logo

SCIENCE CHINA Information Sciences, Volume 64 , Issue 9 : 194201(2021) https://doi.org/10.1007/s11432-019-9920-2

Surface-to-air missile sites detection agent with remote sensing images

More info
  • ReceivedFeb 26, 2019
  • AcceptedJun 6, 2019
  • PublishedMay 21, 2021

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61603210, 61673240).


Supplement

Videos and other supplemental documents.


References

[1] Han J Y. The recognition and extraction of surface to air missile position targets. Dissertation for Master Degree. Jilin: Jilin University, 2016. Google Scholar

[2] Arel I, Rose D C, Karnowski T P. Deep Machine Learning - A New Frontier in Artificial Intelligence Research. IEEE Comput Intell Mag, 2010, 5: 13-18 CrossRef Google Scholar

[3] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014. 580--587. Google Scholar

[4] Redmon J, Farhadi A. Yolov3: an incremental improvement. 2018,. arXiv Google Scholar

[5] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015. 91--99. Google Scholar

[6] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014,. arXiv Google Scholar

[7] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 770--778. Google Scholar

  • Figure 1

    (Color online) (a) SAMSs dataset; (b) working flow of detection agent; (c) detection results of different models. “DA" means data augmentation; (d) searching area and target locations; (e) some searching results.

qqqq

Contact and support