PS-Insar point cloud densification using Sentinel-1 and TerraSAR-X data

ABSTRACT

The persistent scatterer interferometric SAR (PS-InSAR) was presented to overcome the temporal and spatial circumspection in the studied areas. The interferometric phase contains different phase components, with many researchers being particularly interested in the displacement component. In this article, contrary to the conventional use of this method in evaluating the displacement phase, we try to estimate the residual phase component of the topography contribution by changing master image selection criteria and spatial baseline maximization. By calculating the height error of the topography model utilized in PS-InSAR processing, also known as the height phase, it becomes possible to determine the height correction for each permanent scatterer. There is a challenge with generating 3D models of urban buildings due to the absence of acceptable point clouds. To overcome this obstacle, we used shape-completion learning methods. The training process directly transforms from discrete point cloud space to dense space by extracting the general geometric structure of the desired shape while preserving the details. The loss criterion of this network is based on the Chamfer Distance, which measures the distance between the non-dense input and dense output point clouds. We generated 30,000 individual building datasets for network training. We utilized two SAR image datasets consisting of 27 Sentinel-1 and 20 TerraSAR-X images. After evaluating the test dataset of 3D building models and comparing them with aerial LiDAR reference data, we calculated the Root Mean Square Error (RMSE) for the PS height point clouds. For Sentinel-1 VH and VV channels, the RMSE values were 6.369 m and 5.478 m, respectively. As for TerraSAR-X HH channel, the RMSE was 4.317 m.

KEYWORDS:

• PS-InSAR
• deep learning
• building
• shape completion

Acknowledgements

The authors thank the PENNSYLVANIA Geospatial Data Clearinghouse for the LiDAR dataset for Philadelphia City, the Zurich Stadt Open Data for accessing Zurich buildings’ 3D models, the ESA for Sentinel-1 and DLR for the TSX datasets.

Disclosure statement

The data supporting the findings of this study is available from the corresponding author upon reasonable request.