In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
Figure 7. Segmentation result of large buildings obtained through trial-and-error parameter selection, operating time: 2 hours (pan- 
sharpened QuichBird MS, Fredericton) 
Segmentation of other objects was also tested. The trial-and- 
error approach needs 2 to 6 hours to reach acceptable 
segmentation results, whereas FdSP optimizer just needs 30 to 
60 minutes. If the 90% of the time for manual input and output 
is reduced through software integration, FdSP optimizer will 
just need less than 10 minutes to select optimal segmentation 
parameters for large complex objects. 
5. CONCLUSIONS 
A Fuzzy-based Segmentation Parameter (FbSP) optimizer was 
developed to improve the efficiency of segmentation parameter 
selection and accuracy of object segmentation for eCognition. 
The FbSP optimizer can be trained using initially segmented 
sub segments and the corresponding targeted object of interest. 
The FbSP optimizer can then find the optimal segmentation 
parameters for the target object, through fuzzy logical analyses 
of the target object and its sub objects. 
Experiments with QuickBird and Ikonos pan-sharpened MS 
images demonstrated that the FbSP optimizer can effectively 
find optimal segmentation parameters for objects of interest 
within 30 to 60 minutes under the current software 
implementation condition. If the current manual and iterative 
input and output of feature information between FbSP 
optimizer and eCognition is reduced through software 
integration, FbSP optimizer will just need a few minutes to find 
optimal segmentation parameters for an object of interest. In 
contrary, 2 to 6 hours are usually needed for an experienced 
operator to find proper segmentation parameters through trial 
and error. The proposed supervised approach to automated 
determination of optimal segmentation parameters has 
demonstrated its superior advantage in speeding up the 
segmentation parameter selection and improving the 
segmentation quality. It exhibits the potential to boost 
segmentation techniques from current trial-and-error stage into 
the next stage—semi-automated or automated process. 
Further tests with other remote sensing images will be 
conducted. The FbSP optimizer will be further improved. 
6. ACKNOWLEDGMENTS 
This research was supported by the Discovery Research Grants 
Program of NSERC (Natural Science and Engineering 
Research Council of Canada), Canada Research Chairs 
Program, and the Department of National Defence (Canada). 
The original QuickBird and Ikonos satellite images were 
provided by CFB Gagetown and the City of Fredericton, NB, 
Canada. 
7. REFERENCES 
Baatz, M. and A. Schape, 2000, Multiresolution Segmentation - 
An Optimization Approach for High Quality Multi-Scale 
Image Segmentation. Angewandte Geographische 
Informationsverarbeitung XII, Ed. J. Strobl et al. AGIT 
Symposium, Salzburg, Germany, 2000. pp. 12-23.