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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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.
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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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DOI: https://doi.org/10.1007/s11063-023-11305-1