在地面SAR中使用空间自相关性进行大气相位错误校正的集群方法
Yaolong Qi1,2, Jiaxin Hui1,2, Ting Hou1,2
1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
使用空间自相对应的新聚类方法改善了用于地面合成光圈雷达 (GB-SAR) 矿山监测的大气相位校正. 这种技术在具有挑战性的大气条件下提高了准确性,而传统方法失败了.
科学领域:
- 地质物理学 地质物理学
- 遥感 遥感 遥感 遥感
- 地球观测 地球观测
背景情况:
- 基于地面的合成光圈雷达 (GB-SAR) 对露天矿山监测至关重要.
- 动态大气条件引入大气相位误差,降低监测精度.
- 传统的基于回归的大气相调整 (APC) 方法对于复杂的场景是不够的.
研究的目的:
- 为GB-SAR监测开发一种先进的大气相位校正 (APC) 方法.
- 在复杂的大气和地形条件下克服传统APC方法的局限性.
- 为了提高露天采矿中变形监测的准确性.
主要方法:
- 提出了一种基于APC的空间自相关函数的新集群方法.
- 干涉图被分成块,并使用空间自相关性评估相位一致性.
- 一个地区不断增长的算法分类块,然后根据统计数据进行合并.
主要成果:
- 拟议的集群方法显著优于传统的基于回归的APC方法.
- 在复杂的大气相场景中表现出卓越的性能.
- 通过对中国西北部一座露天矿的变形监测得到验证.
结论:
- 基于空间自关联的聚类方法是APC在GB-SAR监测中的优越方法.
- 该方法有效地解决了在具有挑战性的环境中大气相位错误.
- 提高了露天矿山变形监测的可靠性和准确性.
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