In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
233 
THE USE OF SIMILARITY IMAGES ON MULTI-SENSOR AUTOMATIC IMAGE 
REGISTRATION 
H. Gonçalves a ’ b *, J. A. Gonçalves a , L. Corte-Real c d 
a Departamento de Geociências, Ambiente e Ordenamento do Territòrio, Faculdade de Cièncias, Universidade do Porto, 
Rua do Campo Alegre s/n 4169-007 Porto, Portugal - (hernani.gonçalves,jagoncal)@fc.up.pt 
b Centro de Investigaçâo em Cièncias Geo-Espaciais, Universidade do Porto, Rua do Campo Alegre, s/n 4169-007 
Porto, Portugal 
c Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Engenharia, Universidade do Porto, 
Rua Dr Roberto Frias s/n 4200-465 Porto, Portugal 
d INESC Porto, Rua Dr Roberto Frias s/n 4200-465 Porto, Portugal - lreal@inescporto.pt 
Commission VII 
KEY WORDS: Automation, Correction, Correlation, Georeferencing, Image, Matching, Mathematics 
ABSTRACT: 
Automatic image registration (AIR) is still a present challenge regarding remote sensing applications. Although several methods 
have been proposed in the last few years, geometric correction is often a time and effort consuming manual task. The only AIR 
method which is commonly used is the correlation-based template matching method. It usually consists on considering a window 
from one image and passing it throughout the other, looking for a maximum of correlation, which may be associated to the 
displacement between the two images. This approach leads sometimes (for example with multi-sensor image registration) to low 
correlation coefficient values, which do not give sufficient confidence to associate the peak of correlation to the correct displacement 
between the images. Furthermore, the peak of correlation is several times too flat or ambiguous, since more than one local peak may 
occur. Recently, we have tested a new approach, which shortly consists on the identification of a brighter diagonal on a “similarity 
image”. The displacement of this brighter diagonal to the main diagonal corresponds to the displacement in each axis. In this work, 
we explored the potential of using the “similarity images“ instead of the classical “similarity surface”, considering both correlation 
coefficient and mutual information measures. Our experiments were performed on some multi-sensor pairs of images with medium 
(Landsat and ASTER) and high (IKONOS, ALOS-PRISM and orthophotos) spatial resolution, where a subpixel accuracy was 
mostly obtained. It was also shown that the application of a low-pass filtering prior to the similarity measures computation, allows 
for a significant increase of the similarity measures, reinforcing the strength of this methodology in multi-spectral, multi-sensor and 
multi-temporal situations. 
1. INTRODUCTION 
Multi-sensor automatic image registration (AIR) is a present 
challenge, with emphasis on remote sensing applications. Direct 
georeferencing techniques, based on navigation instruments on 
board the satellites allow for the determination of pixel 
geolocation. Bringing images to a well defined cartographic 
reference system allows for an approximate image registration 
with any other precisely georeferenced imagery. Since ideally 
image registration should be done at least at the pixel accuracy, 
improvement is needed for most satellite images. 
A wide variety of AIR methods may be found in the literature 
(Brown, 1992; Fonseca, 1996; Zitovà, 2003). However, there 
are several particularities on the registration of remote sensing 
images which justifies continuous research in this field. These 
particularities include differences in the radiometric content 
(motivated by different spectral bands and/or different sensors), 
the slope variation of the terrain covered by the image, 
differences in the image acquisition geometry, among other 
difficulties. A system which should automatically analyse all 
these aspects and select the most appropriate method or a 
combination of methods seems to be the most reasonable 
solution for the complex problem of multi-sensor AIR. 
The most popular methods for AIR are those based on similarity 
measures, where the correlation coefficient plays an important 
role (Inglada and Giros, 2004). This class of methods mainly 
consists on taking a template from an image and pass it 
throughout the other image, producing a similarity surface. The 
shift between the images is expected to be associated to a well 
defined peak on the similarity surface. However, in several 
times, the surface peak may be associated to a low correlation 
value, present a smooth peak leading to a less accurate location, 
or even erroneous peaks may be found. 
For the above mentioned facts, (Gongalves et al., 2008) 
proposed an automatic image registration method based on 
correlation and Hough transform, which allows for reducing 
these weaknesses associated to the traditional approach of 
correlation-based methods. In this work, this approach was 
further explored by also considering the mutual information, as 
well as an analysis regarding the computational time, evaluated 
for different pairs of images. The proposed methodology is 
described in section 2, and some examples of its application are 
provided in section 3. The discussion and conclusions 
correspond to sections 4 and 5, respectively. 
* Corresponding author.