TY - JOUR
T1 - An Orbital Error Correction Model Based on Triplet Network and Shrunken Estimation
AU - Gao, Zhuang
AU - He, Xiufeng
AU - Ma, Zhang Feng
AU - Sha, Pengcheng
AU - Li, Xing
N1 - KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under Grant 41830110 and Grant 41804005 and in part by the National Key Research Development Program of China under Grant 2018YFC1503603. The Sentinel-1 data were provided by ESA/Copernicus.
PY - 2022/7/7
Y1 - 2022/7/7
N2 - Orbital error, one of the major error sources of InSAR observations, is characterized by long-wavelength artifacts which can downgrade the monitoring accuracy, especially for wide-swath SAR missions. In this letter, we present a novel approach for time-series orbital error correction, with an emphasis on the computational and estimation efficiency of orbital error parameters over wide-area scale scenes. The proposed method combines the temporal triplet network and shrunken estimator, which integrates L2 - with L1 -Norm regularization, also known as Lasso regularization. The rationale behind it is to first determine the initial orbital parameters by utilizing the traditional polynomial-based method in the spatial domain. Next, in order to weaken the interference of phase unwrapping errors and other undesired phase contributions, an additional correction procedure is implemented by building up a redundant triplet network in the time domain and further a shrunken estimation method. Experiments on synthetic data and real Sentinel-1 datasets covering Eastern California confirm that the presented method can better balance the accuracy and computational efficiency. The proposed approach may, therefore, be useful for the processing of emerging big data InSAR.
AB - Orbital error, one of the major error sources of InSAR observations, is characterized by long-wavelength artifacts which can downgrade the monitoring accuracy, especially for wide-swath SAR missions. In this letter, we present a novel approach for time-series orbital error correction, with an emphasis on the computational and estimation efficiency of orbital error parameters over wide-area scale scenes. The proposed method combines the temporal triplet network and shrunken estimator, which integrates L2 - with L1 -Norm regularization, also known as Lasso regularization. The rationale behind it is to first determine the initial orbital parameters by utilizing the traditional polynomial-based method in the spatial domain. Next, in order to weaken the interference of phase unwrapping errors and other undesired phase contributions, an additional correction procedure is implemented by building up a redundant triplet network in the time domain and further a shrunken estimation method. Experiments on synthetic data and real Sentinel-1 datasets covering Eastern California confirm that the presented method can better balance the accuracy and computational efficiency. The proposed approach may, therefore, be useful for the processing of emerging big data InSAR.
UR - http://hdl.handle.net/10754/679797
UR - https://ieeexplore.ieee.org/document/9817128/
UR - http://www.scopus.com/inward/record.url?scp=85134237870&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2022.3188910
DO - 10.1109/LGRS.2022.3188910
M3 - Article
SN - 1558-0571
VL - 19
SP - 1
EP - 5
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -