针对PSInSAR技术在小数据集下地表形变估计精度不理想的问题，提出双加权低秩张量分解算法对时序干涉相位的观测数据去噪，使其更适用于较大城市场景，增强了张量分解在小数据集PSInSAR技术上的实用性。以天津地区的29景TerraSAR图像为数据源，抽取其中6～11景作为小数据集对所提算法进行验证，采用双加权张量分解与PSInSAR结合的技术估计地面沉降。实验结果显示，所提算法对小数据集PSInSAR技术估计的地表形变质量有明显提升效果。在11景图像条件下的细节形变区域，与原低秩张量分解时序算法相比，形变速率估计的误差降低约82%。

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.