Frequently updating DEMs based on multi-track repeat-pass InSAR observations using robust variance component estimation

• A new algorithm was proposed for frequently updating DEMs from multi-track repeat-pass InSAR data. • Robust variance component estimation was used to reduce the effect of InSAR errors on DEM update. • The accuracy and frequency of repeat-pass InSAR-based DEM update can be significantly improved. Space-borne interferometric synthetic aperture radar (InSAR) is a useful technique to generate or update digital elevation models (DEMs) over large regions. Specifical InSAR missions for DEM generation/update currently work in bistatic mode. The bistatic InSAR satellites have a low temporal coverage, causing the difficulty to keep DEM products up to date. InSAR satellites working in a repeat-pass mode can offer numerous data sources with a short temporal coverage, offering a great potential to frequently update DEMs to keep DEM valid with time. However, the accuracy of repeat-pass InSAR DEMs using the existing algorithms is too low for practical uses currently. To circumvent this, we proposed a new method to frequently update DEMs from repeat-pass InSAR datasets, in order to improve update accuracy. Firstly, multi-track repeat-pass InSAR datasets were utilized to offer more redundant observations to mitigate InSAR noises. A new quantitative model was then developed to scientifically guide the exclusion of multi-track interferograms with very short spatial baselines, in order to further reduce the propagation of InSAR errors into DEM products. Thirdly, a robust variance component estimation (RVCE) algorithm, which can adaptively weight multi-track InSAR observations and automatically exclude outliers, was used to dynamically update the DEMs. The proposed method was tested over the Hambach open-pit mine in Germany. The results show that the mean accuracy of the updated DEMs is about 8.7 m, demonstrating a 60 % improvement over classical single-track repeat-pass InSAR techniques. The proposed method offers a new option to frequently update DEMs, especially over areas with changes of surface terrain.

Authors

Institutions

Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-09-01
DOI
https://doi.org/10.1016/j.jag.2025.104821
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Frequently updating DEMs based on multi-track repeat-pass InSAR observations using robust variance component estimation

Cui Zhou, Zhiwei Li, Zhanpeng Cao, Zefa Yang
International Journal of Applied Earth Observation and Geoinformation
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Frequently updating DEMs based on multi-track repeat-pass InSAR observations using robust variance component estimation

Cui Zhou, Zhiwei Li, Zhanpeng Cao, Zefa Yang
article en

Abstract

• A new algorithm was proposed for frequently updating DEMs from multi-track repeat-pass InSAR data. • Robust variance component estimation was used to reduce the effect of InSAR errors on DEM update. • The accuracy and frequency of repeat-pass InSAR-based DEM update can be significantly improved. Space-borne interferometric synthetic aperture radar (InSAR) is a useful technique to generate or update digital elevation models (DEMs) over large regions. Specifical InSAR missions for DEM generation/update currently work in bistatic mode. The bistatic InSAR satellites have a low temporal coverage, causing the difficulty to keep DEM products up to date. InSAR satellites working in a repeat-pass mode can offer numerous data sources with a short temporal coverage, offering a great potential to frequently update DEMs to keep DEM valid with time. However, the accuracy of repeat-pass InSAR DEMs using the existing algorithms is too low for practical uses currently. To circumvent this, we proposed a new method to frequently update DEMs from repeat-pass InSAR datasets, in order to improve update accuracy. Firstly, multi-track repeat-pass InSAR datasets were utilized to offer more redundant observations to mitigate InSAR noises. A new quantitative model was then developed to scientifically guide the exclusion of multi-track interferograms with very short spatial baselines, in order to further reduce the propagation of InSAR errors into DEM products. Thirdly, a robust variance component estimation (RVCE) algorithm, which can adaptively weight multi-track InSAR observations and automatically exclude outliers, was used to dynamically update the DEMs. The proposed method was tested over the Hambach open-pit mine in Germany. The results show that the mean accuracy of the updated DEMs is about 8.7 m, demonstrating a 60 % improvement over classical single-track repeat-pass InSAR techniques. The proposed method offers a new option to frequently update DEMs, especially over areas with changes of surface terrain.

International Journal of Applied Earth Observation and GeoinformationVol. 143
Central South University of Forestry and Technology (CN), Central South University (CN)
Openalex Percentile: Top 69%
Synthetic Aperture Radar (SAR) Applications and Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.