基于全球导航卫星系统和反向传播神经网络的交叉测量合成孔径雷达热层延迟校正方法:更适合明显地形变化的地区
Liangcai Qiu1, Peng Chen1,2, Yibin Yao3,4
1College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
一种使用反向传播神经网络和全球导航卫星系统 (GNSS) 顶点总延迟 (ZTD) 的新方法显著改善了干扰测量合成孔径雷达 (InSAR) 的大气延迟校正. 这种方法提高了空间预测的准确性,并减少了InSAR数据中的大气相位屏幕错误.
科学领域:
- 地质测量和遥感技术
- 大气科学 大气科学
- 人工智能在地球观测中的作用
背景情况:
- 大气延迟校正是干涉测量合成孔径雷达 (InSAR) 精度的一个关键挑战.
- 现有的热层延迟校正方法在处理大型数据集和实现高时间分辨率方面存在局限性.
- 全球导航卫星系统 (GNSS) 数据提供高时间分辨率,但对于InSAR应用需要有效的空间插值.
研究的目的:
- 提出和验证一种新的方法,用于使用反向传播 (BP) 神经网络和GNSS顶点总延迟 (ZTD) 来空间预测InSAR热层延迟阶段.
- 评估拟议的BP + GNSS方法的准确性和校正性能,与传统技术和其他已确定的方法相比.
- 为了证明BP+GNSS方法在生成高分辨率的InSAR热层延迟地图方面的能力.
主要方法:
- 对InSAR.com的常见热层延迟校正方法的审查.
- 开发一个空间预测模型,将BP神经网络与GNSS ZTD数据集成在一起.
- 使用洛杉矶上空的42个Sentinel-1干扰图进行验证,将BP + GNSS与常规插值,GACOS和其他使用标准偏差 (StaD) 和结构相似性指数测量 (SSIM) 的方法进行比较.
主要成果:
- 与传统的插值相比,BP+GNSS方法实现了空间预测中平方根误差 (RMSE) 的95.50%降低.
- 在SAR中,使用BP+GNSS进行干扰图校正,在92.86%的样本中,平均StaD降低了52.03%,优于其他方法.
- 该方法在各个季节都表现出色且稳定,特别是在秋季和冬季表现优于GACOS,在复杂的地形中SSIM较低.
结论:
- 该BP+GNSS方法提供了一个高度准确和高效的解决方案,用于空间预测INSAR热层延迟.
- 这种方法有效地减轻了大气相屏错误,显著提高了InSAR数据质量,特别是在具有挑战性的地形区域.
- 该BP+GNSS方法使得能够生成高时间和空间分辨率的InSAR热层延迟地图,解决InSAR技术的一个关键局限性.
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