The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
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4.3 Dense Grid Matching
4.3.1 Ground Matching: The purpose of image matching is
to extract object’s geometry information and confirm its space
position. Space front intersection should be employed to
calculate the space coordination of the corresponding points
after obtaining parallax of left and right photo through image
matching. The digital surface model is created after the ground
coordinates of corresponding points have been obtained. Some
inter methods have been employed in DSM generation, so the
precision willreduce more or less. The image matching based
on ground only needs calculating Z value because the planar
coordinates (X, Y) are known.
The vertical line locus (VLL) is adopted in the paper. If there is
a vertical line locus on ground, its projection on the photo will
also be a line (refer to fig 9). That is to say the projection of the
intersection point A of VLL must be on the parallax line on the
photo. To solute the Z value of point A on ground through
making use of VLL to search the corresponding image points
1 and
Cl-s
if
Zi = Z min +i-AZ (/ = 1,2,-)
.afX-X s ) + bfY-Y s ) + cfZ-Z s )
a i(X-X s ) + b 3 (Y-Y^+c^Zj -Z s )
.a 2 (y-y 5 ) + fe 2 (T-r 5 ) + c 2 (Z,-Z 5 )
afX-X^ + bfY-Y^ + cfZ'-Zs)
.a\{X-X s ) + b[{Y-Y s ) + c\{Zi-Z s )
afX-X^ + b'fY-Y^ + c'fZ.-Zs)
.a , 2 {X-X s ) + bI 2 {Y-Y s ) + c l 2 {Z i -Z s )
a’(X-X s ) + b' 3 (Y-Y s ) + c'fZ i -Z s )
(x„y,) => Pi
p k > Pi then Z = Z k
Figure 9, Sketch Map for Ground Matching
The ground matching is a linear searching along Z with AZ in
[Zmin, Zmax] because the planar coordinate (X, Y) is known.
The search strategy can improve the space stereo matching
success ratio.
4.3.2 Multi-level Matching: According to the characters
of the remote sensing image, multi-level matching strategy is
adopted to overcome partial image distortion and facular
infection and increase the matching speed. Image matching is a
“sick” program, that is to say there would exist no
corresponding point on one image for a given point on the other
image because of occlusion or there are more than one
corresponding points because of overlap or transparency of the
object. In order to overcome the matter and make it an “order”
program, additive condition and restrict should be imported. In
order to control the complication degree and the reliability of
the matching, it is very important to take into account the multi
level data structure and geometry restrict.
From low to high level strategy is adopted to generate remote
sensing pyramid image and do image matching frequency
divisionally. On the level 0 using single point grey matching
which has a wider drag in range. The initial Z value of each
point in the DEM is given the roughly average value of the area.
Smooth is done to the DEM after each level matching is
finished and the result is regarded as approximation value for
the next level. The search rang includes all height distributing
extent of the area. The higher level uses the results from the
former level as its initial value and using shift spherical surface
approach to forecast the result. The correlation coefficient is set
a threshold and using statistical probability model to eliminate
coarse error.
Figure 10, The Image’s Pyramid Structure
4.3.3 Matching window adjustment: It can refer to the
reference [7] as matching windows self adjustment strategy is
considered for high resolution stereo image matching.
According to the multi-spectrum info character that remote
sensing image has, the initial result which is classified from
original image can be treated as the basis of window adjustment
for the reconstruction of thematic element such as building.
Low resolution multi-spectrum (RGB+NIR) image is used and
spectrum character extraction is applied firstly, then max
similar classification is adopted to realize image auto
classifying to get original classification result [8]. The original
classification result is used as covering template to distinguish
foreground and background information. The image character
information is integrated in the matching window to determine
the image matching result and improve the matching reliability
of character indistinctive area.
(Left Image) (Right Image)
Figure 11, The Spectrum Classify Results of the Stereo Images
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