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We present an application of our framework for 3-D object-centered change
detection
to combined satellite and aerial imagery. In this framework,
geometry is compared to geometry, allowing us to compare
image sets with different acquisition conditions and even different
sensors. By working in this framework,
we do not encounter the restrictions and short-comings of conventional
image-based change detection, which requires that the images being
compared have similar acquisition geometry, photometry, scene
illumination, and so forth.
The contributions of our framework are: (1) using a geometric basis
for change detection, allowing image sets acquired under different
conditions to be compared; (2) explicit modeling of image geometry to
be able to numerically characterize significant and insignificant
change. The contributions of this paper are: (1) the algorithms are
embedded in an integrated cartographic modeling and image processing
system, which can ingest and make use of a variety of government and
commercial imagery and geospatial data products; (2) experimentation
with a variety of imagery and scene content.
Modifications to the algorithms specific to their use with satellite imagery
are discussed and the results from several experiments with both
aerial and satellite images urban domains are described and analyzed.
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