196 
1 2 3 4 5 6 
Fig.7. The object-oriented classification map based on MRF 
and SYM. 
1 2 3 4 5 6 
Fig.8. The pixel-based classification map based on SVM. 
3.3 Accuracy Assessment 
Comparing Fig.7 and Fig.8, we notice that Fig.8 appears “salt 
and pepper” phenomenon. Table 1 shows the confusion matrix 
of classification based on pixel-based SVM, and table 2 shows 
the confusion matrix of object-oriented classification based on 
MRF and SVM. 
The results show that the overall accuracy of pixel-based SVM 
is 89.02%, whereas the overall accuracy of the object-oriented 
classification based on MRF and SVM is 96.06%. The overall 
accuracy is improved by 7.04%. In addition, the user’s 
accuracy of each class using object-oriented classification is 
higher than the pixel-based SVM method. In particular, the land 
cover class, water and road, are obviously distinguished by the 
object-oriented classification method. 
4. CONCLUSION AND DISCUSSION 
The study proposed a new object-oriented land cover 
classification method based on MRF and SVM using HR 
QuickBird data, which built connection between the domains of 
raster analysis and vector analysis. The MRF segmentation can 
generate image regions being homogeneous in spectral 
distribution and textural properties. The SVM classification 
method could solve sparse sampling, non-linear, high 
dimensional data, and global optimum problems. 
Compared with the pixel-based SVM classification method, the 
results indicated that the proposed object-oriented classification 
method can improve the efficiency of training and classification, 
get accurate classification results, and update GIS database in a 
quick and convenient way. Because the proposed classification 
method could detect and reclassify the small misclassified 
objects in an iteration way, we may get better classification 
result. Additionally, this method has the potential to apply in 
land cover monitoring at regional and global scale, object 
detection, feature extraction, etc. 
For the purpose of accurately classifying different ground 
objects, it is important to consider scale effects and additional 
effective features in the near future. 
REFERENCE 
Baatz, M., et al., 2004. eCognition User Guide 4, 
www.definiens-imaging.com(Last visited March 10, 2007). 
Bruzzone, L., Carlin, L. and Melgani, F., 2004. A multilevel 
hierarchical approach to classification of high spatial resolution 
images with support vector machines. IGARSS '04. pp. 20-24. 
Burges, C. J. C., 1998. A tutorial on support vector machines 
for pattern recognition. Knowledge Discovery and Data Mining, 
2(2), pp. 121-167. 
Chang, C. C. and Lin, C. J., 2001. LIBSVM: a library for 
support vector machines, http://www.csie.ntu.edu.tw/~cjlin/, 
(Last visited March 28, 2007). 
Cuozzo, G., Elia, C. D. and Puzzolo,V., 2004. A method based 
on Tree-Structured Markov Random Field for forest area 
classification. IGARSS’04. pp. 2352-2354. 
Li, H.T., Gu, H.Y., Han, Y.S. and Yang, J.H., 2007. Object- 
oriented classification of very high-resolution remote sensing 
imagery based on improved CSC and SVM. Geoinformatics’07. 
Li, S.Z., 2001. Markov random field modeling in image 
analysis. Springer.