Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images
المؤلفون المشاركون
Pan, Bin
Tai, Jianhao
Zheng, Qi
Zhao, Shanshan
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-07-27
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Aircraft detection from high-resolution remote sensing images is important for civil and military applications.
Recently, detection methods based on deep learning have rapidly advanced.
However, they require numerous samples to train the detection model and cannot be directly used to efficiently handle large-area remote sensing images.
A weakly supervised learning method (WSLM) can detect a target with few samples.
However, it cannot extract an adequate number of features, and the detection accuracy requires improvement.
We propose a cascade convolutional neural network (CCNN) framework based on transfer-learning and geometric feature constraints (GFC) for aircraft detection.
It achieves high accuracy and efficient detection with relatively few samples.
A high-accuracy detection model is first obtained using transfer-learning to fine-tune pretrained models with few samples.
Then, a GFC region proposal filtering method improves detection efficiency.
The CCNN framework completes the aircraft detection for large-area remote sensing images.
The framework first-level network is an image classifier, which filters the entire image, excluding most areas with no aircraft.
The second-level network is an object detector, which rapidly detects aircraft from the first-level network output.
Compared with WSLM, detection accuracy increased by 3.66%, false detection decreased by 64%, and missed detection decreased by 23.1%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Pan, Bin& Tai, Jianhao& Zheng, Qi& Zhao, Shanshan. 2017. Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images. Journal of Sensors،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1186764
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Pan, Bin…[et al.]. Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images. Journal of Sensors No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1186764
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Pan, Bin& Tai, Jianhao& Zheng, Qi& Zhao, Shanshan. Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1186764
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186764
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