Airplane detection in remote sensing images using convolutional neural networks

Chao Ouyang; Zhong Chen; Feng Zhang; Yifei Zhang

doi:10.1117/12.2285776

8 March 2018 Airplane detection in remote sensing images using convolutional neural networks

Chao Ouyang, Zhong Chen, Feng Zhang, Yifei Zhang

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091B (2018) https://doi.org/10.1117/12.2285776
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

Citation Download Citation

Chao Ouyang, Zhong Chen, Feng Zhang, and Yifei Zhang "Airplane detection in remote sensing images using convolutional neural networks", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091B (8 March 2018); https://doi.org/10.1117/12.2285776

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