Image registration between multi-source remote sensing images is the 
premise and basis of data fusion, the accuracy of which will directly 
influence the quality of image fusion [9]. So in order to perform 
accurate data fusion, high geometric accuracy between the images is 
needed. In this paper, polynomial rectification and bilinear 
interpolation approach was used to registering the two images. The 
QuickBird image was used as the base image. Then, the TerraSAR-X 
image was geometrically corrected based on the QuickBird image, 
and the RMSE was less than one pixel. 
3.2 Texture analysis 
The texture features extracted by gray level co-occurrence matrix 
method are affected by the chosen window size [10]. So in the 
following we would discuss the window size effect in extracting 
texture features by gray level co-occurrence matrix, and get the 
appropriate window size. 
There are several texture features obtained by GLCM [5], only seven 
of which were chosen in this study. Texture features were detected in 
four directions in 0°, 45°, 90° and 135°, then, used the mean of the 
four directions as the final texture features. This can eliminate the 
directional influence, and improve the extraction accuracy of texture 
features. To obtain the appropriate window size in extracting the 
texture features, window size of 3x3, 5x5, to 31x31, 41x41, 51x51, 
and 61x61 were tested to analyze the influences. 
3.3 Data fusion based on PCA and accuracy evaluation 
Image fusion is performed at three different processing levels 
according to the stage at which the fusion takes place: Pixel, Feature 
and Decision level [11]. In this paper, data fusion was implemented 
at a pixel level and the principal component analysis (PCA) was 
applied. PCA is a statistical that transforms a multivariate data set of 
inter-correlated variables into a set of new uncorrelated linear 
combinations of the original variables, thus generating a new set of 
orthogonal axes. 
Four indexes were used to evaluate the quality of the fusion images, 
Shannon entropy and sharpness to test the details of spatial 
information and spectral distortion and correlation coefficient to test 
the preservation of spectral information. 
Shannon entropy (as in (1)) was put forward by Shannon in 1948, 
which represents the average information abundance in the fusion 
image, larger Shannon entropy with larger information abundance. 
255 
H ==) p(i)log, p(i) 
= Mm 
where p(i) is probability which gray value is i. 
Sharpness (as in (2)) is also known as average gradient, which is used 
to describe sharpness of images, larger average gradient with clearer 
images. 
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whereAAF;(ij) is the gradient in x direction in fusion image, 
and AFj(ij) is in y direction. 
    
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
Spectral distortion (as in (3)) reflects the extent of distortion in multi- 
spectral images, larger spectral distortion with more intensive 
distortion. 
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(3) 
where A(i, j), F(i, j) represent the gray value of images 
before and after fusion respectively. 
Correlation coefficient (as in (4)) represents the similarity of 
the two images, larger correlation coefficient with better fusion. 
Obviously, the best value is 1. 
  
  
(4) 
where A(i, j), F(i, j) represent the gray value of images before 
and after fusion respectively, MeanA and MeanF are the mean value 
of images before and after fusion. 
4. RESULTS AND DISCUSSION 
4.1 Texture analysis of TerraSAR-X image 
For the seven textural measures are all obtained by GLCM, they are 
inevitably related to each other. If the correlation between them is 
high, it will lead to data redundancy. Also, the different moving 
window sizes make the correlations between the textural measures 
change observably. 
With the window size increasing, correlation of textural measures of 
Mean, Dissimilarity and Entropy with others show the same linear or 
exponential changing tendency respectively (Fig.2 (a) — (g)) The 
correlation coefficient reaches maximum at the window size of 
61x61, and shows a saturated tendency. Meanwhile, Homogeneity 
and Second Moment experienced the same change from negative to 
positive correlation. Correlation of Variance and Contrast with other 
textural measures also showed the intensive tendency, but the 
correlations of the two textural measures with Homogeneity and 
Second Moment were influenced low with different window sizes, 
and the correlation coefficients were all small, respectively. 
Correlation of Homogeneity and Second Moment showed the same 
intensive tendency, the different is except the positive correlation 
between Homogeneity and Second Moment, and others all 
experienced from negative to positive. This has showed that the 
increasing moving window size makes the correlations between the 
textural measures strengthen, and lead to more data redundancy. 
Standard Deviation reflects the degree of dispersion of an image, 
bigger with more information. And also we can see from Fig.2 (h) 
that Standard Deviation of the textural measures are increasing with 
the increasing moving window size, reaching maximum at the size of 
11x11, then turn down. 
4.2 Experiments 
After analyzing above, window size of 11x11 was chosen to calculate 
the textural measures by GLCM. The method PCA was applied to 
perform fusion. The single textural measure was used to replace the 
first principal component of the QuickBird multi-spectral image. 
Then use inverse PCA to get the fusion image (Fig. 3).