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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part Bl. Beijing 2008 
matching method adapted to images with various grey aberrations 
and random noise. Experiments for matching validity are 
implemented by various landscapes images, including farmland, 
road, town, river, stream and city. Some images have abundance 
features, higher S/N ratio and smaller non-linear variation of pixel 
intensity. Some images have bigger variation of pixel intensity and 
even grey reversals because of these images were captured in 
different season. Also, there are some images with many self 
similar areas. Table 4 gives success rate for different scene images 
using MI and cross-correlation methods. 
Table 4. Comparison of success rates by 16 grey level normalized 
mutual information and cross-correlation approaches for different 
scene images 
Various Scene 
images 
Cross-correlation 
(%) 
16 grey levels 
normalized 
mutual 
information (%) 
Village 1 
61 
78 
Town 
85 
93 
Farmland 
28 
69 
River 
63 
81 
City 
65 
88 
Village 
10 
57 
Stream 
82 
100 
Average 
56.3 
80.9 
Experiments manifest that the success rate of MI is much greater 
than that of cross-correlation method when the scene images have 
great grey aberration and even reversal. It shows that the 
performance of MI is far excelled than that of cross-correlation in 
dissimilar scene matching. 
5. CONCLUSION 
The performance of MI has no strong relationship to S/N ratio and 
information content of images to be matched, but has a strong 
relationship to self-similar pattern in the reference image that also 
validates the theoretical essence of MI definition and accounts for 
MI has strong ability to overcome grey distortion. It is also showed 
that good matching performance can be derived even images to be 
matched have much lower S/N ratio and grey reversion. Various 
scene images are used to test the matching performance based on 
MI, and success rates are all higher. It is manifested that MI is a 
universal similarity measure and no need feature detection, pre 
processing, user initialization and tune of parameter before 
matching. It is especially suitable for dissimilar images matching 
and it outperforms greatly than cross-correlation method. 
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ACKNOWLEDGEMENT 
This research is supported by National Nature Science Foundation 
of China (No.40771137); a grant from the State Key Laboratory of 
Remote Sensing Science, Jointly Sponsored by the Institute of Re 
mote Sensing Applications, Chinese Academy of Sciences and Bei 
jing Normal University, and partially supported by 2006103269 
NNSFC