期刊
REMOTE SENSING
卷 10, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/rs10030443
关键词
airport detection; convolutional neural network; region proposal network
类别
资金
- National Science and Technology Major Project of China [30-Y20A07-9003-17/18, 30-Y20A04-9001-17/18]
- National Natural Science Foundation of China [41671427, 41371398]
- National Key R & D Program of China [2016YFB0502300]
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.
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