nsity according 
mage 
t applications of 
e classification 
| values in each 
the watermark 
icy. In order to 
lassification, the 
ed classifiers is 
ermarked image 
own in figure 7 
ce of percentage 
watermark into 
lassification. 
3 4 
342 1151 
13.08 13.81 
13.07 13.73 
0.01 0.03 
7 8 
-1059 327 
5.88 13.70 
390 13.69 
-0.03 0.01 
4.4 Comparative Analysis with Formosat-2 Image 
The purpose of this experiment is to test the performance of the 
proposed watermarking algorithm on different kind of satellite 
images with different spatial resolution. The test image in this 
experiment is captured by Formasat-2, the image size is 2000x 
2000 with ground resolution of 2 m . The watermark content 
and parameters are the same with the previous testing of 
WorldView-2 satellite images. Figure 7 shows the circular areas 
of the Formosat-2 image selected from the characteristic scale 
of the feature points, and only four circular areas are used to 
embed watermark. The relationship between number of texture 
sensitivity, deformation, intensity, and robustness of the 
watermark are listed in Table 5. In table 5, the original 
threshold for the WorldView-2 images does not suit the 
Formosat-2 images, resulting in each block of texture sensitive 
value is too large, although the intensity and robustness after the 
attack has performed extremely well, but the intensity of the 
embedded watermark is too large, image distortion after the 
watermarked is very bigger. According to this characteristic, we 
adjusted the critical value of the watermarking algorithm, and 
adjust the embedded watermark intensity according to Table 6. 
The deformation and robustness of watermarked as shown in 
Table 7, although the intensity and robustness of the 
performance is slightly lower, but the deformation amount of 
improvement is significant. 
  
  
  
  
  
No. of Circular Area 1 2 3 4 
Number of Texture Sensitivity| 561 375 232 300 
PSNR 35.84 36.72 33.19 3723 
NC of No Attack 1 0.998 1 0.998 
NC of 3*3 Smoothing 0.735 0757 0786 0853 
  
Table 5. Deformation and robustness of various texture 
sensitive values of the Formosat-2 image after embedded 
watermark 
  
  
vised image 
nage 
) 
al image and 
Number of Texture Sensitivity 1-50 51-100 2100 
  
  
  
  
Watermark Embedding Strength 1.5 1 0.3 
  
Table 6. Adjust the watermark embedding intensity according to 
the texture sensitive values of the Formosat-2 image 
  
  
  
  
  
No. of Circular Area 1 2 3 4 
Number of Texture Sensitivity 277 150 70 116 
PSNR 38.47 39.38 40.33 40.18 
NC of No Attack 0.988 0.968 0.992 0.941 
NC of 3*3 Smoothing 0.665 0:662 0.739 0.675 
  
  
  
  
  
Table 7. Deformation, robustness of adjust the watermark 
embedding intensity according to the texture sensitive values of 
the Formosat-2 image 
Figure 8. Circular areas of the Formosat-2 image selected by 
characteristic scale and dominant gradient orientation of 
Keypoints 
5. CONCLUSION AND FUTURE GOALS 
In this study, a novel watermarking algorithm based on the 
scale-space feature points is proposed for remotely sensing 
images. The proposed watermarking algorithm has been tested 
on WorldView-2 and Formosat-2 image set. The results show 
that the proposed method can resist most of the attacks caused 
by the follow-up image processing, such as the image 
compression, brightness and contrast adjustment. In addition, 
thet embedding watermark into remotely sensing images only 
has slight influence on image classification accuracy. 
Furthermore, the experiment results also show that the 
watermark robustness can be preserved when simple geometric 
processing is performed on the satellite images with 
watermarking embedded. However, the extracted watermark 
after polynomial transformation is unidentifiable and has a 
small NC value. This indicates that this watermarking algorithm 
still not have the ability to resist the polynomial geometric 
correction. In the future, the watermark algorithm will be 
improved to make it more suitable for the copyright protection 
of remotely sensing images 
6. REFERENCES 
Barni, M., F. Bartolini, V. Cappellini, E. Magli, and G. Olmo, 
2002. Near-lossless digital watermarking for copyright 
protection of remote sensing images, Proceedings of the 
IGARSS 2002, Toronto, Canada, Vol. 3, pp. 1447-1449. 
Chen, Q., P. P. Xie, W. J. Ma, W. Y. Wei, and L. H. Ai, 2010. 
A digital watermarking algorithm based on characters of the 
remote-sensing imagery, International Conference on MASS 
2010 , Wuhan, China, pp. 1-4. 
Hsu, P.-H. and C.-C. Chen, 2011. Study and analysis of digital 
watermarking for photogrammetric images", Proceedings of 
32th Asian Conference on Remote Sensing, Taipei, Taiwan, 
TS4-9. 
Kbaier, L, and Z. Belhadj, 2006. A novel content preserving 
watermarking scheme for multipectral images, Proceedings of 
the ICTTA 2006 , Damascus, pp.322-327. 
Tang, C. W., and H. M. Hang, 2003. A feature-based robust 
digital image watermarking scheme, IEEE Transactions on 
Signal Processing, 51(4), pp. 950 — 959. 
Ziegeler, S. B., H. Tamhankar, J. E. Fowler, and L. M. Bruce, 
2003. Wavelet-based watermarking of remotely sensed imagery 
tailored to classification performance, Proceedings of the IEEE 
Workshop on Advances in Techniques for Analysis of Remotely 
Sensed Data, Washington D.C., pp. 564-579.