XXXIX-B3, 2012 
en lines 
lap(m,,m,) 
h(m,),length(m,)) 
(9) 
e overlap length of 
length of line L ,, ang 
| the above similarity 
vill be used to find out 
veen the two lines (Wu 
natching lines AC and 
es of the end points of 
1 using dashed lines in 
nes with the lines AC 
these two lines can be 
  
  
ight image 
find corresponding 
natching 
1e overlap segments of 
ist in a local buffering 
> lines can be used to 
  
inear feature 
porting region 
on and decomposition 
cept of linear feature 
4 (a), L is a straight 
liscrete image surface. 
central axis is L and 
  
the width is 27 , and this rectangular area will be defined as the 
linear feature supporting region of line L . As shown in Figure 
4 (b), the supporting region can be decomposed into 2r+1 
parallel line segments with equal length. L and the left r line 
segments are defines as the left linear feature supporting region, 
also L and the right r line segments are defines as the right 
linear feature supporting region. The gray value of the point J 
in the line I will be marked as g T . Arranges the gray values 
of (r4 1)xn points in the left supporting region can be arranged 
as a matrix form, and then the gray value matrix of the left 
linear feature supporting region can be obtained. 
Simultaneously, the gray value matrix of the right linear feature 
supporting region also can be obtained. On both sides of the to- 
be-matched lines, the Normalized Cross Correlation (NCC) 
values will be calculated separately between the image gray 
values within the supporting region. The larger one is taken as 
the final NCC value for this line.Then the correlation 
coefficients of the linear feature supporting regions can be 
calculated. 
Area sim =max(NCC,,NCC,) (10) 
Where NCC, . NCC, are the correlation coefficients of 
the left and right supporting regions for corresponding lines. 
3. EXPERIMENTAL ANALYSIS 
This paper adopts the unmanned aerial vehicle images and UCX 
digital aerial images to carry on the experiments of line 
matching. 
31 Experiment 1 
The experiment data are two images cut from the stereopair 
imaged by unmanned aerial vehicle, and the image sizes both 
are 512X 512 pixels. Fig. 5(a) is the target image, and Fig. 5(b) 
is the searching image. 
(1) Computation of the homograph matrix. 
In this step, it firstly realizes the image matching based on 
feature points, and the succeed matched corners in the 
stereopair images are shown as Fig. 5(c). Then substitutes the 
matched points to the equation group LH = 0, obtains the 
: : ; T ; , 
coefficient matrix L , and computes the matrix L L . Finally it 
solves the homograph matrix Æ through the Singular Value 
Decomposition about matrix rr (Wang Jinquan, 2008). 
1.0181 0.04434 -3.6743 
H =| 0.006193 1.064 42.29 (11) 
7.3797e-006 0.000117 1 
(2) Line extraction and matching 
This paper adopts the Canny edge detection operator to carry on 
the edge detection of image, and gets the binarization edge 
image. Then it extracts the lines from the binarization edge 
Image using the improved Hough Transform. By 
setting the threshold, it avoid the over connection problem 
for long-distance points, and filters out some 
short straight lines. The line extraction results are shown as 
Fig. 5(d) and Fig. 5(e). Using the computed homograph matrix 
; It projects the line set in Fig. 5(d) to the image coordinate 
System defined by the searching image, and the overlap results 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
of two line sets are shown as Fig. 5(f). This paper determines 
the candidate lines according to the distances between the lines 
to be matched, and fixes the homologous lines using other 
constraint conditions, then obtains the matching results are 
shown as Fig. 5(p) and Fig. 5(q). Through the visual 
interpretation, the ^ one-to-multiple " phenomenon can be 
found in the matching results, which is due to the broken lines 
in the extraction, and belongs to the correct matching results. 
From this experiment it can be found that the homograph matrix 
carries on the effective constraint to the line matching, reduces 
the complexity of matching algorithm, and improves the 
accuracy rate of matching. 
(a) The target image 
    
    
  
(c) (left) The positions of points matched in the two images 
(d) (right) The results of extracting lines in the target image 
  
(e) (left) The results of extracting lines in the searching image 
(f) (right) The fitting of the two sets of straight line segments 
(p) (left) The straight lines matched in the target image 
(q) (right) The straight lines matched in the searching image 
Figure 5. The original images and the results of post-processing