Oil Tank Detection in Optical Remote Sensing Imagery Based on Quasi-circular Shadow

LI Xuan; LIU Yunqing

doi:10.11999/JEIT151334

Volume 38 Issue 6

Jun. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(6): 1489-1495

LI Xuan, LIU Yunqing. Oil Tank Detection in Optical Remote Sensing Imagery Based on Quasi-circular Shadow[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1489-1495. doi: 10.11999/JEIT151334

Citation:

LI Xuan, LIU Yunqing. Oil Tank Detection in Optical Remote Sensing Imagery Based on Quasi-circular Shadow[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1489-1495. doi: 10.11999/JEIT151334

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Oil Tank Detection in Optical Remote Sensing Imagery Based on Quasi-circular Shadow

doi: 10.11999/JEIT151334

LI Xuan¹,
LIU Yunqing²

1.
2.
(School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China)

Funds:

The National 973 Program of China (613192)

Received Date: 2015-11-26
Rev Recd Date: 2016-03-23
Publish Date: 2016-06-19

Abstract

Abstract

To deal with the issue of low oil tanks recognition rate in optical remote sensing image, an improved oil tanks detection method is proposed, which is based on the improved visual saliency model and quasi-circular shadow region. Firstly, the oil tanks are separated from the complex background by using the improved visual saliency model. Secondly, the circular shadow regions are finely detected, and the suspected oil tanks are obtained. Then, the shadow region and the preliminary detection result of oil tanks are obtained. Finally, the false oil tank targets are removed and oil depots are determined based on graph search strategy and prior knowledge. The proposed method is robust to the oil tanks in the optical remote sensing images, and can effectively detect the oil tanks in high recognition rate. The experimental results indicate that the proposed algorithm are fast and accurate to detect the oil tanks, which is suitable for optical remote sensing images of different spatial resolutions.
- Optical remote sensing images,
- Quasi-circular shadow region,
- Visual saliency model,
- Feature detection,
- Oil tanks

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References(21)

References

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