Spatial analysis of image registration methodologies for fusion applications
2011
Data registration is the foundational step for fusion applications such as change detection, data conflation, ATR, and
automated feature extraction. The efficacy of data fusion products can be limited by inadequate selection of the
transformation model, or characterization of uncertainty in the registration process. In this paper, three components of
image-to-image registration are investigated: 1) image correspondence via feature matching, 2) selection of a
transformation function, and 3) estimation of uncertainty. Experimental results are presented for photogrammetric versus
non-photogrammetric transfer of point features for four different sensor types and imaging geometries. The results
demonstrate that a photogrammetric transfer model is generally more accurate at point transfer. Moreover,
photogrammetric methods provide a reliable estimation of accuracy through the process of error propagation. Reliable
local uncertainty derived from the registration process is particularly desirable information to have for subsequent fusion
processes. To that end, uncertainty maps are generated to demonstrate global trends across the test images.
Recommendations for extending this methodology to non-image data types are provided.
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