NSING IMAGES
City 10617, Taiwan —
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narks have high values of
gnized clearly after the
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.