Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering
Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering
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DOI：
10.1016/j.jag.2023.103276
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发表时间：
2023-04
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影响因子：
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通讯作者：
David Festa;A. Novellino;Ekbal Hussain;L. Bateson;N. Casagli;P. Confuorto;M. D. Soldato;F. Raspini
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文献类型：
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作者：
David Festa;A. Novellino;Ekbal Hussain;L. Bateson;N. Casagli;P. Confuorto;M. D. Soldato;F. Raspini
需要实施能够优化地球观测数据使用的高效增值工具，迫使科学界为地球观测信息的下游寻找创新解决方案。本文提出了一种基于主成分分析（PCA）和K均值聚类的无监督自动化方法，用于从干涉合成孔径雷达（干涉合成孔径雷达）时间序列中检测自然或人为地表形变模式。对于我们的概念验证，我们将重点放在瓦莱德奥斯塔地区（意大利西北部），那里经常发生大规模浪费过程，与人类活动和基础设施相互作用。Sentinel-1产生的大量数据允许从视线（LOS）干涉合成孔径雷达测量的多几何数据融合中检索水平和垂直时间序列。在数据降维和特征提取之前，通过主成分分析结合上升/下降干涉合成孔径雷达数据和插值位移在不同的时间步长的额外好处在这里探索。检索到的主成分作为一个连续的解决方案，在K-均值聚类方法中的聚类成员指标，允许定义空间和时间相干的位移现象。然后，地面变形集群的信号被分解成潜在的趋势和季节性分量，以提高分类的卫星干涉合成孔径雷达特征的可解释性。利用2014-2020年的干涉合成孔径雷达时间序列数据，该方法检测了几个斜坡运动和人为变形的线性和季节性位移行为。结果表明，我们的可转让的方法的自动地面运动分析系统的发展的潜在适用性。
The need for implementing efficient value-adding tools able to optimise Earth Observation data usage, compels the scientific community to find innovative solutions for the downstream of Earth Observation information. In this paper we present an unsupervised and automated approach based on Principal Component Analysis (PCA) and K-means clustering to detect patterns of natural or anthropogenic ground deformation from Interferometric Synthetic Aperture Radar (InSAR) Time Series. For our proof-of-concept, we focus on the Valle d’Aosta region (Northwest Italy) where mass wasting processes frequently occurs, interacting with human activities and infrastructures. The large volumes of Sentinel-1 data produced allows for retrieving horizontal and vertical Time Series from multi-geometry data fusion of Line-of-Sight (LOS) InSAR measurements. The added benefit of combining ascending/descending InSAR data and interpolating displacements in time at different time steps is here explored prior to data dimensionality reduction and feature extraction through PCA. The retrieved principal components serve as a continuous solution for cluster membership indicators in the K-means clustering method, allowing to define spatially and temporally coherent displacement phenomena. The signal of the ground deformation clusters is then deconstructed into the underlying trend and seasonality components to enhance the interpretability of the classified satellite InSAR features. Using InSAR Time series data spanning 2014–2020, the proposed approach detects several slope movements and anthropogenic deformations with both linear and seasonal displacement behaviours. The results demonstrate the potential applicability of our transferable approach to the development of automated ground motion analysis systems.