Journal of University of Chinese Academy of Sciences >
Small dataset PSInSAR surface deformation estimation method based on improved low-rank tensor decomposition
Received date: 2024-03-04
Accepted date: 2024-05-08
Online published: 2024-06-04
In addressing the issue of inadequate precision in surface deformation estimation using the PSInSAR technique with small datasets， this study proposes a dual-weighted low-rank tensor decomposition algorithm for denoising the observed data of temporal interferometric phases. This algorithm enhances the applicability of tensor decomposition in the context of small dataset PSInSAR technology， particularly in larger urban areas. This article utilizes a dataset comprising 29 TerraSAR images from the Tianjin region and extracts a subset of 6 to 11 images as a small dataset for validating the proposed algorithm. The combined approach of dual-weighted tensor decomposition and PSInSAR is employed for ground subsidence estimation. Experimental results demonstrate a significant improvement in the quality of surface deformation estimation using the proposed algorithm for small dataset PSInSAR technology. In the region exhibiting detailed deformation under the condition of 11 images， the error in deformation rate estimation is reduced by approximately 82% compared to the results obtained using the original low-rank tensor decomposition algorithm with the same number of SAR images.
Key words： PSInSAR; small datasets; low rank tensor decomposition; ground settlement
Xiaoyu LI , Jili WANG , Lu LI , Shiqiang LI . Small dataset PSInSAR surface deformation estimation method based on improved low-rank tensor decomposition[J]. Journal of University of Chinese Academy of Sciences, 2026 , 43(3) : 414 -421 . DOI: 10.7523/j.ucas.2024.044
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