[1700-800 
8600-700 
8500-600 
400-500 
0300-400 
0200-300 
8100-200 
20-100 
  
  
  
  
  
  
  
Figure 2. Voting count distribution in the shift range for one 
example local block. 
In Figure 2, the distribution of voting counts in the shift range 
of [-5, 5] both in X and Y direction is shown. From it, we find 
that around the bin with the highest value, the counts in the 
surrounding bins are also relatively high. This shows that in this 
local block, most of the key points have the location difference 
similar to the selected shift position. This shows that it is 
reasonable to select the highest voting one as the final shift 
amount for the whole block. This phenomenon is also witnessed 
in other local blocks and other test orthoimages. What's more, 
we find that the final rectification shift amount for each local 
block has similar value to that of its surrounding blocks, though 
each block has different shift amount values. This shows that 
the landform is always gradually changing in the altitude. 
  
(e) (d) 
Figure 3. Comparison of original orthoimages and rectified 
results using global matching method and local matching 
method (a) Original old year orthoimage (b) Original current 
year image (c) Shifted current year image by global matching 
method (d) Shifted current year by local matching method ((c) 
and (d) are already after illumination change adjustment). 
To illustrate the preciseness of the local matching method 
compared with global matching method, we show two examples 
in Figure 3 and 4. For each example, the cross point of the blue 
lines in (a)~(d) shows the same location, basically the location 
of one corner point in (a). In (b)-(d), by comparing the location 
of the cross point and that of the corner point in the new image, 
we may find the location difference. From both figures, it is 
easy to find out the following things. The existing location 
difference shown in (b) is relatively reduced in (c), but more 
precisely rectified in (d). What's more, by comparing Figure 
3(b) and 4(b), we find that the two corner points have different 
location difference. Therefore, the same shift rectification for 
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 
the whole image is not appropriate, which is proved by the 
remaining error shown in Figure 3(c) and 4(c). In comparison, 
by rectification of local shift amount there is almost no 
remaining error in Figure 3(d) and 4(d). 
  
(c) (d) 
Figure 4. Another example part in the same orthoimage as the 
one in Figure 3. (a)-(d) has the same description as Figure 
3(a)~(d). 
Based on the above analysis, we conclude that with local 
matching method, the location difference is more precisely 
rectified. Furthermore, both the shift amount from the global 
matching method and that from the local matching method are 
also reflected to the DSM data, so as to improve the detection 
accuracy of height change simultaneously. 
4. EXPERIMENTAL ANALYSIS 
In this section, various experimental results are shown and 
discussed. To illustrate the effectiveness of the proposed 
framework, we select one example data including two datasets 
from different times with relatively large location difference and 
also quite different illumination condition. To show the 
accuracy improvement of each step in the proposed framework, 
we compare the change detection result on the original dataset 
with the result from the processed dataset after each step. The 
test othoimages are with the size of 3000*2500. There are 46 
changing spots between the two orthoimages based on human 
check result. 
  
Original | After After After 
dataset | 1“ step | 2"*step | 3'* ste 
  
  
  
  
  
Overall number 254 175 128 111 
Wrong detection 214 133 86 65 
Correct detection 40 42 42 46 
Missed 6 4 4 0 
correct detection 
Correct 15.7% 24% 32.8% 41.4% 
detection rate 
Completeness rate | 87.0% | 91.3% 
  
  
  
  
  
  
  
91.3% 100% 
  
Table 1. Comparison of the change detection result on the 
original dataset and the dataset after the each processing step 
In Table 1, the change detection result on both the original 
dataset and the dataset after each processing step is shown. By 
comparing the number of wrong detections, correct detections,