In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
2. METHODOLOGY 
A new approach for the use of the correlation coefficient in 
automatic image registration was recently explored (Gonsalves 
etal., 2008). In this paper, we generalized this approach, which 
will be described in the following. In order to simplify the 
provided analysis, we will focus on the problem of finding a 
translation in both horizontal and vertical directions, assuming 
that the considered region is approximately “flat”. Considering 
(Pref ,Lref ) and (P new ,Lnew) as the (Pixel,Line) coordinates of 
the reference and new (to be registered) images, respectively, 
their relation may be expressed as 
Pnew - Pref + 8 X 
(1) 
Lnew = Lref + §y 
(2) 
where § x and 8 y are the displacements (in pixel units) on the 
horizontal and vertical directions, respectively, between the 
reference and the new image. The registration of a full scene or 
images with more complex deformations may be performed 
according to the description in (Gonfalves etal, 2008), and 
further evaluated trough a proper set of measures (Gongalves 
etal., 2009). The several steps of the proposed methodology will 
be described in the following. 
2.1 Division of the image into tiles 
As previously mentioned, in this work the focus relies on 
approximately “flat” regions. Depending on the terrain slope 
variation and on the image acquisition geometry, it may become 
difficult to avoid slight differences on the shifts throughout the 
images. Therefore, the division of the image into tiles is also 
considered in this work, to evaluate whether it may allow for 
reducing some of these remaining effects. The tiles must be 
sufficiently higher than the shift known or estimated a priori, for 
which a minimum size of 64x64 pixels up to the full image size 
(a single tile) may be generally applicable. The following steps 
are applied to each tile. 
2.2 Similarity image 
Instead of the traditional similarity surface, two similarity 
images are proposed, each one corresponding to the horizontal 
and vertical directions. The similarity image is produced by 
computing a similarity measure along one dimension at a time. 
Considering a tile with m-by-n pixels, then the similarity image 
for the horizontal and vertical directions will have n-by-n and 
m-by-m pixels, respectively. This procedure is schematically 
represented in Figure 1. The correct shift between the tiles is 
expected to produce a brighter diagonal strip on the similarity 
image, corresponding to the higher values of the similarity 
measure. An example of a similarity image is provided in Figure 
2c. 
Image 1 
Image 2 
Similarity image 
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Image 1 
Image 2 
Similanty image 
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Figure 1. Illustration of the similarity image computation in 
both xx and yy axis. 
(c) (d) (e) (f) 
Figure 2. Illustration of the main steps of the proposed 
methodology (further details in section 2): (a) A segment with 
256x256 pixels from a Landsat image; (b) a segment with 
256x256 pixels from an ASTER image; (c) similarity image 
(using the correlation coefficient); (d) filtered similarity image; 
(e) image in (d) converted to binary; (f) -45° line detected by 
the Hough transform, superimposed on the similarity image. 
2.3 Similarity image filtering 
In order to enhance the visibility of the similarity image brighter 
diagonal strip, the similarity image is filtered using a -45° 
oriented line. The length of this line is defined to be 15% of the 
image dimension norm, which may be broadly applied to any 
sensor (Gonijalves etal., 2008). The filter window is composed 
by positive values along the diagonal and zeros outside. Its 
effect is illustrated in Figure 2d. 
2.4 Conversion from gray level to binary 
Prior to the Hough transform computation, there is the need to 
convert the filtered similarity image to a binary format. A 
threshold equal to the percentile (l-3/n)xl00 (rounded to the 
smaller integer) is considered, where n is the number of lines 
(or columns) of the similarity image (which is squared). The 
binary image of the example is provided in Figure 2e. 
2.5 Hough transform 
At the Hough transform step (Hough, 1962), the 0 and p 
resolution is defined as 0.5. The Hough transform is computed 
for the similarity images in both xx and yy axis. For each of 
them, more than one line may be identified, associated to the 
most prominent peaks. 
2.6 Main diagonal identification and displacement 
computation 
The slope of the detected line(s) in the previous step is 
computed, being considered only those with slope between 
-0.95 and -1.05. In case of more than one line is detected with a 
slope of exactly -1.00, the line with highest height is selected. 
The displacement on each axis is finally obtained by computing 
the distance from the selected line to the main diagonal (-45° 
line starting at row 1 column 1). This step is illustrated in 
Figure 2f. 
2.7 Estimation of 5 X and 5 y 
In the case that the image is not divided into tiles (the image 
being itself a single tile), then the estimates for S x and 8 y are