The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
335 
5. RESULTS AND SUMMARY 
Table 1 and Table 2 specify the respective results of classifying 
sub-images as fragments being either "favourable" or 
"unfavourable" from the point of view of their subsequent 
matching, as well as results of matching sub-image 
representations on two successive aerial images. Results 
presented in Table 1 prove that the use of neural networks for 
the preliminary selection of images is advisable on condition 
that proper image representations have been applied. It is 
possible to additionally increase the recognition percentage 
through the use of the rejection technique, which consists in 
testing the recognition reliability, and through the proper 
selection of the threshold value for that reliability. Results 
presented in Table 2 (the third column) were obtained upon 
having assumed the boundary value of shift, below which 
matching can be considered to be acceptable. That value, 
amounting to 30 pixels, was assumed based on results of 
matching whole (non-processed) sub-images by means of area 
based matching method (see last line in Table 2). That was just 
the maximum error of matching by means of that method. In 
turn, the last but one line in Table 2 shows small possibilities of 
direct use of the representation, which is based on a 2D 
histogram of gradients. The best results were obtained as 
regards representations in the form of signatures generated by 
the ICM network (ca. 91% matching). 
Description of 
representation 
Data set 
description 
Result 
Publications 
and 
comments 
outputs of neurons 
of Kohonen's 
SOM layer 
36 pairs of 
images 
(the G 
component) 
10/36 
(Czechowicz 
et al., 2007a) 
100-element 
signatures, 
6 signatures per 1 
sub-image 
44 pairs of 
images 
(in greyscale) 
40/44 
(Mikrut 
et al., 2008) 
19-element profile 
of gradient 
directions 
36 pairs of 
images 
(the G 
component) 
2/36 
(Czechowicz 
et al., 2007a) 
whole sub-image 
- area based 
matching 
as above 
36/36 
(Czechowicz 
et al., 2007a) 
Table 1. Results of recognition of 2 image classes 
Description of 
representation 
Type and 
structure of 
neural 
network 
Recog 
nition 
of test 
set 
Publications 
and comments 
2D histogram of 
gradients: 
profile of 
average gradient 
direction values 
with the 
aggregation of 
20° 
Kohonen's 
SOM 
19-28(7x4] 
89.5% 
(Czechowicz 
et al., 2007) 
rejections 
were applied - 
responses 
from 6 
neurons were 
taken into 
account 
as above 
SOM + BP 
19-(7x4]-5-2 
84.5% 
(Czechowicz 
et al., 2007a) 
as above, but 
with the 
BP 
73-10-2 
84.8% 
(Czechowicz 
et al., 2007a) 
aggregation of 5° 
log-Hough space 
- vector of 
projections upon 
the radius axis, 
aggregated at 
every 2 
BP 
25-2-2 
25-28-2 
72.5% 
69.2% 
(Piekarski et 
al., 2007) 
50-element 
signatures 
generated by the 
ICM network 
BP 
50-6-2 
73.0% 
(Mikrut, 2007) 
- at -20% of 
rejections the 
recognition 
increased up 
to -80% 
Table 2. Results of image matching experiments 
In the case of lower, acceptable distances of deviations 
amounting to 10 and 5 pixels, the percentage values of correct 
matching amount to ca. 63% and 38%, respectively. 
The results obtained in the course of research works confirm the 
original assumptions, though it needs to be stressed that the 
issue is a complex one, and the research subject far more 
extensive than originally assumed. The matched aerial images 
are marked by the changeability of objects, textures and 
illumination. It was mainly those features which, combined 
with too poor initial processing of images, resulted in the 
obtaining of not fully satisfying effects. Summing up, it is 
necessary to state that the application of neural networks 
succeeded in the case when a preliminary selection of sub 
images had been performed. 
The problem of matching requires probably another approach to 
be followed, perhaps the one based on detection and description 
of objects. The authors believe it is reasonable to continue 
research with the use of algorithms that have been modified in 
that way, but with a similar methodology. 
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