Based on the WorldView-2 high resolution remote sensing image in 2011, this study uses object-based classification method and four traditional pixel-based classification methods to extract study area land use information respectively. Then, Visual interpretation map is functioned as reference map to acquire each classification methods overall accuracy and to assess the each classification result and each class type from the aspects of quantity disagreement and allocation disagreement. The result shows that: (1) The average overall classification accuracy is 75.00%. Among all the classification methods, the object-based classification method acquires the highest accuracy, 84.25%. The maximum likelihood classification method gets the lowest accuracy, 62.00%. (2) In all classification methods, the object-based classification method has obtained the lowest quantity disagreement, 4.25%. The others in sequence are as follows: neural net classification method < support vector machine method < mahalanobis distance method < maximum likelihood method. As to allocation disagreement, the support vector machine method has acquired the lowest value, 5.75%. The others in sequence are maximum likelihood method < neural net classification < mahalanobis distance method < object-based classification method. (3) As to separate class type, farmland does great influence on image's overall classification accuracy, whose quantity disagreement sequence is the maximum likelihood method(28.75%) > mahalanobis distance method(21.50%) > support vector machine method(14.75%) > neural net method(11.00%) > object-based method(3.00%). As for allocation disagreement, the sequence is object-based method(10.50%) > neural net method(5.00%) > support vector machine method(1.50%) > maximum likelihood method(0.50%) > mahalanobis distance method(0.00%).