Geometric Registration of High Spatial Resolution Images Based on Google Earth Image and Global DEM Data

  • J T Zang

Abstract

With the development of remote sensing technology, the
application of remote sensing technology is expanding. Before
the application of remote sensing images, geometric registration
and other preprocessing are often required for tilt correction and
projection error correction, which often requires the selection of
ground control points. Given the lack of measured ground
control points, the correction accuracy will be greatly limited. In
this paper, a remote sensing image orthographic correction
process based on Google earth and Global DEM is proposed.
First, in ENVI5.3, the image to be corrected and the reference
image (Google Earth image) were automatically matched with
the corresponding ground object points to obtain the coordinate
file of the correction control point (.pts), and the coordinates
were converted to plane coordinates. Under the ArcGIS10.2
platform, the data of the coordinate table of the correction
control points were converted into the ArcGIS point file (SHP).
Finally, the point file was spatially superimposed with the
elevation data of Global DEM to obtain the elevation value, and
then the ground control point file with elevation value was
obtained, and then the orthographic correction with control
points was carried out. The result showed that compared with
the orthophoto correction without control points, the processing
process adopted in this paper can improve the accuracy of
correction, and the accuracy can meet the requirements of the
1:10000 land survey in the working base map. This research is
expected to provide a new method for obtaining high-quality
digital orthophoto images needed for land surveys.

Published
2020-09-28
How to Cite
Zang, J. (2020, September 28). Geometric Registration of High Spatial Resolution Images Based on Google Earth Image and Global DEM Data. Lowland Technology International, 22(2, Septemb). https://doi.org/https://doi.org/10.0001/ialt_lti.v22i2,%20Septemb.871