从INSAR包装的干扰图直接学习地面移位信号.
Lama Moualla1,2, Alessio Rucci3, Giampiero Naletto4
1Center of Studies and Activities for Space (CISAS) "G. Colombo", University of Padova, Via Venezia 15, 35131 Padova, Italy.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
机器学习模型现在可以使用干涉测量合成孔径雷达 (InSAR) 数据自动检测地面位移,改善早期地缘危险风险识别. 科西诺K-最近邻近模型显示高精度,即使在新区域,展示了强大的地缘危险监测潜力.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 对地位移的监测对于早期地缘危险检测至关重要.
- 干涉测量合成孔径雷达 (InSAR) 提供亚毫米精度,但需要专业知识和复杂的数据处理.
- 从InSAR数据直接指示地面位移的自动化系统是非常可取的.
研究的目的:
- 评估使用机器学习算法用于从InSAR数据中自动检测地面位移的可行性.
- 为了比较不同机器学习模型在分类地面运动模式中的性能.
- 评估训练模型在不同地理区域的通用性和稳定性.
主要方法:
- 使用 Sentinel-1 InSAR 数据,包括过包装干扰图和连贯性图.
- 将高通波器应用于干扰图,以隔离位移信号.
- 训练并测试机器学习模型 (包括Cosine K-近邻) 使用标记的像素表示不同的地面运动速度.
- 纳入伪标签以提高模型通用性,并对意大利,葡萄牙和美国的数据进行了测试.
主要成果:
- 机器学习模型成功地确定了与缓慢和快速地面运动相关的模式.
- 科西因K-近邻模型实现了最高的测试准确性.
- 模型在邻近区域的测试集上表现良好,表明可通用性.
- 最低的测试准确度达到80.1%.
结论:
- 使用来自InSAR数据的机器学习进行自动地位移检测是可行的和有利的.
- K-近邻模型显示了可靠地缘危险监测的巨大潜力.
- 开发的方法提供了一种可靠的方法来评估地面运动,即使在以前未遇到的地区.
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