NSING IMAGES 
City 10617, Taiwan — 
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processing of JPEG compression, brightness adjustment and 
contrast. adjustment. However, it is not easy to identify the 
extracted watermark after the image smoothing. In addition, 
most of the extracted watermarks are identified after the 
geometric attacks such as the image rotation, scaling and 
translation (RST). Furthermore, the unsupervised image 
classification is implemented on the watermarked images to 
evaluate the image quality reduction. The results show that 
classification accuracy is affected slightly after embedding 
watermarks into the satellite images. 
2, DIGITAL WATERMARKING FOR REMOTELY 
SENSING IMAGES 
Digital watermarking for satellite imagery is the process of 
embedding visible or invisible information into the digital 
imagery which may be used to verify its authenticity or the 
identity of its owners. The embedded information can be the 
trademark, script, image chip, or any kind of digital information 
generated from the original images. A review of related research, 
and major study issues of digital watermarking for satellite 
images are described briefly as follows: 
2.1 Review of Related Researches 
Barni et al. (2002) have proposed a near-lossless watermarking 
algorithm using the discrete Fourier transform (DFT), discrete 
wavelet transform (DWT). The size of the watermark has been 
adjusted to test the intensity and robustness of watermark when 
an unsupervised classifier was performed on the satellite image. 
Ziegeler et al. (2003) have illustrated that the digital 
watermarking techniques developed for multimedia data cannot 
be directly applied to the satellite images due to the fact that the 
analytic integrity of the data, rather than perceptual quality, is of 
primary importance. Thus a DWT-based algorithm for the 
watermarking of remotely sensed images was proposed. The 
impact of watermarking by this algorithm on classification 
performance is evaluated. Kbaier and Belhadj (2006) also 
proposed a multispectral image watermarking algorithm based 
on DWT, the algorithm is robust to resist cropping and filtering 
attacks. Chen et al. (2010) have proposed a new watermarking 
algorithm based on block characteristics and discrete cosine 
transform, which reduces the impact of the watermarked 
satellite images. The proposed watermarking algorithm can 
resist image processing, such as added noise, cropping, filtering 
and compression , also can resist to geometric operate. 
22 Major Study Issues of Digital Watermarking 
The study of digital watermarking for satellite images should 
focus on the flowing issues: 
l. The embedding watermark should not affect the content 
of the original satellite images in order to preserve the 
validity on the follow-up processing, such as the image 
matching, image classification and image measurements. 
2. Geometric correction is an important process to reduce 
the satellite image distortions and establish the 
relationship between the image coordinate system and the 
geographic coordinate system. However, changing the 
pixel coordinates and image size also reduces the 
robustness of the embedded watermark. How to resist the 
attack of geometric correction will be an important issue 
of watermarking for satellite images. 
3. In general, the watermarking algorithm based on the 
spatial domain is to directly modify the pixel values. On 
the other hand, the watermarking algorithm based on the 
frequency domain embeds the watermark into the 
frequency coefficients. Therefore, it would be difficult to 
resist the attacks of the image orthorectification which 
consists of the mosaicking and color balancing. 
In this study, a novel watermarking algorithm based on the 
scale-space feature points is proposed to solve the above 
problems. The scale-space feature points are commonly 
invariant to image rotation, scaling and translating, therefore 
they naturally fit into the requirement of geometrically robust 
image watermarking. 
3. DIGITAL WATERMARKING ALGORITHM 
3.1 Image Synchronization for Watermark Embedding and 
Extraction 
The watermark is always embedded into the image according to 
a certain sequence of pixels. The only way to extract the 
watermark exactly is following the same pixel sequence. If the 
watermark embedded image suffers from the changes of rotation 
or scaling attack, the pixel sequence consequently changes and 
causes the failure of watermark extraction. To resolve this 
problem, the pixel sequence should be synchronized when 
embedding and extracting the watermark. 
In this study, we use the SIFT algorithm to find some keypoints 
distributed on the satellite image. These keypoints have the 
property of invariant on image scaling, rotation and brightness 
adjustment. Then the circular area which uses the keypoint as 
the center of the circle is selected to embed the watermark. 
When extracting the watermark, the same circular areas 
surrounding the same keypoints should be found. This can be 
done by using the dominant gradient orientation and 
characteristic scale of the keypoints. 
3.2 Selection of Watermark Embedding Area 
Using the SIFT algorithm, the keypoints and their characteristic 
scales c. can be found out in the image. The circular areas 
surrounding the keypoints which can be used to resist the 
rotation attacks are constituted by the characteristic scale of the 
keypoint: 
(x—x,) +=») = (Sc) (1) 
where S is a parameter to adjust the radius of the circular area 
according to the characteristic scale of the keypoint. Figure 1(a) 
shows the circular areas selected by the characteristic scale and 
dominant gradient orientation of the keypoint (Tang and Hang, 
2003). Since the circular areas of various keypoints are 
overlapped, 4 circular areas are selected to embed the 
watermark based on the demands on large characteristic scale 
and non-overlapping condition. In Figure 1(b) , the direction of 
blue line shows the dominant gradient orientation of the 
keypoint.