The Inter national Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1079 
2.4 Uncertainty of Change Detection Result 
Uncertainty, or error, exists from remote sensing data 
acquisition to each stage of data processing. Understanding the 
nature and spatial distribution of uncertainty during the process 
of change detection result analysis, can reduce the risk of wrong 
decision based on the uncertain data. Based on the classification 
results, there are several uncertainty propagation models of 
change detection such as the product rule-based approach, the 
certainty factor-based approach (Shi and Ehlers, 1996), and the 
probability entropy approach (Van der Wei et al., 1998; DE 
Bruin and Gorte, 2000). In this research, we extend the 
probability vector which is usually used for maximum 
likelihood classification to the object-oriented fuzzy 
classification and the multi-scale and multi-texture feature 
fusion classification based on SVM, then further analyze and 
evaluate uncertainty of the change detection result at the scale 
of pixels based on the probability entropy model, which is 
briefly described as below: 
Different temporal image classification can be regarded as 
independent. In other words, the posterior probability vector of 
a pixel at time t2 is calculated irrespective of the class or feature 
vector at the previous time, tl. According to Shannon’s 
information theory, entropy is calculated as 
M M 
M 1=1 
!X n )\o&(P(C„n IX n ))-YA C J.n IX n )\o&{P{C m !X n )) ( 1} 
m M 
Where, 
P, = P(C, n ,C jT2 /X n ,X T2 ) = P(C, n /X TX )P(C jT2 /X r2 ), 
M represents the number of classes. The range of H is from 0 to 
log 2 (MM) , which means uncertainty varies from absolute 
certain to absolute uncertain. 3 
3. EXPERIMENTAL RESULTS AND ANALYSIS 
3.1 Information Extraction from SPOT5 Image 
Both SPOT5 2.5-meter panchromatic band and four 10-meter 
multi-spectral bands are used in the object-oriented fuzzy 
classification. The first level classes are built-up land, 
vegetation, water body and bare land. Where, built-up land 
consists of three subclasses which are building, building 
shadow and road; vegetation consists of four subclasses which 
are forest, grassland, cropland (including irrigated field and 
nonirrigated field), and vegetable plot (including vegetable 
greenhouse); water body consists of two subclasses which are 
river/canal and lake/pond. NDVI image is also produced as the 
additional band for classification. During the multi-resolution 
segmentation, the weight of SPOT5 panchromatic band and 
multi-spectral bands are assigned to one respectively. After 
several times of experiments, a network of three layers is 
constructed according to features of ground objects. The setting 
of parameters is given in Table 1. 
Level 
Scale 
Color 
Shape 
Composition of shape 
Smoothness 
Compactness 
Level 3 
110 
0.8 
0.2 
0.5 
0.5 
Level 2 
90 
0.9 
0.1 
0.4 
0.6 
Level 1 
40 
0.8 
0.2 
0.4 
0.6 
Table 1. Parameter setting for multi-resolution segmentation 
After the object hierarchy has been established, the nearest 
neighbour classifier and the classifier of membership function 
are integrated to obtain spectral, textural, shape, positional and 
contextual information of image objects. Then, by establishing 
the classification rules to realize the effective separation to 
ground objects in the complex scene (Figure 1). 
Legend 
Building 
Building 
shadow 
Road 
Water 
Bareland 
Forest 
Grassland 
(Cropland 
Vegetable 
plot 
Figure 1. Fuzzy object-oriented classification result of the test 
site (partial) 
From Table 2, it can be seen that for the second level 
classification, the overall accuracy is 88.53%, Kappa coefficient 
is 0.861; for the first level classification, the overall accuracy 
reaches to 90.19% and Kappa coefficient reaches to 0.872. 
Class 
Producers 
accuracy 
% 
User's 
accuracy 
% 
Overall 
accuracy 
% 
Kappa 
coefficient 
Building 
85.28 
92.11 
building shadow 
86.03 
90.27 
road 
79.34 
81.42 
forest 
83.00 
71.18 
grassland 
84 47 
73.65 
88.53 
0.861 
cropland 
90.91 
84 43 
vegetable plot 
91.18 
82.36 
river/canal 
79.16 
76 02 
lake/pond 
92.56 
86.72 
bare land 
80.81 
85.75 
built-up land 
87.90 
92.73 
bare land 
80.81 
85.75 
90.19 
0.872 
1 
vegetation 
92 25 
85.97 
water body 
90.90 
84.48 
Table 2. Accuracy evaluation for the fuzzy object-oriented 
classification 
High performance of information extraction from optical 
imagery is the basis for change detection with SAR data at the 
information processing level.