基于广度的新方法,用于在持久散射干涉测量处理中减少侧叶,使用空间变异的apodization
Natascha Liedel1, Jonas Ziemer1, Jannik Jänichen1
1Department for Earth Observation, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究介绍了一种基于振幅的空间变异化 (SVA) 过器,以减少合成孔径雷达 (SAR) 侧叶. 该方法保留了干扰度相,增强了持久散射干扰度 (PSI) 以准确监测水变形.
科学领域:
- 遥感 遥感 遥感 遥感
- 地质物理学 地质物理学
背景情况:
- 合成孔径雷达 (SAR) 数据通常会受到侧叶物件的影响,这可能会降低变形监测的准确性.
- 持久散射干扰计 (PSI) 是测量地面变形的一种有价值的技术,但它对振幅变化和相位扭曲很敏感.
研究的目的:
- 引入和评估基于振幅的空间变异化 (SVA) 方法,以减少SAR数据中的侧叶.
- 将SVA过器集成到Sentinel应用平台中,以实现用于水监控的斯坦福持续散射器方法 (SNAP2StaMPS) 工作流.
- 评估SVA过对持久散射物 (PS) 检测和变形测量精度的影响.
主要方法:
- 开发了一个基于振幅的SVA过器,并应用于共同注册的SAR数据的In-phase (I) 和Quadrature (Q) 组件.
- 在SNAP2StaMPS工作流中使用SNAP-Python (snappy) 实现了SVA过器.
- 该方法在来自德国索尔佩大的Sentinel-1数据上进行了测试,重点是侧叶片减少和相位保存.
主要成果:
- 在SAR数据中,SVA过成功地减少了侧叶片人工物,导致侧叶片受影响的持久分散物 (PS) 减少了39.26%.
- 基于振幅的方法保留了原来的干扰度相,确保了精确的变形值,根平均平方误差 (RMSE) 约为0.38mm.
- 将SVA过集成到SNAP2StaMPS工作流中改善了PS检测和变形监测能力.
结论:
- 新的基于振幅的SVA方法有效地减少了SAR数据中的侧边,同时保留了关键的相位信息.
- 这种方法提高了PSI用于大变形监测的准确性和可靠性,特别是在存在强大的侧面波的情况下.
- 开发的SVA过器扩展了SNAP2StaMPS工作流,为需要精确SAR干扰测量的地球科学应用提供了有价值的工具.
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