Assessing and Improving Geolocation of InSAR Scatterers with LiDAR Data
Insufficient three-dimensional (3D) geolocation accuracy of interferometric synthetic aperture radar (InSAR) scatterers on dikes often hampers the distinction between the dike crest and slopes. This limitation hinders effective monitoring and interpretation of deformation associated with different dike structures using the multi-temporal InSAR (MT-InSAR) technique. To address this, we propose a geolocation improvement method by integrating light detection and ranging (LiDAR) point cloud. We first construct and transform a 3D error ellipsoid of each InSAR scatterer by quantitatively estimating its geolocation uncertainty. Next, we design and employ a rotation matrix and projection model to extract LiDAR points located within each error ellipsoid. Then we use the mean position and height of the extracted LiDAR counterparts to improve the geolocation accuracy of the InSAR scatterers. The Houtribdijk, as our test site, is a 26.5 km long dike in the Netherlands, where we used both ascending and descending Sentinel-1 tracks acquired from 2018 to 2022. Results show that the original geocoded InSAR scatterers exhibit positional and height discrepancies between the two datasets, and their height variations fail to reflect the actual topographic features of individual slopes. After improvement, the heights of the InSAR scatterers agree well with LiDAR measurements, with root mean square errors reduced by up to 97%. Coefficients of determination with the AHN4-derived digital surface model increase from 0.22 and 0.24 to 0.82 and 0.78 for ascending and descending tracks, respectively, and reach 0.96 along the Houtribdijk. The percentage of boundary scatterers located within the dike extent also increases from 42% and 20% to 85% and 84% for the ascending and descending tracks, respectively. This test demonstrates that our method effectively improves InSAR geolocation and provides the spatial and geometric basis for deformation analysis of different dike structures.
Authors
- Zhuang Gao (ORCID: https://orcid.org/0000-0002-9001-1605)
- Zhuge Xia (ORCID: https://orcid.org/0000-0003-2982-0662)
- Jiacheng Xiong (ORCID: https://orcid.org/0000-0002-6705-6397)
- Juanjuan Yu
- Xiufeng He (ORCID: https://orcid.org/0000-0002-5262-1007)
- Ling Chang (ORCID: https://orcid.org/0000-0001-8212-7221)
Institutions
- Nanchang University (CN)
- Hohai University (CN)
- Southern University of Science and Technology (CN)
- Shanghai Micro Satellite Engineering Center (CN)
- Jiangxi Transportation Research Institute (CN)
- Jiangxi Normal University (CN)
- University of Twente (NL)
Publication Details
- Journal
- Remote Sensing
- Published
- 2026-09-21
- DOI
- https://doi.org/10.3390/rs18183249
- Primary Topic
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00