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Article

Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks

1
Aeronautics Engineering College, AFEU, Xi’an 710038, China
2
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
3
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2335; https://doi.org/10.3390/s18072335
Received: 8 May 2018 / Revised: 5 July 2018 / Accepted: 13 July 2018 / Published: 18 July 2018
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images. View Full-Text
Keywords: remote sensing images; airplane detection; fully convolutional neural networks; feature fusion remote sensing images; airplane detection; fully convolutional neural networks; feature fusion
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MDPI and ACS Style

Xu, Y.; Zhu, M.; Xin, P.; Li, S.; Qi, M.; Ma, S. Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks. Sensors 2018, 18, 2335. https://doi.org/10.3390/s18072335

AMA Style

Xu Y, Zhu M, Xin P, Li S, Qi M, Ma S. Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks. Sensors. 2018; 18(7):2335. https://doi.org/10.3390/s18072335

Chicago/Turabian Style

Xu, Yuelei, Mingming Zhu, Peng Xin, Shuai Li, Min Qi, and Shiping Ma. 2018. "Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks" Sensors 18, no. 7: 2335. https://doi.org/10.3390/s18072335

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