The Inter national Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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Classifier 
Class 
Producer's 
User's 
Overall 
Kappa 
accuracy % 
accuracy % 
accuracy % 
coefficient 
built-up land 
45.57 
72.34 
MLC 
bare land 
43.29 
63.76 
vegetation 
64.56 
52.52 
59.1143 
0.3816 
water body 
53.42 
48.72 
built-up land 
77.15 
64.39 
SVM 
bare land 
vegetation 
60.76 
70.98 
66.61 
69.8926 
0.4916 
77.54 
water body 
50.4P 
53.23 
Z value 71.6227 
Table 5. Comparison of Z statistic for MLC and SVM 
3.3 Change Detection Result and Uncertainty Analysis 
By the direct post-classification comparison, the change 
detection result can be obtained (Figure 5). Where, the black 
represents no change, the green represents positive difference 
Figure 5. Change detection result of the test site based on the 
hard-decision (partial) 
values and the red represents negative difference values (the 
classification code is: bare land 1, built-up land 2, vegetation 3 
and water body 4). In order to effectively evaluate the change 
detection accuracy, special effort sampling was done and the 
number of samples was calculated according to multinomial 
distribution recommended by Khorram (Siamak Khorram et al., 
1999). According to Table 6, the overall accuracy of the change 
detection result is 62.7%. 
Reference data 
Sum in row 
Classification 
data 
unchanged 
changed 
unchanged 
163 
37 
200 
changed 
187 
213 
400 
Sum in column 
350 
250 
600 
Table 6. Accuracy analysis for the hard-decision change 
On the basis of the extended probability vector of the 
classification result with different temporal, and according to 
the probability entropy model of uncertainty propagation, the 
spatial distribution of uncertainty of change detection result at 
the scale of pixels can be obtained, which is shown in Figure 6. 
From this figure, we can see that the blacker area represents 
smaller uncertainty of the change detection result; usually, there 
exists higher uncertainty on the fringe of different land 
use/cover types. 
Figure 6. Spatial distribution of uncertainty of the change 
detection result represented by probability entropy 
Interval of 
entropy (A) 
0-0.4 
0.4-0.8 
0.8-1.2 
1.2-1.6 
1.6-2.0 
Pixel percentage 
(%) 
39.91 
6 94 
30.97 
2.54 
14.19 
Accumulative 
percentage 
(%) 
39.91 
46.85 
77.82 
80.36 
94.55 
Interval of 
entropy (A) 
2.0-2.4 
24-2.8 
2.8-3.2 
3.2-3.6 
3.6-4.0 
Pixel percentage 
(%) 
4.81 
0.64 
0.0 
0.0 
0.0 
Accumulative 
percentage 
(%) 
99.36 
100.0 
100.0 
100.0 
100.0 
Table 7. Uncertainty of the change detection result 
Separate the entropy value into ten intervals according to the 
range of entropy, then count the number of pixels falling in 
between each interval. From Table 7, it can be seen that nearly 
95% pixels have the uncertainty less than 2.0. It means that 
although uncertainty increases during the process of error 
propagation, the change detection result by hard-decision still 
has the acceptable certainty level in this research. 
Employ the soft-decision change detection method described in 
2.3 for the further analysis (Figure 7). We can see that by 
executing the rules in turn, lots of false detected changes are 
eliminated, for example, after executing the rule 1-3, the area of 
detected changes decreased by 22.6%, and after executing the 
rule 4-6, the area of detected changes decreased by another 
28.9%.