3. METHODOLOGY
The proposed framework for accuracy improvement of change
detection aims at solving the problems of illumination change
and location difference between orthoimages. After analyzing
the mutual influence of the above problems, we design the
following framework.
Firstly, the global location difference between two orthoimages
for change detection is rectified, which should be implemented
at first especially when there exists overall large location
difference. Secondly, the illumination change adjustment is
carried out to make two orthoimages have more unified
illumination condition. Thirdly, more precise rectification of
location difference in local regions is performed, aiming at
making the same object appear in the same location.
This framework for accuracy improvement is implemented
before the change detection as pre-processing, since the above
three steps improve the quality of original input data for change
detection. By implementing these steps to get more accurate
input data, more accurate detection results and less processing
time of change detection can be expected.
3.1 Global Location Difference Rectification
As it is stated in the above, change detection is carried out on
both orthoimages and DSMs from different times. For both
orthoimages and DSMs, the location difference between two
datasets may interfere with the detection accuracy of final
results. The location difference between two datasets happens
due to various different factors. For example, the aerial
triangulation data used for generating DSM from stereo images
may have different accuracy levels for two datasets. Or there are
different systematic errors coming from camera, aerial
triangulation calculation, and stereo matching.
Rather than analyze the above reasons of the location difference,
we decide to directly analyze the two datasets to find out the
location error between them. For the two datasets for change
detection, in the wide area shown in the images, most of the
parts are not changing. For example, according to [4], the
percentage of changing buildings annually in one photograph is
only 3% to 5% of the overall number of buildings in most cases.
Based on this, from another point of view, it is possible to carry
out matching on the unchanging parts in two datasets from
different time. Compared with DSM data including only height
information, orthoimages have more information with three
color channels and more characteristics are able to be extracted.
Therefore it is easier to carry out matching between two
orthoimages than DSM data. Through image matching, we
attempt to find the location difference between the parts in
respective orthoimage that describe the same area in the real
world.
Basically, orthoimages are ortho-rectified results of original
aerial images based on 3D information of DSM. Strictly
speaking, after ortho-rectification, each pixel in the orthoimage
corresponds to only one point in the real world with unique
latitude and longitude. In this sense, in the two orthoimages of
different time, two corresponding points should be in the same
location. But due to the errors stated in the beginning of this
section, there still exists some small difference between
corresponding points. At the same time, the orthoimages input
for change detection are already sampled to the same resolution
to facilitate change detection. Based on the above analysis, we
conclude on the rotation invariance and scale invariance for the
two orthoimages. The location difference between them can be
simply described as the shift in X and Y direction.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
We utilize a global matching method to obtain the overall
location difference for the whole orthoimage. The computing
scheme is described as follows. For two orthoimages ;, and 7,
with the same size, any of them can be selected as the
comparison target image, for example; . Then initially we put
1, and 7 totally overlapped with each other, and then shift; in
both X and Y directions in a defined certain range, [-r, r]. For
each shift position the matching cost is computed and the
position with the minimum matching cost is decided as the
global location difference between two orthoimages.
Y lg m, j* n- GJ)
arg min, — (D
me[-r,r] N
mn
ne[-r,r]
where g,(i, j) ^ intensity of pixel (; j) in 7,
gl(i+m,j+n) = intensity of the pixel in shifted I,
that corresponds to (j, ;) of 1,
N m, "count on the pair of corresponding pixels in
shift position of 71,7
m,n = shift position of 7,
[=r,r] = shift range of m and n
Experimental results of the global matching method show that
compared with original datasets the location difference over the
whole image is reduced. Especially for the orthoimage showing
relatively flat land, there is almost no location difference after
the global rectification. In comparison, for the orthoimage
including height changing landform, we find that for the regions
with various altitudes, there are still remaining location errors,
respectively different in each region. This phenomenon happens
because for different altitude levels, the ortho-rectification
amount on the original images is different, which results in
different location difference. To solve this problem, a local
matching method is further proposed later.
3.2 Illumination Change Adjustment
For change detection, the original images are taken under
different conditions like the season, the weather and the
shooting time in a day, which result in different illuminations in
the images. Even for the same rooftop, its color may appear in
quite different ways in the orthoimages of two different times.
And this phenomenon leads to many wrong detections of color
change. In order to solve this problem, we decide to unify the
illumination of the two orthoimages for change detection.
There have been many methods to analyze the illumination
model of the sun for the aerial images according to the shooting
season, the shooting time in the day and sometimes the
characteristics of the rooftop material for light. To save the
efficiency of the whole processing, rather than such complex
model analysis, we decide to undertake the color transfer, to
only adjust the color tone of one orthoimage to make it look like
another orthoimage.
Here we utilize the method of histogram matching [5], which
adjusts each color channel based on the global image statistics.
In details, for each channel, the following function is designed
to transfer each intensity value in the source image to the target
image.
