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Among others, SIFT(Scale Invariant Feature
Transform) is one of the most widely used method
for point feature detection. SIFT algorithm is known
to be steady and resistant to geometric deformations
and illumination changes for partial target matching
and recognition. When the feature points have good
lamination changes for partial target matching and
recognition, then feature points has stability in terms
of geometrical variation. However SIFT algorithm
suffers from a drawback of consuming a significant
amount of time for searching and calculating the
matching points. Hence SURF algorithm has been
proposed as a means to replace the SIFT by
simplifying complicated SIFT algorithm while the
general feature of high accuracy and stability are
well preserved. It is asserted that SURF is several
times faster than SIFT and claimed by its authors to
be more robust against different image
transformations than SIFT. In this paper, we adopt
the previous SURF algorithm for SAR change
detection. Parameter estimation and optimization are
carried out to obtain the best matching results.
1.1 SURF Algorithm
SURF is a useful tool for matching points between
different images. After SURF is performed,
RANSAC is applied to obtain optimal coordinate
conversion functions using the extracted matching
points. After ground point selection is completed
and conversion function is obtained, projective
transformation and second order polynomial
transformation is used to rearrange the remaining
pixels into the new coordinates. Projective
transformation needs four or more ground points and
second order polynomial needs minimum 4 ground
points. Higher multinomial matrix might be required
for the optimal geometrical correction depending on
the level of distortion in the images and the number
of extracted ground points.
SURF algorithm has been mainly applied to
optical image cases due to its inherent nature of
requiring high contrast of pixel values. Y.Murali has
developed a MOSAIC image stitching method based
on SURF algorithm. In other case, an input query
imaging technique is proposed that SURF feature
points detector algorithm is incorporated with the
color edge matching method.(Ryo Mitsumori, 2009)
A modified SURF algorithm is developed by
adopting the color and relative location information
of interest points.(KyungSeung Lee, 2012) However
the majority of the previous researches have dealt
with optical images are SAR images have rarely a
topic of research. This is mainly attributed to the low
level of contrast in radar image pixels and inherent
speckle noises around feature points. For this reason,
it has been considered a difficult task to apply SURF
for SAR change detection purpose. In this paper, we
have adopted higher multinomial matrix equation in
the process of applying SURF to the SAR images.
By doing this, the probability of erroneous feature
point detection is reduced and the point matching
can be better performed against target movement,
rotation and scale. After desired ground points are
selected, the remaining pixels are rearranged on the
new coordinate plane using a cubic convolution. We
show that the modified SURF algorithm can be
easily applied to the conventional SAR images. The
performance of the proposed algorithm is verified
throughout medium-to-low level resolution satellite
SAR images.
1.2 KI Thresholding
For a simple change detection purpose, image
thresholding is the most straightforward technique.
There are a number of known image thresholding
techniques suitable for different applications. Sezgin
et al, has shown that the clustering-based method by
Kittler and Illingworth (1986) provides the most
reliable thresholding result in their experiments. This
technique has been called KI thresholding and is
performed as: Step 1. Choose an arbitrary initial
threshold T. Step 2. Compute priori probability,
mean value, variance value. Step 3. Compute the
updated threshold. Step 4. Compare old threshold
with updated threshold. KI thresholding technique
has been widely applied in the past since thanks to
its robust performance. In this paper, we adopted KI
technique variation for the purpose of change
detection between two different SAR images.
2 EXPERIMENT RESULTS
In Fig. 1 and Fig.2 are shown Radarsat-1,2 images
taken over Vancouver respectively and their
characteristics are described in Table 1 and 2. SAR
images can be seen totally different depending upon
the sensor position and viewing angle over the same
scene. In this case, we have attempted to detect
changes for Radarsat-1 and Radarsat-2, which are
distinguished by different resolutions and their
performances are compared later. Since the scene
coverage is different from each other, regions of
interest are extracted first. Then SURF algorithms
are applied to both images to find out the common
feature points that matched with each other.
Sar Image Change Detection using Surf Algorithm