The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
281 
Figure 3. Road extraction from optical satellite image (IKONOS pan- 
sharpened). Green: correct extraction; red: missing extraction; blue: 
false alarms. For more details, see (Mayer et al. 2006) 
3.3 Internal Evaluation: Effectiveness and processing time 
An automatic object extraction system as exemplified in the 
previous section may not be expected to deliver absolutely per 
fect results and, thus, for meeting predefined application re 
quirements, a human operator must inspect the automatically 
obtained results. In order to speed up the time- and cost 
intensive inspection, the system should provide the operator 
with confidence values characterizing the system’s performance 
- a so-called internal evaluation. This information can only be 
derived from redundancies within the underlying data or the 
incorporated object knowledge. In this context, “object knowl 
edge” means knowledge that is purely described by the object 
model and not by other external data. Therefore, internal evalua 
tion is strongly linked to the extraction process. The results of 
internal evaluation are particularly important if the extraction 
results are combined with other data, e.g., if they are fused with 
results from other extraction approaches or if they are used for 
the update of GIS data. On the other hand, they are also very 
useful for guiding a human operator during post-editing the 
results of an automatic extraction. In practice, however, this is 
rarely the case. 
In order to achieve a highly independent evaluation, we utilize 
“knowledge redundancies” in the form of object properties that 
have not been used during the extraction (see (Hinz & Wiede 
mann, 2004) for details). Usually, these properties relate to 
global characteristics of objects which can be hardly used during 
bottom-up object extraction processes. An extracted road net 
work, for instance, must be in accordance with some typical 
global network characteristics: few connected components, no 
clusters of junctions outside urban areas, convenient connec 
tions between various places depending on the terrain type etc. 
Such properties are used in (Hinz & Wiedemann, 2004) to 
evaluate the reliability of portions of the network with a fuzzy- 
set theoretic approach. 
To allow a quick and effective inspection by a human operator, 
the evaluated road segments are displayed in an overview win 
dow and categorized into three classes: green (to be accepted), 
yellow (to be checked), and red (to be rejected). In addition, the 
average quality of the evaluated network and the distribution are 
displayed (see Figure 4). 
Figure 4. Overview of self-diagnosis. Top: Length-weighted histogram 
of evaluation scores (left-most bar indicates mean value = green in this 
example), thin vertical lines indicate thresholds between categories. 
Bottom: Overview window 
Figure 5. Detailed inspection of evaluation results of a specific portion 
of the road network. Bars indicate results of evaluation for each 
criterion involved ([0 ; 1]). 
Whenever a particular road section is sought to be inspected in 
more detail, it can be selected in a separate cutout to investigate 
the evaluation details (see Figure 5). Based on the visualization 
and the quality information, the operator may decide how to 
handle a particular road section — whether it should be re 
tained, deleted, or edited.