b
Figure 5. Co-registration between the diagonal QuickBird
and off-diagonal IKONOS subimages, overlaid in a
checkerboard mode; A zooming-in window is attached for a
close look at the seams.
Affine transform, (.83-pixel mean
145 + FPS warping, 9,83-pixel mcan
1.00
2,95
à 9
0.85
Distance RMS error (pixel)
0.75
3.70
4 8 12 16 20 24 28
Times for random sampling
Figure 6. Registration accuracy of high-resolution satellite
images in radial RMSs; while the number of control and
check points did not change, their spatial distribution altered.
Both the affine and TPS algorithms work equally well, at a
10 percent significance level.
4. CONCLUSIONS AND OUTLOOK
Both the RANSAC and IDS processings are aimed at an
exclusion of gross errors, with RANSAC being the preferred
approach because it is a fast technique in handling huge datasets.
If two images look dissimilar in brightness, the SIFT processing
may be replaced by a technique based on between-edge cost
minimization. Our algorithmic development takes full
advantage of the complementary characteristics. Practically, it
is meaningful to continue experimentation to learn under which
circumstances the TPS methodology can reveal its merits.
Nonetheless, trials with the current computer program version
to co-register optical Formosat-2 and radar ERS-2 images were
poor and thus unworthy of noting. This is a persisting challenge
that awaits to be overcome. Consequently, in a generalized term,
one of our research goals is to devise a method that allows us to
efficiently conduct digital interpretations regarding the fusion
between heterogeneous images and line maps.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
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