TY - JOUR
T1 - Scatterer identification and analysis using combined InSAR and laser data
AU - Hanssen, Ramon
AU - van Natijne, Adriaan
AU - Lindenbergh, Roderik
AU - Dheenathayalan, Prabu
AU - Yang, Mengshi
AU - Chang, Ling
AU - van Leijen, Freek
AU - Lopez Dekker, Paco
AU - van der Maaden, Jippe
AU - van Oosterom, P.J.M.
AU - Xiong, Hanjiang
AU - Hu, PingBo
AU - Zhan, Zhang
AU - Yang, Bisheng
PY - 2018
Y1 - 2018
N2 - The geolocation of coherent radar scatterers, used for InSAR deformation analysis, is often not accurate enough to associate them to physical geo-objects. The imaging geometry of satellite InSAR results in (i) biases in the entire point field, and (ii) quite elongated and skewed confidence ellipsoids in the range, azimuth and cross-range direction. The metric defined by the covariance matrix of the InSAR results defines the optimal way to associate scatterers with geo-objects. Laser scanning point clouds, stemming from aerial or terrestrial laser surveys, yield very dense geometry of geo-objects and topography. Here we combine InSAR and laser point clouds, taking the covariance metrics of the InSAR data into account. This enables us to correct the positions of InSAR data, to provide a geometric match with geo-objects. We demonstrate how this allows for adding contextual information as attributes to individual scatterers, which improves the interpretation of the InSAR results.
AB - The geolocation of coherent radar scatterers, used for InSAR deformation analysis, is often not accurate enough to associate them to physical geo-objects. The imaging geometry of satellite InSAR results in (i) biases in the entire point field, and (ii) quite elongated and skewed confidence ellipsoids in the range, azimuth and cross-range direction. The metric defined by the covariance matrix of the InSAR results defines the optimal way to associate scatterers with geo-objects. Laser scanning point clouds, stemming from aerial or terrestrial laser surveys, yield very dense geometry of geo-objects and topography. Here we combine InSAR and laser point clouds, taking the covariance metrics of the InSAR data into account. This enables us to correct the positions of InSAR data, to provide a geometric match with geo-objects. We demonstrate how this allows for adding contextual information as attributes to individual scatterers, which improves the interpretation of the InSAR results.
M3 - Meeting Abstract
VL - 20
JO - Geophysical Research Abstracts (online)
JF - Geophysical Research Abstracts (online)
SN - 1607-7962
M1 - EGU2018-17008
T2 - EGU General Assembly 2018
Y2 - 8 April 2018 through 13 April 2018
ER -