After that, this algorithm deletes the feature points successfully
matched, and moves the plane to the next height position. Then
it initializes the number of projection rays pass through all grid
cells in the plane number = 0, and repeats the process above
until reaches the lowest height value position.
By moving the plane to different height positions, this
algorithm utilizes the positions of grid cells in the plane to
constraint the range of projection rays for feature points in
different images, and determines the matching candidates. This
algorithm is called as the moving Z-Plane constraint. The flow
chart of overall algorithm is shown in Fig. 2, involving the key
technology as follows:
2.2 Hierarchy Matching Strategy
Considering the occlusion problem in multi-view image
matching, and some feature points in some images are not
extracted in the process of feature extraction, our algorithm
matches all the grid cells whose number of projection rays are
above 1. The more the projection rays passing through a grid,
the more reliable matching result will be obtained. According to
the matching principle of “the best candidate will be matched in
the first instant”, this paper adopts a hierarchy matching
strategy. First, it matches the best grid cells in the plane at each
height position, namely matching the grid cells with
number > T ,and T is half of the number of matching image.
Second, it matches the second-best grid cells in the plane at
each height position, namely matching the grid cells with
2 < number <T, . In order to enhance the reliability of
matching results, this process utilizes the matching results of
the best grid cells to constraint the matching results of the
second-best grid cells.
2.3 Grey similarity constraint
For the feature points in different images whose projection rays
pass through the same grid cell in the plane, this paper selects
images that have the same feature points, and adopts the grey
similarity constraint to match these feature points. This process
involves three key problems as follows:
(1) Correlation window transform strategy of from object space
to image space. First, the object window with the center of grid
cell to be matched in the plane is determined, and is marked by
W. In this process, it assumes that the ground is flat, and all
points in this window are having the same height values, the
same as the height value of plane location. Then, this paper
projects the four corners of the object window onto all images
to be matched according to the mathematical model of the
sensor imaging, and obtains four corner points of corresponding
region correlation windows in different images. Finally,
according to the sizes of correlation windows, it calculates the
number of pixels within the correlation windows, and inserts
the pixel gray values in the corresponding positions.
(2) Reference image selection. For each grid cell to be matched,
the MZPC algorithm selects the image having a nearest distance
from projection center in the plane to the grid cell to be
matched as reference image.
(3) Similarity measurement calculation. The MZPC algorithm
takes correlation window in the reference image as a standard.
Then it separately calculates the normalized cross-correlation
(NCC) based on the grey similarity measure in correlation
windows between the reference image and other images, and
finally obtains average of the NCC (ANCC) according to
calculate the average of all the NCC.
D (So) = TU,(S,(s)) -T)
NCC (Z Gien ei SE ==
2406.69 - 1 SU, )-1»
| (I)
1, =a TAS (T= TS
Rae SAY
6)
row,col )
1 k
ANCC(Z, Grid, ow.cot ) = 7 > NCC,(Z, Grid
izl
where Z= plane height position
Grid = grid cell with row and co/
row,col
W = the object correlation window
S,(s)» S,(s) ^ separately denote to transform from
the object correlation window to image, which are
obtained in corresponding pixel coordinates in the
reference image and other image.
I,(®), I,(®) = separately mean the pixel grey in the
reference image and other images
k = the number of stereopairs
2.4 Constraint by plane grid cell height
The MZPC algorithm initializes the height of all grid cells in
the plane as zero, and records the grid cell height using a matrix
having the same dimension with the grid cells in the plane,
marked by grid height matrix R where m and n separately
mxn
mean the number of column and the number of rows of grid cell
in the plane. Each value in the matrix means the height of
corresponding position grid cell in the plane.
After matching the best grid cells, the MZPC algorithm assigns
the height values to every successfully matched grid cells to
constrain the latter matching. In the process of matching with
the second-best grid cell, for the grid cells meeting the grey
similarity constraint, it assigns the plane height to this grid cell,
and compares with the heights of other grid cells in a certain
neighborhood range. According to the surface smooth principle
in the local range area, if there are some grid cells having the
similar height value within this neighborhood, it will be
considered as the correct matching result, otherwise will be
abandoned.
3. EXPERIMENTS ANALYSIS
In the experiments, this paper selectes three UCX digital aerial
images in the same strip, whose pixel size is 7.2um, the
corresponding ground resolution is 0.049m, and the along-track
overlapping is more than 8096. The precise orientation elements
of each image are obtained by the triangulation using the
VirtuoZo. In these images, there are some high buildings, which
produce different form occlusions to those surrounding surfaces.
3.4 Determine the grid plane
The coverage area of the image is determined as the range of
moving Z-Plane. The height range of the area in image as shov
is 3 m — 93 m, and the plane moving step is 1m.
32 Feature
constraint
Focusing on
Forstner oper
number of fe
image L3 are
(1) Initially m
from high to |
at each heigh
7=31 positior
0.85 in the gr
of initially m
and the numl
denoted by r
grid cell is sh
Figure 3. T
Figure 4.
Figu
