The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXIX-B4
https://doi.org/10.5194/isprsarchives-XXXIX-B4-301-2012
https://doi.org/10.5194/isprsarchives-XXXIX-B4-301-2012
31 Jul 2012
 | 31 Jul 2012

SYSTEMATIC BUNDLE ADJUSTMENT OF HRSC IMAGE DATA

J. Bostelmann, R. Schmidt, and C. Heipke

Keywords: Extra-terrestrial, Mapping, Planetary, Matching, Bundle, Adjustment, Global, Analysis

Abstract. The European Mars Express mission was launched in June 2003 and sent into orbit around Mars. On board the orbiter is the German High Resolution Stereo Camera (HRSC). This multi-line sensor images the Martian surface with a resolution of up to 12m per pixel in three dimensions and provides RGB and infra-red color information. The usage of the stereoscopic image information for the improvement of the observed position and attitude information via bundle adjustment is important to derive high quality 3D surface models, color orthoimages and other data products. In many cases overlapping image strips of different orbits can be used to form photogrammetric blocks, thus allowing the simultaneous adjustment of the exterior orientation data. This reduces not only local, but also regional inconsistencies in the data. With the growing number of HRSC image strips in this ongoing mission, the size and complexity of potential blocks is increasing. Therefore, a workflow has been built up for the systematic improvement of the exterior orientation using single orbit strips and regional blocks. For a successful bundle adjustment of blocks using multiple image strips a sufficient number of tie points in the overlapping area is needed. The number of tie points depends mainly on the geometric and radiometric quality of the images. This is considered by detailed analysis of the tie point accuracy and distribution. The combination of methods for image pre-processing, tie point matching, bundle adjustment and evaluation of the results in an automated workflow allows for all HRSC images a global assessment of the quality and a systematic selection of data for larger blocks.