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轻量级深度神经网络用于合成光圈雷达中的射频干扰检测和细分
Fenghao Zheng1, Zhongmin Zhang2, Dang Zhang1
1College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China.
Scientific reports
|September 5, 2024
概括
本研究介绍了LDNet,这是一种轻量级的神经网络,用于在合成光圈雷达 (SAR) 图像中检测和细分射频干扰 (RFI). LDNet显著提高了RFI分析的准确性,并降低了RFI分析的计算成本.
科学领域:
- 遥感 遥感 遥感 遥感
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 无线电频率干扰 (RFI) 复杂化了合成光圈雷达 (SAR) 图像分析.
- 当前的RFI抑制方法往往需要先前了解RFI的存在.
- 准确的RFI检测和细分对于可靠的SAR数据至关重要.
研究的目的:
- 提出一种新的轻量级神经网络,LDNet,用于在时间频域中进行RFI检测和细分.
- 提高RFI抑制系统的运行速度和效率.
- 为了提高SAR图像中RFI划分的准确性.
主要方法:
- 开发一个轻量级神经网络 (LDNet) 用于时间频域RFI分析.
- 实施轻量级模块和修剪操作以优化网络性能.
- 对LDNet与基于值的方法和深度学习细分网络的评估,包括AC-UNet.
主要成果:
- LDNet在平均交叉与联合 (MIoU) 中取得了显著的改进:比基于值的方法提高24.56%,比一般深度学习网络提高13.29%,比AC-UNet提高7.54%.
- 与AC-UNet相比,LDNet显示模型大小 (99.03%) 和推断延迟 (24.53%) 的大幅减少.
- 该网络精确地将RFI像素区域分成时间频谱图.
结论:
- LDNet为SAR图像中的RFI检测和细分提供了有效和高效的解决方案.
- 拟议的轻量化方法解决了现有的RFI抑制系统的局限性.
- 在RFI的存在下,LDNet为改进SAR图像分析提供了有希望的进步.
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