Full text: XVIIth ISPRS Congress (Part B4)

ng 
ut 
ni- 
  
  
  
Evaluation and clean-up 
Procedures are required that detect the above operating 
characteristics. 
In order to detect unconnected line-pieces, the skeleton 
is vectorized. For each segment the direction is 
computed. The decision whether the line segments will 
be connected is done by a length-weighted direction 
table. The problem is to decide whether fragmentation 
of the road boundary is due to the segmentation 
technique or due to a new node. 
Another procedure has to be defined that detects 
parallel boundaries in the region of interest. One 
method is to detect jumps in the maximum cost path. 
Their detection results in adaptation of the width of 
the region of interest and an assumption for the 
location of another (parallel) road. 
Testing of hypotheses 
In fig. 4 three boundaries run over the complete length 
of the region of interest, but there is a gap. The 
presence of the road is first checked for the parts at 
both sides of the gap. Fig. 6 shows how the region of 
interest was splitted in two candidate arcs and one 
candidate node. The boundaries in the candidate arcs 
run over the complete length of the region of interest, 
so there is evidence for the presence of a road 
segment. However, also canals have parallel borders. 
So the conclusion is not unique and further evidence 
has to be found by combining edge information with 
surface information. 
When checking edges in the candidate node, edges 
will be found perpendicular to the boundaries in the 
candidate arcs. This indicates high evidence for the 
presence of a fly-over, but this hypothesis is tested in 
another stage of the process. 
44 High level: control 
After analysis of the segmentation result, the next step 
in the hypotheses hierarchy has to be determined. 
Control by the high level consists of two parts: 
1. local and context confirmation of the candidate road 
segment; 
2. updating of the evidence of the interpretated 
objects. 
Local and context confirmation 
If evidence has been found for the presence of a road 
boundary, it needs to be confirmed by evidence of a 
road surface. The hypothesis that the road is still 
present is accepted on bases of local evidence. It is 
known that roads form a connected network. Hence 
we can use context information to find evidence for 
this hypothesis. Suppose the evidence of the candidate 
node increases in a next step, than the evidence for the 
two adjacent candidate road segment increases. 
Fig.6 The region of interest of fig.3 splitted in thre 
a) candidate road segment; | b) candidate node; 
e parts 
The problem of the high level is: When is enough 
evidence gathered to accept a definitive hypothesis? 
For example: How many segmentation techniques 
should be used before enough evidence is gathered 
about the presence of a road? Ideally, unambiguous 
measures, able to compare results of all segmentation 
techniques, should indicate the quality of the segmen- 
tation result. This measure should not only include 
geometry, but aspects like context as well. 
Updating of the interpretation evidence 
The candidate node has to be examined in a next step 
of the hypotheses hierarchy. Because we already found 
evidence for the presence of a fly-over, we will first 
examine if it is a fly-over. For this confirmation we 
need context information too. So recognized parts of 
the road network can guide further interpretation. 
Future recognized objects may also influence present 
decisions. If road segments are found that cross the 
current road at the location of the gap, the evidence 
that there is a node of type fly-over increases. 
5. CONCLUSIONS 
Image interpretation of aerial images by computer is a 
highly complex task. The casestudy illustrates that 
even for one simple road segment in the database, a 
complex procedure is needed to verify its presence in 
the aerial photograph and to check whether its 
properties, such as width and curvature, have changed. 
The problem is to bridge the gap between the image, 
which is a two-dimensional intensity array, and the 
object models. 
A multitude of segmentation methods should be 
employed in an integrated way. In our opinion first 
more insight is needed into the performance of 
segmentation techniques on aerial images, before the 
image interpretation problem can be solved. A bottle- 
neck is the lack of measures for the evaluation and 
comparison of the performance of segmentation 
techniques. 
An inherent problem of interpretation is the integration 
of knowledge sources. Based on these knowledge 
sources, hypotheses should be constructed, tested and 
updated. In a multi-stage approach the parts of the 
image that have been examined influence the inter- 
pretation process. The evidence of previous interpreted 
objects should be updated, with progressing 
availability of context information. 
Consequently, the computational burden of managing 
the interpretation process with its many feedback-loops 
is huge. 
One of our present points of concerns is whether 
expert shells are suitable for managing the complexity 
of the interpretation process. 
  
c) candidate road segment. 
483 
 
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.