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The International Archives of the Phutogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
For vehicles (Figure 1 a and b), eCognition is more responsive to 
parameter settings, judging from the more pronounced curvature 
in the distribution of parameter combinations over S. It is also 
readily apparent that eCognition produces results closer to an 
optimal segmentation near the origin. For larger training objects, 
such as buildings (Figure 1. c and d), the opposite is true, with 
ASTRO producing a result closer to the origin (smaller D). The 
software is less distinguishable from the other training object sets 
(trees, Figure 1 e and f; and combined vehicles, buildings and 
trees, Figure 1 g and h). 
For both ASTRO and eCognition (relative to combined vehicles, 
buildings and trees), the parameter combinations with the lowest 
D values differ from the combinations with the lowest D when 
averaged over training objects. For ASTRO, the scale=50, 
color=0.1, smoothness=0.5 combination minimizes D while the 
scale=40, color=0.1, smoothness=0.5 minimizes D when it is 
computed by averaging over training objects. For eCognition, the 
scale=60, color=0.3, smoothness=0.3 combination minimizes D 
while the scale=40, color=0.3, smoothness=0.1 minimizes D when 
it is computed by averaging over training objects. The 
segmentations that result from parameter combinations that 
minimize D are shown in Figures 2 and 3 for a subset of the image 
we used. Figure 4 shows the training objects in the same subset. 
The results are quite obviously qualitatively different, suggesting 
that visual interpretation of the segmentation is relevant to the 
ultimate selection of a particular parameter combination or 
segmentation software. 
Figure 2. The ASTRO result that minimizes D: scale=50, 
color=0.1, smoothness=0.5. 
Figure 3. The eCognition result that minimizes D: scale=60, 
color=0.3, smoothness=0.3. 
Figure 4. The training shapes corresponding to the area in 
Figures 2 and 3. 
DISCUSSION 
The problem of finding an optimal configuration of parameter 
settings has been addressed by Holt et al. (accepted). The index D 
can be used for this purpose, though it not been attempted for this 
study. The procedure involves fitting a convex function of scale,