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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004
embedding algorithm of the image content-based localized
watermarks; in section 3 and 4, we respectively discussed the
choice and detection of the localized watermarks and
experimental results and oùr conclusion were presented
respectively in section 5 and 6.
2. Embedding of Localized Watermarks
In our algorithm we exploited the spacial feature points to
orient watermarks, then adaptively embedded watermarks of
different strength into the wavelet domain of the only
sub-image which corresponds with the feature point and
detected watermarks to realize localizing of watermarks. We
considered to choose those angle points (including crossed
points, Y-shape points and T-shape points) as the feature points,
because those points have good stability. After finding those
feature points, we can embed the localized watermark into the
D XD sub-image which is centered by each feature point.
2.1 Strategy of Secrete Watermarks Embedding Based on
DWT
According to the distributed character and the qualitative and
quantitative characters of breadth of wavelet coefficients, we
exploited the new strategy presented in Document (Huang
Daren, 2002a), namely watermarks should be firstly embedded
into the low-frequency wavelet coefficients and then embedded
into those high-frequency coefficients according to their
important orders if there are still surplus watermarks.
Furthermore watermarks should be embedded into the
low-frequency and high-frequency coefficients by different
embedding strength.
2.2 Adaptive Watermarking Algorithm Based on HVS
In addition to the position of watermark embedding, the
robustness of watermarks also depends on the embedding
strength of watermarks. So we should make adequate use of
vision characters, namely under the condition of invisibility, we
should rationally distribute the embedded watermarking energy
and improve the strength of the local embedded watermarking
weight as large as possible. And adaptive watermarking
algorithms are just based on this idea.
Given the above embedding strategy, in this paper we exploited
the adaptive watermarking algorithm which introduced vision
system characters into watermark embedding procedure,
namely organized anew the wavelet coefficients into wavelet
blocks, then according to the texture-hiding characters of vision
system, classified those wavelet blocks and by the classification
results, embedded the watermarking weights of different
strength into different wavelet coefficients.
2.2.1 Characters of Wavelet Blocks and the Relation with
Low-frequency Coefficients: We made wavelet decomposition
of the original remote sensing image, then each pixel is
corresponding with some blocks in the wavelet domain, the
relation among which can be denoted by a four-branch tree,
namely a wavelet sub-tree. The root of a wavelet sub-tree lies
on the low-frequency domain and we can organize all the three
four-branch wavelet sub-trees of different orientations and the
same root to form a block of the fixed size. All the blocks of the
fixed size are called as wavelet blocks.
2.2.2 Classification of Wavelet Blocks: In order to improve as
193
high as possible the watermark embedding strength, we
classified wavelet blocks into 2 classification: classification 1 is
the wavelet blocks of weaker textures, marked as s/ and
classification 2 is the ones of stronger textures, marked as s2.
The wavelet coefficients of large breadths are corresponding
with the breaking pixels in the original remote sensing image,
so the textures of a wavelet block would be stronger if it has
more wavelet coefficients of large breadths, otherwise the
textures would be weaker. Namely, if
number( | Fu. vy > Tl(uv)O W.O) 5 T2, then the wavelet
block Wy [0S Cotherwise "x €52 0 and TI T2are the
preliminarily designed fields. In this paper, we chose
T1-0,T2-45.
2.3 Embedding Algorithm of Localized Watermarks:
Ruizhen Liu has presented CBWM (Content-Based
Watermarking Model) in Document (Ruizhen Liu, 2001a)
‘Image Content-based Watermarking Model’. CBWM makes
no hypothesis to the original remote sensing image and is an
addition watermarking model and because in CBWM the
chosen watermark is orthogonal to the feature vector, it’s also
an image content-based watermarking model. CBWM is a
universal watermarking model based on frequency domain and
can be combined with any watermarking algorithm based on
frequency domain. The image content-based localized adaptive
watermarking algorithm presented in this paper is just an
adaptive watermarking algorithm which applied CBWM to
DWT of the local area (sub-image).
The watermarking algorithm presented in this paper is remote
sensing image content-based, namely each chosen local
watermarking sequence is orthogonal to the feature vector of
the corresponding sub-image, so we chose the low-frequency
coefficients of DWT as the choosing space of the feature
vectors and according to formula (1), adaptively embedded the
watermark H' into the feature vector in the low-frequency
domain. And we can exploit the texture-hiding character of
vision system by adjusting the factor (*. By a large amount of
experiments, we found: letting
dk=d, +aw, » k=1,2,,K
0.02 WkEs2
a= (1)
0.005 WkESsI
the watermarked remote sensing image would have a good
robustness. F(u,v) is the wavelet low-frequency coefficient, and
where
in our algorithm it is the weight d , of the feature vector in the
low-frequency domain and Wk is the wavelet block in which
d, lies.
Let A={ai j) € FP be the original remote sensing
sub-image and B={bi j) € FP be the adaptively
watermarked remote sensing sub-image, then the procedure of
the CBWM localized adaptive watermarking algorithm is as
follows:
