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Mapping without the sun
Zhang, Jixian

P = AxL + BxL +CxL +DxL
Blue Green Red N
Where P = approximated panchromatic
L =Top-of-atmosphere band-integrated radiance
of blue band
L = top-of-atmosphere band-integrated radiance
of green band
L r ; =top-of-atmosphere band-integrated radiance
of red band
L =top-of-atmosphere band-integrated radiance
of near-infrared band
A,B,C,D = Weighted coefficients, which can be
evaluated according to the image scene context by
iterative computing or giving a estimated constant.
The new pan-sharpening algorithm can be shown as follows
(Figure 3).
As an example, the new pan-sharpening algorithm and other
data fusion methods like HIS, PCA and UNB (Digital Globe’s
default pan sharpening algorithm) were applied to four related
multi-spectral images of a Quickbird satellite scene to extract
geographic features information (see Figure 4).
The PCA algorithm’s sharpness is too bad in scenes where
there is a lot of green vegetation; other three pan-sharpening
algorithms have a good sharpness in the vegetation. The road is
very clearly in IHS, UNB and new pan-sharpening algorithms
and is some blurry in PCA algorithm.
The HIS algorithm’s color recovery is worse than UNB and
new pan-sharpening algorithms in whole scene. The new
pan-sharpening algorithm’s color recovery is good in whole
Some tiny objects like car etc al are more clearly and easily to
observe and analyze in new pan-sharpening algorithm than in
PCA, UNB, HIS algorithms.
Figure 4. Comparison of HIS, PCA, UNB and new
pan-sharpening algorithms (from upper left to lower right)
In theory, the P/P’ (see Figure 3) should equal or very nearly to
1. A,B,C,D can work better in a scene with single
geographic features , we can evaluate the weighted coefficients
a constant according to the geographic features types. The
more convergence of P/P’, the better is the pan-sharpening
result (see Figure 5).
When the scene is full of various geographic features, the
weighted coefficients cannot evaluate a constant, the value of
weighted coefficients change with respect to the pixel of
geographic features.
Figure 5. Comparison of new pan-sharpening algorithms with
different weighted coefficients (Left: A = 0.4,B =
0.63,01.6,D=1.45; Right: A = 0.4,B = 0.6,C=1.5,D=2.0)