Abstract
Abstract. Vendor-provided rational polynomial coefficients (RPCs) are commonly used to generate digital elevation models (DEMs) from high-resolution satellite images. This results in a level of accuracy that can be improved using ground control points (GCPs). It is well known that due to the inherent bias of sensor orientation the generated DEM is distorted. In the traditional way of working, the bias is corrected by integrating GCPs into the standard processing chain. This involves additional effort, since the provision of GCPs and the measurement of their image coordinates are required.In this paper, we examine whether and how the data recorded by NASA's ICESat (Ice, Cloud, and Land Elevation Satellite) mission can be used as GCPs without measuring image coordinates. The starting point are DEMs that are generated by image matching from KOMPSAT-3 satellite images with given RPCs. We developed a point-to-surface matching method that matches the ICESat points to the DEM in order to correct the DEM and improve its precision. For the experimental investigations a KOMPSAT 3 image pair is used that covers an area of 20 by 16 km2 in the Yangsan city regions. The generated DEM has a height accuracy of about 9 m. The point-to-surface algorithm with 505 ICESat points led to an improvement of the DEM height accuracy to about 2 m.
Highlights
Photogrammetric processing of high-resolution satellite images for products like digital elevation models (DEM) requires rigorous sensor modelling or highly accurate approximations to it in the form of rational polynomial coefficients (RPCs) which are widely free from sensor orientation biases (Dial, Grodecki, 2005)
It must be ensured that only those ground control points (GCPs) are included in the point-to-surface adaptation where vegetation or other influences do not lead to deviations of the DEM from the surface of the terrain
13 national control points (NCPs) were available for use in the experiment to obtain an independent estimate of the points to the DEM transformation parameters
Summary
Photogrammetric processing of high-resolution satellite images for products like digital elevation models (DEM) requires rigorous sensor modelling or highly accurate approximations to it in the form of rational polynomial coefficients (RPCs) which are widely free from sensor orientation biases (Dial, Grodecki, 2005). As demonstrated for many high-resolution imaging satellites using RPC geo-positioning, there is ample experimental evidence that the relative accuracy to meter level is attainable These methods require one or more ground control points (GCPs). We use a point-to-surface matching algorithm that we developed for the correction of DEMs generated from KOMPSAT satellite images with given RPCs (Lee and Hahn, 2019). It must be ensured that only those GCPs are included in the point-to-surface adaptation where vegetation or other influences do not lead to deviations of the DEM from the surface of the terrain For this reason, we analyze the use of a land cover map to investigate the influence of ICESat data on the point-to-surface matching
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