(b)
Figure 6. Geometric correction of watermarked image : (a)
Selected points on watermarked image, (b) Selected points on
digital map
(a) - (b)
Figure 7. (a) NC(=0.899) of RST (b) NC(=0.307) of polynomial.
4.2 Analysis of Texture Sensitivity and Watermark
Intensity
Table 1 shows relationship between the number of texture
sensitivity and PSNR and NC. One may notice that No. 2 has
the maximum number of texture sensitivity. When embedding
the watermark with the fixed intensity previously designed, the
deformation of No. 2 is the largest (with smallest value of
PSNR) comparing to the others, however the NC values for
different attacks are the highest when extracting the watermark.
On the other hand, No. 3 has the smallest number of texture
sensitivity, the deformation is smaller when embedding the
watermark with the same intensity previously designed.
However, the small NC values also indicate the reduced
robustness of watermark. According to this phenomenon, an
adaptive satellite image watermarking algorithm based on the
number of texture sensitivity has been proposed by adjusting
the watermark intensity. The adjusted value of watermark
embedding intensity is showing in Table 2, the deformation and
robustness results of embedded watermark are shown in Table 3.
The results show that satellite image distortion with a lower
amount after embedding watermark, using the texture sensitive
value to adjust intensity can get more robust and lower
deformation results.
No. of Circular Area 1 2 3 4
Number of Texture Sensitivity | 148 154 33 74
PSNR 41.15 40.58 41.75 42.00
NC of No Attack 0.991 0.992 0.994 0.998
NC of 3*3 Smoothing 0.812 0.852 0.874 0.854
NC of RST Geometric correction|0.821 0.881 0.835 0.802
Table 3. Adjust the watermark embedding intensity according
to the texture sensitive values of WorldView-2 image
4.3 Influence on Image Classification
Image classification is one of the most important applications of
remote sensing. Because most of the image classification
methods are based on the variation of the pixel values in each
band, the change of the pixel value due to the watermark
embedding may reduce the classification accuracy. In order to
evaluate the influence of watermark on image classification, the
k-means method which is a kind of unsupervised classifiers is
performing on the original image and the watermarked image
respectively. The classification results are shown in figure 7
and table 4. One may find the greatest difference of percentage
is only 0.04%, which means that embedding watermark into
satellite images has little effects on subsequent classification.
No. of Circular Area 1 2 3 4
Number of Differences 1580 -1779 342 1151
Original Image (%) 1959 12:50 1308 1381
Watermark Image (%) 1955 1255 1307 13.73
Difference of Percentage (%)| 0.04 -0.04 0.01 0.03
Number 5 6 7 8
Number of Differences -1544 982 -1059 327
Original Image (%) 11.43. 10.02 5:88 13.70
Watermark Image (%) 1147 19.99 590 13.69
Difference of Percentage (%)| -0.04 0.02 -0.03 0.01
No. of Circular Area 1 2 3 4
Number of Texture Sensitivity 148 154 33 74
PSNR 39.70 39.03 43.36 41.51
NC of No Attack 0.994 0.998 0.975 0.998
NC of 3*3 Smoothing 0.767 0.821 0.621 0.762
NC of RST Geometric correction|0.798 0.899 0.6277 0.751
Table 1. Deformation and robustness of various texture
sensitive values after embedded watermark
Number of Texture Sensitivity 1-50 51-100 >100
Watermark Embedding Intensity 2 0.8 0.5
Table 2. Adjust the watermark embedding intensity according
to the texture sensitive values
Table 4. Effect of watermarking on unsupervised image
classification using WorldView-2 Image
(a) (b)
Figure 7. Classification results of the original image and
watermarked image
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