Geo-Information based on Very High Resolution Optical Satellites


Prof. Karsten Jacobsen
University of Hannover
jacobsen@ipi.uni-hannover.de


Currently with IKONOS, QuickBird and OrbView-3 three commercially used very high resolution (VHR) optical satellites with ground sampling distances (GSD) of 1m and better are active. The number of VHR satellites will grow very soon with the announced systems IRS Cartosat-2 from India, Kompsat-2 from South Korea, EROS-B from Israel and Pleiades from France having a GSD between 0.7m and 1m. In addition the resolution will be improved by WorldView-1, WorldView-2 and OrbView-5 from USA down to 0.5m. The competition is improving the order conditions, making the data acquisition for geo-information products more economic. Of course not only the number of imaging satellites is important, also their imaging capacity. The imaging capacity is depending upon the storing and download possibilities and more important the agility and the requirement for a slow down mode. With not sufficient sampling rate or the requirement for collecting more energy because of missing transfer delay and integration – the electronic forward motion compensation – the satellites have to rotate during imaging to reduce the angular speed; this of course leads to a reduced imaging capacity.


slow down of imaging by permanent rotation of view direction slow down factor = b / a


SCENE ORIENTATION
Nearly original space images and images projected to a plane with constant height like IKONOS Geo and QuickBird OR Standard are used. The tendency goes to images projected to a plane, named by SPOT level 1B. The scene orientation has to respect the image product. All imaging satellites are equipped with a positioning system like GPS, gyros and star sensors. Based on this, the full orientation of each image line can be determined. The now available VHR sensors do allow a standard deviation of the ground coordinates better than 10m without control points. This may be sufficient for some purposes, but usually it has to be verified or improved. There are different orientation procedures in use:

  1. Rational polynomial Coefficients (RPC) – the direct sensor orientation from the satellite vendors do allow the determination of the relation between the image and the ground coordinates by a polynomial as function of the geographic ground coordinates X, Y, Z divided by another. Third order polynomials are in use, so with 80 coefficients the orientation information can be expressed. This can be improved by means of control points named bias corrected RPC method.
  2. For the scene centre or start of the scene, the view direction from the ground to the satellite is given in the header data distributed together with the images. Together with the information about the satellite orbit and the image progress, this allows the geometric reconstruction of the imaging for any ground point. Like with the preceding described method, this has to be improved by means of control points. For original images the ephemeris is given, allowing a similar scene orientation.
  3. The field of view is very small, allowing also some approximations. With the 3D-affine transformation the mathematical model of parallel projection may be used. It is not using any of the available orientation information, so at least 4 three-dimensional well distributed control points have to be used.
  4. The satellite images do have perspective geometry in the CCD-line direction, and a close to parallel projection in the direction of scan. With the direct linear transformation (DLT) a perspective model is used, also not based on pre-information about the orientation. For the 11 unknowns at least 6 well distributed control points are required.
  5. A reduced number of the RPC-coefficients can be computed based on control points. Such terrain dependent RPCs also do not use the existing orientation information. The minimal number of required coefficients can be determined with at least 6 three-dimensional well distributed control points.
In the area of Zonguldak, Turkey, the orientation of the different VHR sensor images has been compared with the different methods and a varying number of control points. The terrain dependent RPCs only have been used for a limited number of tests because the results at independent check points have been so poor, that only a warning can be given for this method. It is not possible to control the quality of the results with the residuals at control points.

The orientation based on RPC from the satellite vendors and the geometric reconstruction can be made without control points. Without bias correction, against check points, in the root mean square 6.2m differences has been shown for IKONOS images – this confirms the quality of the direct sensor orientation. The orientation with the approximations DLT and 3D-affine transformation do need at least 2 control points more than the theoretical required number to deliver acceptable results. With a high number of control points their result came also to sub-pixel accuracy. With bias corrected RPC and geometric reconstruction, starting from one control point in the average sub-pixel accuracy has been reached with slightly better results for the RPC solution. The inner accuracy of the IKONOS scenes is excellent, so a simple shift of the terrain corrected coordinates gave for both rigorous solutions even better results than a 2D-affine transformation. The location of the control points in the scene has been shown as unimportant.


IKONOS Zonguldak: Results at independent check points for the different orientation methods as a function of the number of control points



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