Abstract
Monitoring target through satellite images is widely used in intelligence analysis and for anomaly detection. Meanwhile, it is also challenging due to the shooting conditions and the huge amounts of data. We propose a method for target monitoring based on deep convolutional neural networks (DCNN). The method is implemented by three procedures: (i) Label the target and generate the dataset, (ii) train a classifier, and (iii) monitor the target. First, the target area is labelled manually to form a dataset. In the second stage a classifier based on DCNN using Keras library is well-trained. In the last stage the target is monitored in the test satellite images. The method was tested on two different application scenarios. The results show that the mothed is effective.
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Acknowledgments
The authors would like to thank Jianjun Zhang and Yifei Fan for their constructive discussions and comments.
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© 2016 Springer International Publishing Switzerland
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Sui, X., Zhang, J., Hu, X., Zhang, L. (2016). Monitoring Target Through Satellite Images by Using Deep Convolutional Networks. In: Lee, R. (eds) Software Engineering Research, Management and Applications. Studies in Computational Intelligence, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-319-33903-0_6
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DOI: https://doi.org/10.1007/978-3-319-33903-0_6
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