TY - GEN
T1 - A Physics-Based Decorrelation Phase Covariance Model for Effective Decorrelation Noise Reduction in Interferogram Stacks
AU - Zheng, Yujie
AU - Zebker, Howard
AU - Michaelides, Roger
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Here we present a physics-based decorrelation phase covariance model and discuss its role in effective decorrelation noise reduction in interferogram stacks. We test our model in both Cascadia - a rapidly decorrelating region, and Death Valley - a slowly decorrelating region, with observations collected by Sentinel-1. We find that in Cascadia, including redundant interferograms in the stack reduces phase variance from 0.28 rad2 to 0.04 rad2, while in Death Valley, both redundant and independent interferogram stacking yield phase variances of 0.10 rad2. Both observations are consistent with predictions from our model. Comparing with three existing decorrelation phase covariance models, our proposed model matches observations with the smallest average discrepancy between theory and observations - 0.017 rad2 in Cascadia and 0.066 rad2 in Death Valley.
AB - Here we present a physics-based decorrelation phase covariance model and discuss its role in effective decorrelation noise reduction in interferogram stacks. We test our model in both Cascadia - a rapidly decorrelating region, and Death Valley - a slowly decorrelating region, with observations collected by Sentinel-1. We find that in Cascadia, including redundant interferograms in the stack reduces phase variance from 0.28 rad2 to 0.04 rad2, while in Death Valley, both redundant and independent interferogram stacking yield phase variances of 0.10 rad2. Both observations are consistent with predictions from our model. Comparing with three existing decorrelation phase covariance models, our proposed model matches observations with the smallest average discrepancy between theory and observations - 0.017 rad2 in Cascadia and 0.066 rad2 in Death Valley.
KW - Covariance matrix
KW - Decorrelation noise
KW - InSAR noise reduction
UR - https://www.scopus.com/pages/publications/85101976004
U2 - 10.1109/IGARSS39084.2020.9323237
DO - 10.1109/IGARSS39084.2020.9323237
M3 - Conference contribution
AN - SCOPUS:85101976004
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 16
EP - 19
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -