963 
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rea 
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methods and 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
compared the performance of object-level post classification 
comparison(OL-PCC) method and pixel-level post classification 
comparison (PL-PCC)approach. 
Change detection approach 
OL-PCC 
PL-PCC 
Detection accuracy (%) 
67.3 
52.6 
Overall accuracy (%) 
85.4 
68.9 
Omission error (%) 
32.7 
48.4 
Commission error (%) 
15.7 
25.2 
Overall kappa 
0.74 
0.54 
Table 3. Change detection accuracy of OL-PCC and PL-PCC 
5. RESULT DISCUSSION 
As outlined above, the proposed OL-PCC method combinng 
MS&RG multiscale image segmentation, SVM and OOA was 
proved to have advantages against PL-PCC 
methodology.Besides the low detection precision,it is worthing 
noting that the phenomenon of “ Salt and pepper ” is severe in 
the change map based on PL-PCC. 
Through the OLCD, the initial multiscale image segmentation 
process insures the quality of the multispectral data to be 
submitted to SVM classification. Indeed, the object delineation 
combined the spectral, spatial and contextual information to 
create consistent units of interest. The segmentation is also less 
sensitive to misregistration errors than traditional pixel-level 
analysis methods(Makela & Pekkarinen, 2001)between 
multidate images and reduces the change detection processing 
time given that there are much fewer objects than pixels. Based 
on these objects,OLCD method breakes the constraint of sensor 
characteristics and spatial resolution in multisource satellite 
images and the change detection performances are 
increased.Moreover, the object boundaries derived directly from 
the segmented images are more convenient to update GIS 
database in land surface monitoring and map updating. 
6. SUMMARY AND RECOMMENDATIONS 
The object-level change detection method proposed here proved 
to be very efficient to identify land use and land cover changes 
of HR satellite images.A detection accuracy higher than 85% 
and an overall kappa higher than 0.7 were achieved using a 
SPOT5 and IKONOS multitemporal data set covering a 4-years 
time span. This technique can be considered scene-independent 
in the sense that OOA determines whether change or not 
according to class attributes of each object ,instead of 
predefined change threshold of the multidate image. 
Whereas this research focused mainly on the whole technique 
workflow and final detection accuaracy,igoring the influence of 
segmentation precison.If the aim is to obtain accurate and 
quantitative assessments about the change area,this 
approach needs further theoretical developments. 
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ACKNOWLEDGEMENTS 
This work was supported by National Key Basic Research and 
Development Program(Grant No. 2006CB701303).