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Dim small target detection based on convolutinal neural network in star image

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Abstract

The detection of dim target in star image is a challenging task because of the low SNR target and complex background. In this paper, we present a deep learning approach to detecting dim small targets in single-frame star image under uneven background and different kinds of noises. We propose a fully convolutional neural network to achieve pixel-wise classification, which can complete target-background separation in a single stage rapidly. To train this network, we also build a synthetic star image dataset covering various noises and background distribution. The precise annotations of the target regions and centroid positions provided by this dataset make the supervised learning approach possible. Experimental results show that the proposed method outperforms the state-of-the-art in terms of higher detection rate and less false alarm caused by noises.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Danna Xue, Yaoqi Hu, Yu Zhu & Yanning Zhang

  2. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    Jinqiu Sun

  3. School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an, 710072, China

    Yushu Zheng

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  1. Danna Xue
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  2. Jinqiu Sun
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  3. Yaoqi Hu
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  4. Yushu Zheng
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  5. Yu Zhu
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  6. Yanning Zhang
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Correspondence to Jinqiu Sun.

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Supported be the National Natural Science Foundation of China (Grant No.61871328). Supported by the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology of China.

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Xue, D., Sun, J., Hu, Y. et al. Dim small target detection based on convolutinal neural network in star image. Multimed Tools Appl 79, 4681–4698 (2020). https://doi.org/10.1007/s11042-019-7412-z

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  • DOI: https://doi.org/10.1007/s11042-019-7412-z

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