基于城市遥感卫星影像对的立体匹配

To solve the problem of poor stereo matching effect owing to numerous shadows and disparity step regions in urban satellite remote sensing images, a stereo matching algorithm suitable for urban remote sensing image pairs was proposed. The matching cost function, cost aggregation method, disparity, and optimization method used by the algorithm were investigated. First, the matching cost function was improved and the multi-order weighted census algorithm was used to reduce the influence of noise and other factors. Subsequently, the constraints of the building edge information were added to the cost aggregation. Finally, regarding disparity refinement, the disparity map was optimized by fully considering the characteristics of urban building morphology. The experimental results show that on the Middlebury dataset, the accuracy of this algorithm is 4.54% higher than that of the classic SGM algorithm. On the WorldView-2 stereo image pair in the urban area, the variance of the building roof elevation is 0.71. The requirements to obtain high-precision disparity maps are met based on urban satellite remote sensing images and good conditions for urban three-dimensional reconstruction are provided.