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Complex-Valued UNet for Radar Image Segmentation

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Abstract

In this paper, we design a complex-valued UNet (CV-UNet) architecture for radar complex data. This method selected the two-dimensional distributed complex-valued information after imaging as the feature, making full use of the amplitude and phase information in radar data, which is more suitable for the target segmentation with a small dataset. At the same time, an improved loss function is proposed to reduce the impact of the class imbalance problem. Compared to the real-valued UNet, UNet+, and UNet++ methods, the effectiveness and superiority of the CV-UNet are verified. The simulation results indicate that complex-valued operations can greatly improve the performance of segmentation, and our proposed loss function can effectively balance the loss values of different pixels to further improve the segmentation accuracy.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Wang Yufei & Zhang Linxi

  2. No. 365 Institution, Northwestern Polytechnical University, Xi’an, 710065, China

    Song Zuxun

Authors
  1. Wang Yufei
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  2. Zhang Linxi
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  3. Song Zuxun
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Contributions

YW and LZ wrote the main manuscript text and YW prepared all figures. LZ and ZS provide the hardware and software support. All authors reviewed the manuscript.

Corresponding author

Correspondence to Zhang Linxi.

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The authors declare no competing interests.

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The demo of this paper can be downloaded from https://pan.baidu.com/s/1rdZGe1I8B-92WXoAyZ4zbA?pwd=wyf1.

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Yufei, W., Linxi, Z. & Zuxun, S. Complex-Valued UNet for Radar Image Segmentation. Neural Process Lett 55, 8151–8162 (2023). https://doi.org/10.1007/s11063-023-11305-1

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