The International Archives of the Photoyrammetry. Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beiiinp 2008 
Field and Wetland Vegetation were also significant. In addition, 
Paddy Field and Wetland Vegetation area, which in reality exist 
only in the lowlands, and shown in the uplands as well. This is 
most likely due to the fact that Paddy Field and Wetland 
Vegetation patches may not have held water at the time the data 
was acquired, and thus showed spectral characteristics that can 
easily be misinterpreted as Bare Ground or other upland 
landscape types. Using only spectral characteristics, producer’s 
accuracy for Paddy Field was as low as 45%. 
In contrast, classification results incorporating the GIS data 
showed overall accuracy and overall Kappa index above those 
for the spectral only classification. This difference was 
especially evident in terms of producer’s accuracy for 
Grassland, Wetland Vegetation and Paddy Field. These results 
show that misclassifications among the upland and lowland 
landscape types can be eliminated by using the topographic data. 
Figure 8 shows an expanded view of one section of the master 
landscape map (a) compared to the object-based classification 
using topographic data results (b), and the object-based only 
classification results (c). As can be clearly seen, the 
misclassifications of lowland landscape types Paddy Field and 
Wetland Vegetation as upland landscape types Bare Ground 
and Grassland, have been eliminated by incorporating the 
topographic data. Although the user’s accuracy for Paddy Field 
was improved, that for Grassland £nd Wetland Vegetation 
remained low. This is most likely due to the fact that at the 
time the data was acquired some of the paddies had been 
planted in a winter crop of wheat, which was easily confused 
with Wetland Vegetation or Grassland. 
In addition, although the producer's (78%) and user's (26%) 
accuracies for Rural Residential improved slightly, the gap 
between these two remained large. In both classifications, the 
mean value for each object was used to derive the minimum 
distance. In the Rural Residential landscape, very tiny patches 
of forest, Bare Ground and Grassland are mixed together. For 
this type of landscape some improvement in accuracy may be 
obtained by using the standard deviation rather than the mean 
value. 
IKONOS image, master landscape map, and segmentation 
results for a section of the target area are shown in Figures 9 
and 10. In Figure 9, segmentation results for Rural Residential 
are shown at two different scale parameters, SP=66 (Fig.9c) and 
SP=235 (Fig.9d). As can be seen, at the lower scale parameter 
each individual object is too small, resulting in over 
segmentation. The higher parameter, on the other hand, 
produces a much more accurate segmentation. 
In Figure 10 the same comparison is made for the othei 
landscape types. In this case, the higher parameter fails to 
distinguish among the different types of vegetation. These 
results indicate that the best scale parameter may vary 
according to the type of landscape being segmented. Further 
research in this direction may lead to future improvements in 
accuracy. 
4. CONCLUSION 
This research was designed to test and improve systems for 
applying object-based classification of very high resolution 
IKONOS data to mapping of landscape types. In addition to the 
extant multi-spectral object-based classification system as 
developed by Kamagata et al (2006), topographic data derived 
from field surveys and topographic maps was used to perform 
an initial segmentation that divided this research area into two 
topographic zones. The results showed that some of the 
misclassification problems that plagued the extant system could 
be eliminated by this method. Incorporation of topographic 
data should prove especially useful in the Japanese countryside, 
where various small patches are scattered in a complicated 
mosaic pattern. The research also indicated that further gains in 
accuracy can be achieved by adjusting the scale parameter to 
the characteristics of each landscape type. 
ACKNOWLEDEMENTS 
The authors would like to thank Dr. Hijiri Shimojima of TUIS. 
Professor Kevin Short of TUIS helped with the English editing 
work. 
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