Volume XL-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 19-21, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W1-19-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 19-21, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W1-19-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Apr 2013

30 Apr 2013

REGISTRATION OF OPTICAL DATA WITH HIGH-RESOLUTION SAR DATA: A NEW IMAGE REGISTRATION SOLUTION

T. Bahr1 and X. Jin2 T. Bahr and X. Jin
  • 1Exelis Visual Information Solutions GmbH, Gilching, Germany
  • 2Exelis Visual Information Solutions, Boulder, Colorado, USA

Keywords: Image Registration, Multisensor, Optical Data, SAR, TerraSAR-X, Pléiades-1a, Automation, HyPARE, Algorithm

Abstract. Accurate image-to-image registration is critical for many image processing workflows, including georeferencing, change detection, data fusion, image mosaicking, DEM extraction and 3D modeling. Users need a solution to generate tie points accurately and geometrically align the images automatically. To solve these requirements we developed the Hybrid Powered Auto-Registration Engine (HyPARE). HyPARE combines all available spatial reference information with a number of image registration approaches to improve the accuracy, performance, and automation of tie point generation and image registration. We demonstrate this approach by the registration of a Pléiades-1a image with a TerraSAR-X SpotLight image of Hannover, Germany. Registering images with different modalities is a known challenging problem; e.g. manual tie point collection is prone to error. The registration engine allows to generate tie points automatically, using an optimized mutual information-based matching method. It produces more accurate results than traditional correlation-based measures. In this example the resulting tie points are well distributed across the overlapping areas, even as the images have significant local feature differences.