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In military fields, it is essential to monitor the dynamic of aircrafts through remote sensing images. Due to lack of automated assistive analysis methods of recent works, we propose a novel method for automatically monitoring the dynamic of aircrafts through remote sensing images in this paper. The method consists of two phases: (i) establish a priori model of aircrafts in airports and learn a Convolutional Neural Networks (CNN) classifier that identifies the state of aircrafts, and (ii) predict the states of aircrafts in the new images. The proposed method was tested on the remote sensing images of two typical airports. Experimental results show that the method is able to monitor the dynamic of aircrafts with high accuracy. We conclude that the method can report the states of aircrafts in airports correctly.
Xudong Sui,Xiaohui Hu, andJinfang Zhang
"CNN based aircraft dynamic monitoring through remote sensing images", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203E (21 July 2017); https://doi.org/10.1117/12.2281745
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Xudong Sui, Xiaohui Hu, Jinfang Zhang, "CNN based aircraft dynamic monitoring through remote sensing images," Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203E (21 July 2017); https://doi.org/10.1117/12.2281745