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Fast automatic airport detection in remote sensing images using convolutional neural networks

(2018) REMOTE SENSING. 10(3).
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Abstract
Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.
Keywords
RECOGNITION, airport detection, convolutional neural network, region proposal network

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Citation

Please use this url to cite or link to this publication:

Chicago
Chen, Fen, Ruilong Ren, Tim Van de Voorde, Wenbo Xu, Guiyun Zhou, and Yan Zhou. 2018. “Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks.” Remote Sensing 10 (3).
APA
Chen, Fen, Ren, R., Van de Voorde, T., Xu, W., Zhou, G., & Zhou, Y. (2018). Fast automatic airport detection in remote sensing images using convolutional neural networks. REMOTE SENSING, 10(3).
Vancouver
1.
Chen F, Ren R, Van de Voorde T, Xu W, Zhou G, Zhou Y. Fast automatic airport detection in remote sensing images using convolutional neural networks. REMOTE SENSING. 2018;10(3).
MLA
Chen, Fen, Ruilong Ren, Tim Van de Voorde, et al. “Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks.” REMOTE SENSING 10.3 (2018): n. pag. Print.
@article{8570693,
  abstract     = {Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.},
  articleno    = {443},
  author       = {Chen, Fen and Ren, Ruilong and Van de Voorde, Tim and Xu, Wenbo and Zhou, Guiyun and Zhou, Yan},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keyword      = {RECOGNITION,airport detection,convolutional neural network,region proposal network},
  language     = {eng},
  number       = {3},
  pages        = {20},
  title        = {Fast automatic airport detection in remote sensing images using convolutional neural networks},
  url          = {http://dx.doi.org/10.3390/rs10030443},
  volume       = {10},
  year         = {2018},
}

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