Improving geometric accuracy of optical VHR satellite data using Terrasar-X data

The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This precision has been reached fully automatically without any human interaction and is due to good sensor calibration, high accuracy of satellite position and the low dependency on the satellites attitude solution. High resolution optical images from space don't show this high geometric precision and need further ground control information, which is mainly due to insufficient attitude knowledge. Therefore TerraSAR-X data can be used as ¿ground control¿ to improve the exterior orientation and thereby the geometric accuracy of orthorectified optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using adapted mutual information (MI) and to estimate improvements for the exterior orientation or Rational Polynomial Coefficients (RPCs). To be able to use this intensity based method, the radar data have to be filtered before starting the matching procedure. Through adjustment calculations falsely matched points are eliminated and an optimal improvement for the attitude angles is found. The optical data are orthorectified using these improvements and the available DEM. The results are very promising and compared using conventional ground control information from maps or GPS measurements.

[1]  Peter Reinartz,et al.  Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Richard Bamler,et al.  Using geometric accuracy of TerraSAR-X data for improvement of direct sensor orientation and ortho-rectification of optical sattelite data , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[3]  P. Reinartz,et al.  On the Possibility of Intensity Based Registration for Metric Resolution SAR and Optical Imagery , 2009 .

[4]  Paul C. Bresnahan,et al.  GEOMETRIC PRECISION IN SPACE RADAR IMAGING: RESULTS FROM TERRASAR-X , 2009 .

[5]  David Small,et al.  Geometric validation of TerraSAR-X high-resolution products , 2008 .

[6]  Peter Reinartz,et al.  Application of generalized partial volume estimation for mutual information based registration of high resolution SAR and optical imagery , 2008, 2008 11th International Conference on Information Fusion.

[7]  Peter Reinartz,et al.  AUTOMATIC PRODUCTION OF A EUROPEAN ORTHOIMAGE COVERAGE WITHIN THE GMES LAND FAST TRACK SERVICE USING SPOT 4/5 AND IRS-P6 LISS III DATA , 2007 .

[8]  Jordi Inglada,et al.  Analysis of Artifacts in Subpixel Remote Sensing Image Registration , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Peter Reinartz,et al.  Accuracy analysis for DSM and orthoimages derived from SPOT HRS stereo data using direct georeferencing , 2006 .

[10]  K. Jacobsen.  Pros and cons of the orientation of very high resolution optical space images , 2006 .

[11]  P. Reinartz,et al.  DSM and Orthoimages from QuickBird and Ikonos Data using Rational Polynomial Functions , 2005 .

[12]  Peter Reinartz,et al.  EVALUATION OF SPACEBORNE AND AIRBORNE LINE SCANNER IMAGES USING A GENERIC ORTHO IMAGE PROCESSOR , 2005 .

[13]  Pramod K. Varshney,et al.  Performance of mutual information similarity measure for registration of multitemporal remote sensing images , 2003, IEEE Trans. Geosci. Remote. Sens..

[14]  Pramod K. Varshney,et al.  Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation , 2003, IEEE Transactions on Medical Imaging.

[15]  Jeffrey Tsao,et al.  Interpolation artifacts in multimodality image registration based on maximization of mutual information , 2003, IEEE Transactions on Medical Imaging.

[16]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[17]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[18]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[19]  Guy Marchal,et al.  Automated multi-moda lity image registration based on information theory , 1995 .

[20]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .