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Proceedings Paper

Airplane detection in remote sensing images using convolutional neural networks
Author(s): Chao Ouyang; Zhong Chen; Feng Zhang; Yifei Zhang
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Paper Abstract

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

Paper Details

Date Published: 8 March 2018
PDF: 5 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091B (8 March 2018);
Show Author Affiliations
Chao Ouyang, Huazhong Univ. of Science and Technology (China)
Zhong Chen, Huazhong Univ. of Science and Technology (China)
Feng Zhang, Huazhong Univ. of Science and Technology (China)
Yifei Zhang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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