Enhancing Large-Area DEM modeling of GF-7 stereo imagery: Integrating ICESat-2 data with Multi-characteristic constraint filtering and terrain matching correction

Abstract

The integration of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data with Optical Photogrammetric Satellite Stereo Imagery (OPSSI) for Block Adjustment (BA) has emerged as a novel approach for generating large-area, high-accuracy Digital Elevation Models (DEMs). However, owing to the discrepancies between these two data platforms and the systematic errors of their sensors, errors arise in the BA fusion outcomes during the matching process of the two datasets. To tackle this issue, this paper proposes a method aimed at enhancing the accuracy of the BA process. Initially, the multi-characteristic constraint is used to filter the ICESat-2 ATL08 product to obtain control points and check points. Subsequently, the Terrain Matching Correction is applied to control points, and then integrated with the GF-7 OPSSI for BA to generate DEM. Ultimately, the check points are employed to assess the accuracy of the established DEM. Experiments in a 2,000 km² test area in the Wuding River Basin show that: (1) The inclusion of ICESat-2 data has remarkably enhanced the accuracy of DEM modeling utilizing GF-7 OPSSI, and the Root Mean Square Error (RMSE) has been reduced from the range of 5–10 m to 2–6 m. (2) Multi-characteristic constraint filtering is crucial for the identification of high quality ICESat-2 control points in flat and low relief areas. When implementing this filtering method, the established criteria should comprehensively consider both the quantity and the spatial distribution of control points to ensure optimal results. (3) Terrain Matching Correction on ICESat-2 data has effectively elevated the vertical accuracy of DEM modeling, particularly in regions with flat terrain. The RMSE of the vertical accuracy in such areas can be decreased by 1–3 m. In summary, the integration of spaceborne laser altimeter data with OPSSI holds immense significance for the production of large-scale and high-accuracy DEMs, offering a promising solution for terrain modeling and analysis on regional scales.