![](/images/graphics-bg.png)
Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images
Joint Authors
Pan, Bin
Tai, Jianhao
Zheng, Qi
Zhao, Shanshan
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-27
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract 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%.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186764