In: Wagner W„ Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
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the original profiles, which result is provided in the lower plots 
of Figure 10. It can be observed that a noticeable improvement 
on the similarity between the profiles from the Landsat and 
ASTER images was obtained, supported by a considerable 
increase of both similarity measures. The potential of this 
filtering stage deserves further research, in particular with 
respect to the increase in the computational complexity and 
subsequent processing time. 
CC=0.54249; Ml=1.7767 CC=0.32423; Ml=2.3112 
Figure 10. Two different profiles (left and right plots) obtained 
from the images in Figure 2: solid lines are columns 100 and 
250 from the Landsat image; dotted lines are columns 101 and 
251 from the ASTER image. The plots from the first row 
correspond to the original profiles, whereas the lower plots are 
the same profiles after low-pass filtering. The CC and MI 
similarity measures are provided above each plot. Further 
details in subsection 3.4. 
4. DISCUSSION 
The proposed methodology starts with the division of an image 
into tiles. With respect to the tiles dimension, it was observed 
that the smaller tiles led in general to more accurate results. 
This is related to the fact that when using smaller tiles, a larger 
set of shifts are obtained. Although a higher number of 
misleading shifts may be obtained, the statistical based 
procedure of outliers removal allow for focusing on a “cloud” 
of correct shifts. 
It was shown that the proposed methodology clearly 
outperforms the traditional approach of using similarity 
measures on image registration. It should be noticed that 
through the division of the image into tiles, it was possible to 
achieve a subpixel accuracy, without requiring the use of 
fractional shifts. 
Although accurate results were obtained using the CC, other 
similarity measures than the CC and MI could have been used 
and will deserve further research. In particular, the cross-ApEn 
(Pincus and Singer, 1996) which is an entropy-based measure 
will be explored in the future. 
The proposed methodology comprises some image processing 
steps, which are necessarily associated to higher computational 
costs. However, the presented computational times are far from 
being optimized, since a large number of graphical outputs 
which are produced and stored for quality assessment are totally 
unnecessary for what really cares, which is merely the 
estimation of 8 X and 8 y . Therefore, further work on this topic 
will allow for a drastic reduction of the presented computational 
times. 
The selection of the dataset segments was based on considering 
regions with slight terrain slope variations. However, there were 
still some significant slopes, which may lead to less accurate 
results. In the case that smaller tiles are used, the set of obtained 
shifts may be weighted by a cost function associated to the 
terrain height variation in each tile. This is an idea which 
deserves further research. 
5. CONCLUSIONS 
A new approach for the use of similarity measures was 
explored, which allows for an accurate registration of multi 
sensor, multi-spectral and multi-temporal pairs of remote 
sensing images. It allows for reducing the ambiguity associated 
to the traditional approach, providing robust estimations of both 
horizontal and vertical shifts. A set of local shifts may be used 
for the registration of full scenes with more complex distortions. 
ACKNOWLEDGEMENTS 
The first author acknowledges Fundaijao para a Ciencia e a 
Tecnologia, Portugal, for the financial support. 
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