Zhang, X. G., 2000. Introduction to statistical learning theory
and support vector machines. Journal of Automatization, 26(1), ACKNOWLEDGEMENTS
pp.32-42.
This work was supported by Key Laboratory of Geo-
Kato, Z., and Pong, T.C., 2006. A Markov random field image informatics of State Bureau of Surveying and Mapping (Grant
segmentation model for color textured images. Image and No. 200727).
Vision Computing 24, pp. 1103-1114.
Class
Water
bodies
Road
Trail
Shrub and
grassland
Agriculture
Building
Row
Total
User’s
Accuracy
(%)
Water bodies
108
0
0
0
0
0
108
100.0
Road
0
134
0
2
4
0
140
95.71
Trail
0
0
81
1
0
1
83
97.59
Shrub and grassland
0
0
10
170
22
0
202
84.16
Agriculture
16
2
0
32
164
2
216
75.93
Building
0
0
0
0
0
89
89
100.0
Column Total
124
136
91
205
190
92
838
Producer’s Accuracy (%)
87.10
98.53
89.01
82.93
86.32
96.74
Note: Total samples = 838 pixels, correctly classified samples =746 pixels, overall accuracy 89.02%, kappa coefficient k = 0.87.
Table 1. The confusion matrix of classification based on pixel-based SVM.
Class
Water
bodies
Road
Trail
Shrub and
grassland
Agriculture
Building
Row
Total
User’s
Accuracy
(%)
Water bodies
124
0
0
0
0
0
124
100.0
Road
0
136
0
0
0
0
136
100.0
Trail
0
0
77
0
0
0
77
100.0
Shrub and grassland
0
0
14
205
13
0
232
88.36
Agriculture
0
0
0
0
177
6
183
96.72
Building
0
0
0
0
0
86
86
100.0
Column Total
124
136
91
205
190
92
838
Producer’s Accuracy (%)
100.0
100.0
84.62
100.0
93.16
93.48
Note: Total samples = 838 pixels, correctly classified samples =805 pixels, overall accuracy 96.06%, kappa coefficient k = 0.95.
Table 2. The confusion matrix of object-oriented classification based on MRF and SVM.
