Remote sensing image classification is an important part of image analysis, and post-classification accuracy assessment is the main basis for determining the effect of image classification. At present, random verification points are often used as assessment parameters in object-oriented classification, which may easily lead to inaccurate classification results. An object-oriented classification accuracy assessment method based on regular verification points is proposed in this paper. Regular and random verification points are used to evaluate the classification accuracy by using support vector machine, CART (classification and regression tree ) decision tree, and K nearest neighbor classification. Experimental results show that the proposed method has higher classification accuracy than traditional methods based on random verification points. The optimal overall classification accuracy of the three classification methods based on the regular verification points reaches 87.92%, 91.94%, and 94.63%, respectively, which are better than the accuracy assessment results of random verification points based methods.