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Article

Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN

1
School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China
2
Polytechnic Center for Natural Resources Big-Data, Ministry of Natural Resources of China, Beijing 100036, China
*
Author to whom correspondence should be addressed.
Academic Editors: Edoardo Pasolli, Zhou Zhang, Zhengxia Zou and Zhiyong Lv
Remote Sens. 2021, 13(11), 2207; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112207
Received: 23 April 2021 / Revised: 2 June 2021 / Accepted: 3 June 2021 / Published: 4 June 2021
Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks. View Full-Text
Keywords: aircraft detection; remote sensing image; multi-angle; majority voting; convolutional neural network aircraft detection; remote sensing image; multi-angle; majority voting; convolutional neural network
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MDPI and ACS Style

Ji, F.; Ming, D.; Zeng, B.; Yu, J.; Qing, Y.; Du, T.; Zhang, X. Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN. Remote Sens. 2021, 13, 2207. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112207

AMA Style

Ji F, Ming D, Zeng B, Yu J, Qing Y, Du T, Zhang X. Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN. Remote Sensing. 2021; 13(11):2207. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112207

Chicago/Turabian Style

Ji, Fengcheng, Dongping Ming, Beichen Zeng, Jiawei Yu, Yuanzhao Qing, Tongyao Du, and Xinyi Zhang. 2021. "Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN" Remote Sensing 13, no. 11: 2207. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112207

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