在基于GBSAR数据的对象分类中利用极化多样性
Filip Turčinović1, Marin Kačan1, Dario Bojanjac1
1Faculty of Electrical Engineering and Computing, University of Zagreb, 10 000 Zagreb, Croatia.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究通过整合偏振数据来提高使用地面合成孔径雷达 (GBSAR) 的物体分类. 简单的数据连接与深度学习模型被证明是提高雷达传感性能最有效的方法.
科学领域:
- 微波和毫米波传感器的微波和毫米波传感器.
- 智能传感器系统 智能传感器系统
- 深度学习应用程序
背景情况:
- 廉价硬件的进步刺激了微波和毫米波传感的增长.
- 之前的工作是利用GBSAR-Pi系统的原始雷达数据探索对象分类.
研究的目的:
- 分析极化信息的潜力,以使用原始地面合成孔径雷达 (GBSAR) 数据改进深度学习模型.
- 研究将双极化雷达数据集成到分类模型中的不同策略.
主要方法:
- 在24 GHz采集GBSAR数据,具有垂直 (VV) 和水平 (HH) 两极分化.
- 使用修改后的ResNet18架构开发分类模型.
- 介绍了一种新式架构,用于双输入雷达数据.
主要成果:
- 集成的VV和HH偏振数据显著影响深度学习模型的性能.
- 一种简单的数据连接方法成为结合偏振信息的最有效方法.
- 该研究强调了天线偏振和数据合并策略的关键作用.
结论:
- 利用极化信息对于使用GBSAR数据增强基于深度学习的对象分类至关重要.
- 对于双极化数据的合并策略的选择直接影响了分类准确性.
- 未来的研究应该专注于优化偏振数据集成在雷达传感应用.
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