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Open AccessArticle

Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

1
School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, China
3
Department of Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
4
Department of Geography, Ghent University, Krijgslaan 281, S8, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 443; https://doi.org/10.3390/rs10030443
Received: 5 February 2018 / Revised: 28 February 2018 / Accepted: 4 March 2018 / Published: 12 March 2018
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. View Full-Text
Keywords: airport detection; convolutional neural network; region proposal network airport detection; convolutional neural network; region proposal network
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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 Sens. 2018, 10, 443.

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