In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
of 2.5m and present a temporal difference of around 2 years. 
The two segments are represented in Figure 3 (third row). 
As can be observed in Figure 4, the considered region for this 
pair of images presents a smooth terrain elevation, ranging from 
68m to 114m (according to the SRTM DEM). The reference 
shifts were manually obtained through the identification of 5 
conjugate points, associated to an average+standard-deviation 
of -4.6+0.9 and 0.1+0.8 pixels, for the horizontal and vertical 
directions, respectively. 
3.2 The traditional approach 
The traditional approach of AIR based on similarity measures 
mainly consists on taking a window (template) from one image 
and pass it throughout the other, aiming to find a peak on the 
similarity surface. This peak is expected to correspond to the 
correct shift (in both horizontal and vertical directions) between 
the images. The location of the template, the size of the 
template, and the associated computational cost may lead to a 
wide variety of template selections. The results presented in 
Figure 5 illustrate the effect of considering different sizes of the 
template (for the three pairs of images in Figure 3) - defined as 
a squared region with its centre corresponding to the center of 
the reference image - considering the correlation coefficient as 
the similarity measure. Although the computational time 
increases with the increase in the template size, it still presents a 
relatively fast performance (Figure 6). The ambiguous aspect 
associated to the template selection may lead to misleading 
solutions, as the results presented in Figure 5 clearly illustrate, 
in particular for the medium spatial resolution images. 
Furthermore, for the high resolution images, the traditional 
approach is not able to accurately register them. 
Figure 5. Obtained shifts for horizontal (8 X ) and vertical (8 y ) 
directions on the first and second columns, respectively, using 
the traditional approach described in subsection 3.2, applied to 
the three pairs of images presented in Figure 3 (in the same 
order from top to bottom). Dashed lines are the reference shifts. 
(c) 
Figure 6. Computational time (in seconds) associated to the 
traditional approach as described in subsection 3.2, applied to 
the three pairs of images presented in Figure 3: (a) 
Landsat/ASTER; (b) orthophoto/IKONOS; (c) 
orthophoto/ALOS. 
3.3 Application of the proposed methodology 
There is a wide variety of similarity measures which may be 
applied in the proposed methodology (Inglada and Giros, 2004). 
The correlation coefficient (CC) is one of the most used 
similarity measures regarding image registration applications, 
and its definition is widely known (Brown, 1992; Inglada and 
Giros, 2004; Zitova and Flusser, 2003). The mutual information 
(MI) of two random variables A and B can be obtained as 
(Cover, 1991) 
MI(A,B) = H(A) + H(B) - H(A,B) (3) 
where H(A) and H(B) are the entropies of A and B, and H(A,B) 
is their joint entropy. The Mi-based registration criterion states 
that the images shall be registered when MI(A,B) is maximal. 
The remaining definitions of the entropies and corresponding 
probabilities can be found in (Chen, 2003). 
The CC and MI measures were applied to the pair of images 
represented in Figure 2, considering each image as a single tile. 
The obtained similarity images for both horizontal and vertical 
directions are provided in Figure 7. It can be observed that the 
CC is clearly more adequate than MI. One of the reasons behind 
this may be the fact that we have applied cross-correlation to all 
possible lags, and used the maximum among these. This 
procedure allows for minimizing the misalignment which is 
present when computing ID correlation. For instance, when 
computing the correlation on the horizontal direction, the DNs 
values of each column from the reference image will present 
some misalignment on the corresponding column of the image 
to be registered, due to the shift on the vertical direction. 
Additionally, the CC presents a significant faster performance 
than MI. 
Based on the above mentioned experiments, in this work the 
application of the proposed methodology will rely on the CC as 
the similarity measure. The obtained results for both horizontal 
(8 X ) and vertical (8 y ) directions, with respect to the three pairs of 
images in Figure 3 are provided in Figure 8 (considering tiles of 
size 64x64, 128x128, 256x256 and 512x512 pixels).