In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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Figure 4. Interface of FbSP optimizer. The Segmentation 
Parameters, Target Object Information and Sub Object 
Information are inputted into the system to train FbSP 
optimizer to estimate the optimal Segmentation Parameters for 
the Target Object through fuzzy logic analyses. 
In Figure 4, the Segmentation Parameters (Scale, Shape, and 
Smoothness weight) for performing a preliminary over 
segmentation are initially selected and inputted by user. They 
are then updated by the FbSP optimizer in the next iteration 
according to the information of the target object ant its sub 
objects. The Texture, Stability, Brightness and Area for the 
target object and sub objects are calculated according to the 
pixel grey values within the target object and individual sub 
objects, respectively. The Compactness and Rectangular Fit are 
calculated according to the shape of the target object. 
4. EXPERIMENT AND RESULTS COMPARISION 
4.1. Data Sets 
Pan-sharpened QuickBird MS image and pan-sharpened Ikonos 
MS image over Fredericton, Canada, and pan-sharpened 
QuickBird MS image of Oromocto, Canada, were used to test 
the FbSP optimizer. The UNB-Pansharp was used to fuse the 
Pan and MS images. The four pan-sharpened multispectral 
bands were used as input bands. 
4.2. Segmentation Process and Results 
For small objects, the FbSP optimizer can find optimal 
segmentation parameters through one or two iteration(s) of 
segmentation parameter estimation (Figure 3 and Figure 5). For 
example, for small buildings shown in Figure 5, the FbSP 
optimizer used just one iteration to find the optimal 
segmentation parameters for the object of interest—small 
buildings. If the initial parameter selection by the user for the 
initial over segmentation is counted as one iteration, two 
iterations of parameter selection in total were needed, one by 
the user and one by the FbSP optimizer (Figure 5.a and 5.c, and 
Table 1). 
Table 1 shows the segmentation parameters selected by the user 
for initial over segmentation (Iteration 1), and the parameters 
estimated by the FbSP optimizer in the first loop (Iteration 2). 
Table 2 shows the feature information of the sub objects 
(Figure 5.a, red) selected to form a target object. Table 3 lists 
the feature information of the target object (Figure 5.b, red). 
(a) 
JEL 
(£) 
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Figure 5. Segmentation process and final segmentation result 
using the FbSP optimizer for small buildings (pan-sharpened 
QuichBird MS, Oromocto). (a) Initial over segmentation using 
user selected initial segmentation parameters (first iteration) 
and the sub objects (red) selected to form a target object, (b) 
Target object formed by the sub objects for training the FbSP 
optimizer, (c) Segmentation result achieved using the 
parameters estimated by FbSP optimizer in the first loop 
(second iteration in total), (d) Final segmentation result using 
the parameters estimated in (c). 
According to the feature information in Table 2 and Table 3, 
the FbSP optimizer estimated the optimal segmentation 
parameters (Table 1, Iteration 2) for the target object. Using the 
parameters in Iteration 2 of Table 1 to segment the input image, 
the segmentation result shown in Figure 5.c was achieved. The 
feature information (Table 4) of the resulting segment (Figure 
5.c, red) is almost identical to that of the target object (Table 3 
and Figure 5.b, red), i.e. the resulting segment converges with 
the target object (Figure 3, step 11), so that the segmentation 
parameters estimated by FbSP optimizer in the first loop 
(Iteration 2 of Table 1) was accepted as the optimal 
segmentation parameters for small buildings. 
For large objects, more sub objects need to be selected to form 
a target object, so that more iterations are usually needed to 
reach the convergence between the target object and the 
resulting segment. For example, four iterations were needed to 
reach the convergence for large buildings shown in Figure 6. 
4.3. Result Evaluation 
Figure 7 shows the segmentation result of large buildings using 
trial-and-error approach for segmentation parameter selection. 
The result was achieved by a very experienced operator 
through approximately two hours of parameter selection and 
test. Comparing the result from FbSP optimizer (Figure 6) and 
that from trial and error (Figure 7), we can see that the FbSP 
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