An object-oriented contour constrained generalized gradient vector flow (GGVF) Snake model for building boundary optimization method was proposed by analyzing the features of buildings and the problems of conventional methods that the irregular boundary problem caused by false classification is pervasive. Each building boundary was automatically extracted as the initial boundary of GGVF Snake based on the initial building results obtained by classification. The circumscribed rectangle of each building boundary was obtained for object clipping in order to extract each building sub-image object. Canny edge detection was performed on each sub-image object to acquire each building boundary result that will be combined with Hough transform to extract linear features. Then the linear features were inputted into the generalized gradient vector flow model’s iterative solution to minimize the energy function. Then the precise of the object-level building contour was enhanced. The experimental results show that the proposed method can automatically obtain the initial building boundary information and improve the automation degree of optimization. The object-level contour detection and the addition of line features help GGVF Snake quickly fit and accurately smooth the building boundary to enhance the contour accuracy. The overall accuracy and comprehensive value of the proposed method are improved compared with other contour optimization methods. Thus the method can be used as an effective post-processing optimization method for the classification principle to improve the building extraction precision.