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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
Layer 
No 
Feature Names in Layer 
Number of 
Detected 
Features 
Compilation 
Percentages 
Aerial 
Photo 
Quickbird 
£ 
5 
Rocks and stony place 
22 
- 
0.0 
31 
Water depot 
1 
- 
0.0 
42 
Telephone/Radio line/station 
2 
- 
0.0 
43 
Sporting facilities 
6 
- 
0.0 
44 
Single grave or graveyard 
2 
- 
0.0 
49 
Pipe line and sewerage 
3 
- 
0.0 
51 
Ditch, set and tumulus 
43 
- 
0.0 
27 
Lean-to roof 
397 
13 
3.3 
48 
Telephone/Electric pole, lamp 
321 
32 
10.0 
20 
Pavement 
183 
30 
16.4 
50 
Slope and natural split 
183 
31 
16.9 
12 
Water well and canal 
9 
2 
22.2 
45 
Electric line and transformer 
53 
18 
34.0 
38 
Hedge, wire fence, railing 
245 
95 
38.8 
14 
Water tower, small lake, winch 
10 
4 
40.0 
16 
Ownership border 
235 
99 
42.1 
11 
Stream, spring, marsh 
20 
9 
45.0 
26 
Building under construction 
19 
9 
47.4 
39 
Bushes, orchard, tree 
1758 
1015 
57.7 
21 
Country road, footpath 
543 
375 
69.1 
13 
Fountain and pool 
14 
12 
85.7 
25 
Private building 
249 
225 
90.4 
36 
Factory, chimney, factory hut 
41 
48 
117.1 
17 
Disapproval ownership border 
94 
120 
127.7 
46 
Patrol station and pump 
6 
8 
133.3 
15 
Tunnel, bridge, stop 
67 
108 
161.2 
40 
Tree, forest area, green 
house 
18 
51 
283.3 
41 
Park and garden 
- 
1 
+ 1 
22 
Under- and top-passage 
- 
2 
+ 2 
Table 6. Comparison of feature layers compiled on Quickbird 
satellite images in 1. sheet 
In field control application, the control of outputs of 
compilations belong to aerial photographs has been carried out 
firstly because most of the data have been compiled on aerial 
photographs. While determining an error during the controls, 
brief notes have been taken on the outputs about the errors and 
then these errors have been controlled on the outputs of 
compilations belong to high resolution satellite images. The 
attributes of features controlled and the errors determined have 
been investigated on laptop and lastly taking all these data into 
consideration, it has been tried to evaluate the compilations. 
Because of long time interval between images and field controls, 
some difficulties have been encountered in finding and 
detection of features on Gôlbaçi region which is growing very 
quickly. 
4.4 Feature Compilation Assessment Results 
Using high resolution satellite data, the feature types that are 
required for 1:10.000 to 1:50.000 scale mapping could be 
satisfactorily identified and captured. In some cases, features 
required for larger scale mapping (e.g. roads and woodland 
boundaries) could also be identified. But as may be expected, it 
is impossible to distinguish the narrow linear features (such as 
electricity transmission lines, shapes of buildings, boundaries, 
walls, fences and hedges) on satellite imagery. A combination 
of panchromatic and multispectral imagery can help to 
differentiate between vegetation and artificial features (e.g. 
between hedges and walls) but in general the imagery is 
unsuitable for the capture of these narrow linear features 
(Holland and Marshall, 2004; Holland et al., 2006). 
Feature compilation assessment results show that high 
resolution satellite images couldn’t reach to the level of aerial 
photographs in determining/identifying of small features yet. As 
a result, concerning compilation applications, we can say that; 
■ The number of features compiled from Quickbird and 
IKONOS ortho-images was approximately equal and we 
determined that the nearest values to the aerial photographs was 
obtained firstly in polygon layer (% 63 - 65), secondly in point 
layer (% 57 - 64) and lastly in line layer (% 46 - 50). 
■ Quickbird orthophotos showed better performance in line 
layer and IKONOS orthophotos have shown better performance 
in point layer. 
■ The features which were almost not compiled at all in 
high resolution satellite images (% 0-10) and acquired in aerial 
photographs are; telephone and electric poles, borders, rocks, 
stony and sandy places, lean-to roofs and pavements. The 
features compiled in minimum number (% 10 - % 40) are; 
slopes, natural splits, telephone and electric poles, water wells, 
canals, transformers, trees and forest area. The features 
compiled in number of % 40 - % 70 are; streams, springs, 
hedges, railings and walls, tunnels, bridges, fountains and 
bushes. And the features compiled in best number (% 70 - 
% 100) compared aerial photographs are; country roads, 
footpaths and single buildings. 
As an overall assessment for field control applications, we can 
say that the operators have had some difficulties in determining 
and identifying of some features existing in high resolution 
satellite images. These features are; water wells and 
transformers taking place in every private country house, 
communication and electricity transmission lines in dense 
residence areas, electric/illumination poles, wire hedges, small 
huts and lean-to roofs. And these results indicate that high 
resolution satellite imagery can be used to identify topographic 
changes for both large- and small-scale mapping, even if this 
imagery cannot be used as a source of direct topographic data 
capture (Holland et al., 2006). 
5. CONCLUSION 
In summary, it can be said that; 
■ IKONOS-DEM can be used instead of 
photogrammetric DEM produced from 1:16.000 scaled aerial 
images and the GCP quality which depends on well spread 
distribution and easy recognition is as important as the number 
of GCP’s. 
■ When using direct sensor orientation parameters, 
IKONOS images have better accuracy than Quickbird images. 
In addition, systematic errors have been observed in the 
easting/north easting (across track) direction. 
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