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多基线 InSAR 的最大概率曲面表面估计,用于山区环境中的 DEM 生产
Dehao Liang1,2, Yugang Tian1,2, Xinbo Liu1
1Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究介绍了最大概率曲面估计 (MLCSE),一种使用多基线交叉测量合成孔径雷达 (InSAR) 的新方法,以提高山区数字海拔模型 (DEM) 的准确性. MLCSE显著减少了高度误差,满足了高精度标准.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 这是一种摄影计量技术 (photogrammetry).
背景情况:
- 使用交叉测量合成孔径雷达 (InSAR) 在山地带生成精确的数字海拔模型 (DEM) 具有挑战性,因为信号与复杂的地形有关.
- 现有的InSAR方法在高浮雕和信号损失的区域中难以准确.
研究的目的:
- 开发和验证一种新的方法,即最大概率曲面估计 (MLCSE),用于提高山区的DEM准确性.
- 通过解决在具有挑战性的环境中传统InSAR技术的局限性来改善海拔估计.
主要方法:
- 使用多基线InSAR与Sentinel-1图像来获得精确的干扰度相.
- 实施了MLCSE,结合了适应性社区大小选择和本地曲面参数估计,以改进高度准确性.
- 应用了两个关键策略,以提高最大概率的海拔估计,减轻地形缓解和装饰关系的问题.
主要成果:
- MLCSE的平均误差为7.89米,其中70.32%的误差低于10米,超过其他InSAR方法.
- 在不同地形 (丘陵,山地,高山) 上表现稳定,显著减少高度误差.
- 水文分析证实了MLCSE的优势,特别是适应性社区选择,在视觉和定量评估中.
结论:
- 在山区,MLCSE为基于InSAR的DEM生成提供了显著的进步.
- 该方法的准确性达到或超过了既定标准,如DTED-2和各种1:50,000 DEM级别.
- 在具有挑战性的环境中,MLCSE为高分辨率的地形绘图提供了强大而准确的解决方案.
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