Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks
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 algorith...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/10/3/443 |
| _version_ | 1818321558053060608 |
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| author | Fen Chen Ruilong Ren Tim Van de Voorde Wenbo Xu Guiyun Zhou Yan Zhou |
| author_facet | Fen Chen Ruilong Ren Tim Van de Voorde Wenbo Xu Guiyun Zhou Yan Zhou |
| author_sort | Fen Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T10:42:48Z |
| format | Article |
| id | doaj.art-c6556140fb814017b0c3818d28648540 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:42:48Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c6556140fb814017b0c3818d286485402022-12-21T23:50:24ZengMDPI AGRemote Sensing2072-42922018-03-0110344310.3390/rs10030443rs10030443Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural NetworksFen Chen0Ruilong Ren1Tim Van de Voorde2Wenbo Xu3Guiyun Zhou4Yan Zhou5School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, ChinaDepartment of Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, BelgiumSchool of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, ChinaFast 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.http://www.mdpi.com/2072-4292/10/3/443airport detectionconvolutional neural networkregion proposal network |
| spellingShingle | Fen Chen Ruilong Ren Tim Van de Voorde Wenbo Xu Guiyun Zhou Yan Zhou Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks Remote Sensing airport detection convolutional neural network region proposal network |
| title | Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks |
| title_full | Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks |
| title_fullStr | Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks |
| title_full_unstemmed | Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks |
| title_short | Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks |
| title_sort | fast automatic airport detection in remote sensing images using convolutional neural networks |
| topic | airport detection convolutional neural network region proposal network |
| url | http://www.mdpi.com/2072-4292/10/3/443 |
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