International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
including standard deviation of photo coordination 
measurement, From Table 2 can be seen that when the 
observed pointed are divided into two groups, a’priori standard 
deviation of photo coordination measurement is defined 
separately as lum and 22pm for the Block 1 and 12um and 
25um for the Block 2, while when the points are not grouped, 
there is only one value used : 11pm for the Block 1 and 121m 
for Block 2. This is because the points on trees and non-trees 
are needed to given different and the thresholds for detecting 
and low regions according to the disparity images. Next, the 
trees are extracted by Fuzzy C-Mean in the high and low areas, 
using a set texture and color features. We demonstrated in a 
number of aerial images that the correct rates of extraction trees 
are from 95% to 88%. Finally, we have shown that algorithm 
can be used to group automatically the observed points in aerial 
triangulation. The comparison results illustrate that the 
adjustment accuracy is improved using the observed points 
grouped in the forest-covered areas. In the future, with this 
extraction algorithm ,we would analyse that forest affect the 
Table 2. Comparison results of the adjustments in the two different conditions 
  
  
Description Block 1 Block 2 Percentage(%) 
Grouped Ungrouped Grouped Ungrouped 
Pre-Std.dev. of photo No. I 11 No.l 12 
coord(u m) No.2 22 11 No.l 25 12 Block! Block 3 
Std.dev.of photo 
coord (uum) 8.08 17.03 6.90 11.61 53 41 
RMS control (cm) 7,4,5 11,13,14 45,51,65 66,76,112 36,69,64 32,33,42 
RMS check (cm) 20.18.27 30,40,54 65,70,101 80,91,120 33.55.50 19,23,16 
Mean Std.dev.of 
Objects points(cm) 15,14,26 21,20,36 45,53,63 50,58 75 29,30,28 10,9 11 
Max. Std.dev.of 
Objects points(cm) 40,35,70 39,35,69 118,180,172 110,172,175 -2,0,-1 -7,-5,2 
Min. Std.dev.of 
Objects points (cm) 72411 14,14,23 22.23.26 31,32,34 50,50,52 29,28,24 
Mean td.dev.of 2027.17 47,40,29 44,46,34 49,50,40 38,33,41 10,8,15 
rere 17,17,9 27.26.16 4.1,4.2,3.3 6.4,8.2,5.4 37,65,44 36,49,39 
mgon 
Max. Std.dev.of 60,54,33 60,52,41 83,82,78 96,85,79 0,-4,20 7,4,1 
ext.orient(cm)/(mgon) 35,34,19 35.33.21 53.3.7.5.1 6.0,3.5.5.2 0,-3,10 8,-2,2 
Min. Std.dev.of 18,16,9.2 3331.19 30.3222 33.37.30 46,48,53 14,13,27 
ext.orient(cm)(mgon) 10,9,5 19.19.10 2.8,2.5,1.4 3 0 27,18 47,52,50 7.7.22 
  
percentage (%)=(U-G)/U , where U and G expresses the accuracy values ungrouped and grouped respectively. 
error and the weights, in order to guarantee the connecting 
strength of aerial triangulation network that is very important to 
obtain good results, decrease the negative influence of the 
points on trees. The effect of the points grouped is clearly seen 
in the results : the accuracy of photo coordinates in the 
observed points grouped as compared with the points 
ungrouped is 53% higher for the Block 1 and 41% higher for 
the Block 2. The RMS of control points in the observed points 
grouped is lower 36% (X),69% (Y ) and 64%(Z) for the Block 
1 and 32%(X),33%(Y) and 42%(Z) for the Block 2, but, the 
maximum standard deviation of objects in the observed points 
grouped is higher 2%(X)and 1%(Z) for the Block land 
7%(X),5%(Y)and 2%(Z) for the Block 2. This is due to the fact 
that the results of the adjustment by the points grouped is very 
well within two region, but it has problems that the threshold 
for detecting error of the points on trees is bigger than non- 
trees, a few lower accuracy observed points are permitted to be 
involved in the adjustment. To prevent some bad observed 
points to participate the final calculation, in practice, the 
reasonable rate of the a'priori standard deviation of photo 
coordination measurement is chosen, according to the terrain 
characteristic of blocks. Note however that the main accuracy 
measures of the adjustment by the observed points grouped are 
better than of the points ungrouped for the two blocks. 
4. Conclusion 
In this paper, we have proposed a new algorithm to extract 
the objects which are higher than surrounding in color aerial 
images. At first, the original images are segmented into high 
accuracy of digital elevation model in the forest-covered 
regions. 
References 
T.Dang, O.Jamet, H.Maitre. 1995. Applying Perceptual 
Grouping and Surface Models to the Detection and Stereo 
Reconstruction of Building in Aerial Imagery. IntArchPhRS, 
Vol.30, Part 3/1, pp.165-170. 
N.Haala. 1994. Detection of Building by Fusion of Range and 
Image Data. IntArchPhRS, Vol 30, Part 3/1. 
W.Eckstein, C. Steger, 1996. Fusion of Digital Terrain Model 
and Texture for Object Extraction, Proceeding of 2™ Airborne 
Remote Sensing Conference , San Francisco, 1-10. 
R. Kaczynski, J. Ziobro, 2000. Comparison of Semi-Automatic 
and Automatic Digital Aerial Triangulation. 
IntArchPhPR, Vol. X XXIII, Part 3.Amsterfam, pp.457-461. 
L. Pan. 2001. The Study on Forest Area Recognition from 
Color Aerial Image and Its Application for Automatic Aerial 
Triangulation, Ph.D. thesis, Wuhan University.