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- W3132155590 abstract "Oil is an essential asset for every country, and plays a key role in world trade system. The detection of oil tanks is a very important task for both military and commerce. Recently, researchers have shown an increasing interest in oil tanks detection in remote sensing imagery. However, the previous works almost used the methods of circle detection, but the real oil tanks in remote sensing imagery are more close to ellipses. In this paper, we propose an oil tanks detector base on an U-shape Fully Convolutional Network(FCN) in optical remote sensing images. The structure of our network consists of three parts: feature extraction part, feature merge part and the output layer. The output layer consists of two output branches, one branch is a score map branch, which generates confidence score to indicate the region of oil tanks at pixel wise, and the other ends up with several channels which regress the ellipse geometric parameters (center, horizontal axis and vertical axis). In addition, we also design a novel loss function adapted to our network. The experimental results conducted on our dataset collected from Google Earth show that this method achieves promising performance on oil tanks detection in terms of both efficiency and accuracy in high-resolution optical remote sensing images." @default.
- W3132155590 created "2021-03-01" @default.
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- W3132155590 date "2020-09-26" @default.
- W3132155590 modified "2023-10-18" @default.
- W3132155590 title "Ellipse-FCN: Oil Tanks Detection from Remote Sensing Images with Fully Convolution Network" @default.
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- W3132155590 doi "https://doi.org/10.1109/igarss39084.2020.9324631" @default.
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