山崩
干涉测量
预处理器
遥感
干涉合成孔径雷达
理论(学习稳定性)
地质学
计算机科学
大地测量学
数据预处理
合成孔径雷达
数据挖掘
地震学
物理
人工智能
光学
机器学习
作者
Davide Notti,Gerardo Herrera,Silvia Bianchini,Claudia Meisina,Juan Carlos García-Davalillo,Francesco Zucca
标识
DOI:10.1080/01431161.2014.889864
摘要
In this work, we present a methodology for improving persistent scatterer interferometry (PSI) data analysis for landslide studies. This methodology is a revision of previously described procedures with several improved and newly proposed aspects. To both evaluate and validate the results from this methodology, we used various persistent scatterer (PS) datasets from different satellites (ERS – ENVISAT, Radarsat, TerraSAR-X, and ALOS PALSAR) that were processed using three PSI techniques (stable point network – SPN, permanent scatterer interferometry – PSInSAR™, and SqueeSAR™) to map and monitor landslides in various mountainous environments in Spain and Italy. This methodology consists of a preprocessing model that predicts the presence of a PS over a certain area and a post-processing method used to determine the stability threshold, project the line of sight (LOS) velocity along the slope, estimate the E–W and vertical components of the velocity, and identify anomalous areas.
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