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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U fM-IXN-1) 757 55
Q)
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.
1 ex
=. Liz
MxF
n
NE i S a
* Af::Xi
i 1 — AG LH
(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).
