| Abstract |
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Conventional spatial imaging of the same object at
different times or with different sensing modalities often requires
the identification of corresponding points within a solid object. A
mathematically similar problem occurs in the remote hyperspectral
imaging of one scene at two widely separated time intervals.
In both cases the information can be interpreted using linear
vector spaces, and the differences in sensed signals can be modeled
with linear transformations of these spaces. Here we explore
first, how much can be deduced about the transformations based
solely on the multivariate statistics of the two data sets. Then we
solve application-specific models for each of conventional and
hyperspectral applications.
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 Additional Information
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Index Terms- Covariance equalization, Data Association, Registration, Hyperspectral Imaging, Point set matching
Citation:
A. Schaum,
"Data Association for Fusion in Spatial and Spectral Imaging,"
aipr,
p. 87,
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03),
2003
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