ANALYSIS OF RELIABILITY AND IMPACT FACTORS OF MUTUAL INFORMATION 
SIMILARITY CRITERION FOR REMOTE SENSING IMAGERY TEMPLATE 
MATCHING 
H.L. Wang 9 ’, R. An b , * Q. Zhang b , C.Y. Chen b 
a College of Geography and Sea Sciences, Nanjing University, Nanjing 210093; 
b Geo-informatics Department of College of Hydrology and Water Resources, Hohai University, Nanjing 210098. 
KEY WORDS: Matching, Registration, Reliability, Performance, Impact Analysis, Multi-sensor 
ABSTRACT: 
Reliability of similarity measure is a key factor for successful image matching. In this paper, mutual information is introduced to 
template image matching as a similarity measure. Its ability of adapting to different scene, overcoming the grey reversal, and factors 
which influence on the successful matching of mutual information method are discussed. These factors include signal-noise ratio, 
information content and self-similarity. The test data are remote sensing images captured by different sensors, with different spatial 
resolution and at different seasons. Some typical scenes such as city, town, village, river, stream, and field land are used to test its 
ability of adapting to different scenes and the ability to overcome the grey reversal. The experiments manifest that there is no strong 
relationship between success rate and the amount of signal-noise ratio or information content for the mutual information matching 
method. The successful matching based on mutual information is mainly affected by whether there are similar pattern areas in the 
reference image or not. The experiments also indicate that when the scene has large non-linear change of grey, the success rate of 
mutual information matching method is much greater than that of cross-correlation method. 
1. INTRODUCTION 
Template matching is essential in many image analysis and 
computer vision tasks. Reliability of similarity measure is a key 
factor for successful template matching. In conventional template 
matching methods, cross-correlation methods are often used 
because of their strong suitability to different scene, simple 
arithmetic and easily parallel calculation. However, when a big 
grey aberrance (such as grey reversal), geometrical deformation 
(such as rotation) and random noise exist in images to be matched, 
the success rate of matching is low and matching usually fails. 
Therefore, it is necessary to study more reliable similarity measure 
to boost up the ability to overcome intensity aberration and random 
noise in order to improve the success rate of matching (Brown, 
1992; Zitova et al., 2003;Su et al., 2000). 
In this paper, mutual information is introduced to template image 
matching as a similarity measure. Its ability of adapting to different 
scene, overcoming the grey reversal, and factors which influence 
on the successful matching of mutual information method are 
discussed. Mutual information is a fundamental concept in the 
information theory, and it is the measure of the statistic relativity 
for two stochastic variables. When images with the same structure 
matched best, the mutual information of corresponding pixels is the 
biggest in principle (Maes, et al., 1997; Studholme, et al., 1999; 
Arlene, et al., 2003). Because mutual information similarity 
criterion doesn’t need any assumption of pixel value relationship 
between the images to be matched, and any segmentation and pre 
processing are not required before matching too, it has been widely 
applied in the matching of various kinds of images such as 
medicine images (Josien, et al., 2003). Whereas, in template 
matching, there are few articles discussed about it so far as I know, 
so it is a valuable topic to be discussed farther. 
The factors, which affect the successful matching of mutual 
information method, are also analysed. These factors include 
signal-noise ratio, information content and self-similarity. The 
relationship between these factors and success rate of matching are 
presented. The success rate of mutual information and cross 
correlation based methods are compared. Some beneficial 
conclusions are drawn. 
The rest of the paper is organized as follows. The basic principle of 
mutual information is described in Section II. The test data and 
matching method are described in Section III. Reliability and 
impact factors of mutual information similarity criterion are 
discussed in detail in section IV. Finally, some conclusions are 
drawn in Section V. 
2. MUTUAL INFORMATION SIMILARITY METRIC 
Mutual information (MI) is a concept developed from information 
theory. It indicates how much information one random variable 
tells about another. The MI registration criterion can be thought of 
as a measure of how well one image explains the other. It is applied 
to measure the statistical dependence between image intensities of 
corresponding pixels in both images, which is assumed to be 
maximal if the images are geometrically aligned; therefore, it can 
be regarded as the similarity measure in image matching (Maes, et 
al. 1997; Viola, et al.1997; Arlene, et al. 2003; Josien, et al. 2003). 
It has been widely applied in the matching of various kinds of 
images such as medicine images that obtained with different mode, 
and also has been used in remote sensing imagery registration 
recently (Chen,et al. 2003). 
The mutual information is denoted by information entropy as 
follows: 
‘Corresponding author. Tel.: +86 025 83787578; E-mail addresses: anrunj@yahoo.com.cn; anrunj@163.com. 
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