The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
executing rule 6 executing rule 5 
Figure 7. Soft-decision change detection based on rules 
By applying the same accuracy analysis method applied to the 
hard-decision change detection result, from Table 8, it can be 
seen that the soft-decision analysis can generate improved 
change detection result, the omission error is 6.5%, the 
commission error is 15.3%; compared with Table 6, the overall 
accuracy is improved from 62.7% to 78.2%, and the 
commission error is reduced by half. 
Reference data 
Sum in row 
Classification 
data 
unchanged 
changed 
unchanged 
161 
39 
200 
changed 
92 
308 
400 
Sum in column 
253 
347 
600 
Table 8. Accuracy evaluation for the soft-decision change 
detection 
'^^2004 
2005^"\ 
vegetation 
built-up land 
bare land 
water body 
vegetation 
0.0 
0.0 
0.39 
built-up land 
149.0 
7.5 
4.8 
bare land 
173.6 
0.0 
18.8 
water body 
7.8 
0.0 
0.0 
Table 9. Area change of the land use types of the test site (Area 
unit: hectare) 
The statistic of the change of the area of each land use type in 
the research area (Table 9) shows that from October 2004 to 
October 2005, the main land use/cover changes are the decrease 
of vegetation, as well as the increase of the built-up land and 
the bare land. By further analyzing the classified result of the 
SPOT5 image acquired in 2004, we find out that in the 
trajectory of vegetation to built-up land, 20% of vegetation is 
cropland and 80% of vegetation is grassland. Because the study 
area is located at the downstream region of Haihe River, which 
belongs to the fluvial plain and marine plain, the annual mean 
relative humidity is high, and the case of vegetation changed to 
bare land usually occur when the parcel has been authorized to 
be used as built-up land but construction has not been done or 
just at the beginning of the construction. The circumstance of 
the decrease of vegetation, as well as the increase of the built- 
up land and the bare land well reflects the phenomenon and the 
trend of urban expansion and the occupation of the cropland 
and the unused land at the urban fringe areas. 
4. CONCLUSIONS 
Because of the different imaging mechanisms, optical imagery 
and SAR data has different and complementary image 
characteristics and information content. In order to overcome 
the insufficiency of single remote sensing data source during 
change detection, and to make use of the complementary 
characteristics of these two kinds of data source, this paper 
develops the idea and presents the approach to land use/cover 
change detection by different temporal SAR and optical image 
integration. Data adopted in this research are SPOT5 image and 
Radarsat-1 SAR data. With the successful launch of the high- 
resolution and polarized SAR data since the end of last year, 
such as Cosmo-SkyMed (Italy), TerraSAR-X (German) and 
Radarsat-2 (Canada), it provides us more choices for SAR data 
selection. With the abundant textural information and rich 
polarization information presented in these data, change 
detection accuracy will be improved considerablely. 
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