A Performance Study on Different Stereo Matching Costs Using Airborne Image Sequences and Satellite Images

Most recent stereo algorithms are designed to perform well on close range stereo datasets with relatively small baselines and good radiometric conditions. In this paper, different matching costs on the Semi-Global Matching algorithm are evaluated and compared using aerial image sequences and satellite images with ground truth. The influence of various cost functions on the stereo matching performance using datasets with different baseline lengths and natural radiometric changes is evaluated. A novel matching cost merging Mutual Information and Census is introduced and shows the highest robustness and accuracy. Our study indicates that using an adaptively weighted combination of Mutual Information and Census as matching cost can improve the peformance of stereo matching for airborne image sequences and satellite images. This contribution was selected in a double blind review process to be published within the Lecture Notes in Computer Science series (Springer-Verlag, Heidelberg). Photogrammetric Image Analysis Volume Editors: Stilla U, Rottensteiner F, Mayer H, Jutzi B, Butenuth M LNCS Volume: 6952 Series Editors: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Kobsa A, Mattern F, Mitchell JC, Naor M, Nierstrasz O, Pandu Rangan C, Steffen B, Sudan M, Terzopoulos D, Tygar D, Weikum G ISSN: 0302-9743 The article is accessible online through www.springerlink.com. 35 In: Stilla U et al (Eds) PIA11. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (3/W22)