المشاركون
Ali, Asil Sami
Abd al-Munim, Mathil Imad al-Din
المصدر
العدد
المجلد 61، العدد 5 (31 مايو/أيار 2020)، ص ص. 1223-1232، 10ص.
الناشر
تاريخ النشر
2020-05-31
دولة النشر
العراق
عدد الصفحات
10
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
In this review paper, several studies and researches were surveyed for assisting future researchers to identify available techniques in the field of classification of Synthetic Aperture Radar (SAR) images.
SAR images are becoming increasingly important in a variety of remote sensing applications due to the ability of SAR sensors to operate in all types of weather conditions, including day and night remote sensing for long ranges and coverage areas.
Its properties of vast planning, search, rescue, mine detection, and target identification make it very attractive for surveillance and observation missions of Earth resources.
With the increasing popularity and availability of these images, the need for machines has emerged to enhance the ability to identify and interpret these images effectively.
This is due to the fact that SAR image processing requires the formation of an image from the measured radar scatter returns, followed by a treatment to discover and define the image's composition.
After reviewing several previous studies that succeeded in achieving a classification of SAR images for specific goals, it became obvious that they could be generalized to all types of SAR images.
The most prominent use of Convolutional Neural Networks (CNN) was successful in extracting features from the images and training the neural network to analyze and classify them into classes according to these features.
The dataset used in this model was obtained from the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, which consists of a set of SAR images of military vehicles, for which the application of the CNN approach achieved a final accuracy of 97.91% on ten different classes.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ali, Asil Sami& Abd al-Munim, Mathil Imad al-Din. 2020. Synthetic aperture radar image classification : a survey. Iraqi Journal of Science،Vol. 61, no. 5, pp.1223-1232.
https://search.emarefa.net/detail/BIM-970230
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ali, Asil Sami& Abd al-Munim, Mathil Imad al-Din. Synthetic aperture radar image classification : a survey. Iraqi Journal of Science Vol. 61, no. 5 (2020), pp.1223-1232.
https://search.emarefa.net/detail/BIM-970230
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ali, Asil Sami& Abd al-Munim, Mathil Imad al-Din. Synthetic aperture radar image classification : a survey. Iraqi Journal of Science. 2020. Vol. 61, no. 5, pp.1223-1232.
https://search.emarefa.net/detail/BIM-970230
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Text in English ; abstracts in English and Arabic.
رقم السجل
BIM-970230
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