The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 215–221, 2014
https://doi.org/10.5194/isprsarchives-XL-4-215-2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 215–221, 2014
https://doi.org/10.5194/isprsarchives-XL-4-215-2014

  23 Apr 2014

23 Apr 2014

Evaluation of an Area-Based matching algorithm with advanced shape models

C. Re1, R. Roncella2, G. Forlani2, G. Cremonese1, and G. Naletto3 C. Re et al.
  • 1INAF Astronomical Observatory, 35122 Padova, Italy
  • 2Dept.of Civil Engineering, Parma University, 43124 Parma, Italy
  • 3Centro Interdipartimentale Studi e Attività Spaziali (CISAS) – G. Colombo, University of Padova, 35131 Padova, Italy

Keywords: Space Photogrammetry, DTM Reconstruction Accuracy, Area-based Image Matching, Image Warping

Abstract. Nowadays, the scientific institutions involved in planetary mapping are working on new strategies to produce accurate high resolution DTMs from space images at planetary scale, usually dealing with extremely large data volumes. From a methodological point of view, despite the introduction of a series of new algorithms for image matching (e.g. the Semi Global Matching) that yield superior results (especially because they produce usually smooth and continuous surfaces) with lower processing times, the preference in this field still goes to well established area-based matching techniques. Many efforts are consequently directed to improve each phase of the photogrammetric process, from image pre-processing to DTM interpolation.

In this context, the Dense Matcher software (DM) developed at the University of Parma has been recently optimized to cope with very high resolution images provided by the most recent missions (LROC NAC and HiRISE) focusing the efforts mainly to the improvement of the correlation phase and the process automation. Important changes have been made to the correlation algorithm, still maintaining its high performance in terms of precision and accuracy, by implementing an advanced version of the Least Squares Matching (LSM) algorithm. In particular, an iterative algorithm has been developed to adapt the geometric transformation in image resampling using different shape functions as originally proposed by other authors in different applications.