Figure 4: RGB channels of the Gram-Schmidt Fusion image
Figure 5: RGB channels of the Wavelet Fusion image
Figure 6: RGB channels of the Segmentation-based Fusion image
5—2 Profiles
The progression of greyvalues along profiles represents a value-
able criterion for the evaluation of pansharpening methods. Par-
ticularly critical are profiles where the visible and infrared chan-
nels strongly diverge. (Hirschmugl et al. 2005) report for some
fusion methods even trend reversals in the greyvalue progression.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
In Figures 7, 8 we consider two profiles in the original HyMap
data and three pansharpening methods. For each profile of a pan-
sharpened image we represent three visible (RGB) and one in-
frared channels, viz. 454nm, 544nm, 635nm and 1500nm wave-
lengths. The infrared channel is represented in pink in each case.
(a) Location of Profile 1 in the RGB
image
oom *e^ 1 asl
À 5 3 29
REN a Escation
(c) Profile of the original hyper- (d) Profile ofthe Gram-Schmidt Fu-
spectral image sion image
(e) Profile of the Wavelet Fusion (f) Profile of the Segmentation-
image based Fusion image
Figure 7: Profile 1 for different Fusion Methods
The first profile (Figure 7) extends over a street which is on the
left side seamed by small bushes and by trees at the right. As
the RGB and the hyperspectral data have been registered at dif-
ferent seasons, the respective profiles show a different behaviour:
The RGB image was registered at early springtime, so there was
only little foliation on the trees; the corresponding profile shows
a gradual decay from left to right. The hyperspectral data were
recorded in summer, the foliation was fully developed and the
trees overhanging to the street; the profile shows a more abrupt
decay already in the middle of the road. In particular the Gram-
Schmidt Fusion, but to a lower extent also the Wavelet Fusion re-
flects the behaviour of the RGB image, because the overall bright-
ness is (completely or partly) adapted from there. The profile
of the segmentation-based method, on contrary, features sharper
edges between road and vegetation; as the vector data reflect the
actual border of the road on the ground, the segment extends fur-
ther to the right than the greyvalues in the images suggest, and
the low power of the inverse distance interpolation, which gives
relative high weight to far data points, provides an extension of
the characteristic “road signature” to the right. Gram-Schmidt
and Wavelet fusion feature both strong oscillations which are ob-
viously also inherited from the RGB image and which affect all
four channels in the same way; the segmentation-based interpo-
lation on contrary exhibits a strong smoothing effect within the
segments.
