Automatic Precision Correction of Satellite Images
using the Chips of Different Resolution
Yong-Jo Lim*, Moon-Gyu Kim*, Taejung Kim**, Seong-Ik Cho***,
Song-Og Park*, Ji-Hyun Shin*
Satellite Technology Research Center/ Korea Advanced Institute of Science and Technology *
373-1 Kusung-dong, Yusong, Taejon, Korea 305-701*
Tel: (82)-42-869-8629, Fax: (82)-42-861 0064*
E-mail: yjim@satrec.kaist.ac.kr,
mgkim@satrec.kaist.ac.kr *
Dept. of Geoinformatic Engineering, Inha University**
Telematics Research Division, Electronics and Telecommunications Research Institute***
ABSTRACT Precision correction is the process of geometrically aligning images to a reference
coordinate system using GCPs(Ground Control Points). In general GCP is collected manually,
using digital map, field survey (GPS surveying), rectified satellite image, and so on. In this paper,
we propose GCP collection from images acquired from different sensors. The main idea to this
paper is twofold. Firstly, the positional accuracy of higher resolution has more accurate than
lower resolution at systematic corrected level. In this regard, if we are able to finding the GCP of
lower resolution images using higher resolution images, we should improved the positional
accuracy of the lower satellite images without GCPs and man power. In addition, the satellite
images of lower resolution have wider swath width compared with high resolution images. Hence,
it is possible to reduce the number of GCPs to get precisely geometrically corrected satellite
images over the same area if we are able to utilize the GCP chips collected from lower resolution
satellite images for the precision correction of the higher resolution satellite images. In this
experiment, we used GCP chips collected from IKONOS panchromatic images of 1m resolutions
to perform precision correction of the Landsat-7 images of 15m resolutions and then we perform
precision correction of the KOMPSAT-1 EOC images of 6.7m resolutions with previously
precision corrected Landsat-7 images. In this case, since IKONOS images provide higher
positional accuracy of less than 15m CE 90% at the standard geometrically corrected level
comparing about 250m positional accuracy of Landsat-7, GCPs collected from IKONOS images
can provide enough accuracy for precision correction of Landsat-7 images. To utilize this
approach easily, this study will be applied to the automatic precision correction method developed
previously, which exploits the normalized cross correlation and the RANSAC(Random Sample
Consensus) algorithm to removed the outliers of matching results.
1. INTRODUCTION
Geometric correction is the transformation of a remotely sensed image so that it has the scale and
projection properties of a map. The precision correction is a process same with geometric
correction but it utilize the ground control points (GCP) to achieve higher geometric accuracy.
The precision correction consists of three steps: 1) GCP collection, 2) GCP registration onto the
target image, and 3) geometric correction using camera model and GCP information. The
procedure of precision correction are usually done manually and very time consuming and
laborious steps, so that there has been great demand on automation of these steps.
In general GCP is collected manually, using digital map, field survey (GPS surveying), rectified
satellite image, and so on. In this paper, we propose GCP collection from images acquired from
different sensors. The main idea to this paper is twofold. Firstly, the positional accuracy of higher
resolution has more accurate than lower resolution for systematic corrected level. In this regard, if
we are able to finding the GCP of lower resolution images using higher resolution images, we
should improved the positional accuracy of the lower satellite images without GCPs and man
power. Secondly, the satellite images of lower resolution have wider swath width compared with
high resolutions image. Hence, it is possible to reduce the number of GCPs to get precisely
geometrically corrected satellite images on the same area if we are able to utilize the GCP chips
collected from lower resolution satellite images for the precision correction of the higher
resolution satellite images.
In recent years, numerous studies have attempted to improve the results of accuracy for automatic
image registration with wavelet scheme, multi-spectral analysis[1][2][3], generic algorithm[4],
Digital Terrain Model[5] and so on. However, these techniques can not be applied directly to the
automatic precision correction because the error can be propagate globally in the case of the
precision correction. Therefore we need an effective method to remove the outliers from the
modeling. In order to overcome the outliers, we have developed the algorithm that can be
automatically extracted GCP(Ground Control Point) using RANSAC(Random Sample
Consensus) in success[6][7].
In this experiment, we will generate GCP chips from IKONOS panchromatic image and use
in-house algorithms to remove the outliers of matching results for Landsat-7 image. And then we
will precisely correct Landsat-7 image with matching result and correct KOMPSAT-1 EOC
image from previously precise corrected Landsat-7 panchromatic image and DEM.
The following section will briefly describe the characteristics of test datasets and the layout of
experiments carried out here.
