Aiming at the mismatching problem of traditional semi-global matching (SGM) in high-resolution images with weak textures and disparity discontinuities, an SGM algorithm that takes into account the image segmentation information is proposed. In the cost calculation stage of this algorithm, the size of the matching window is first adaptively adjusted according to the image segmentation information, and the improved Census transform with different state information is used to calculate the initial cost, which solves the traditional algorithm's dependence on the center pixel of the Census transform window and reduces the matching time. In the cost aggregation stage, the image segmentation information is organically combined with the global energy function of the traditional SGM algorithm to improve the matching effect of the algorithm in weak texture and depth discontinuous regions. Finally, the optimized disparity map is obtained through left-right consistency detection and sub-pixel refinement. The proposed algorithm is verified by using the standard data of the middlebury platform. The experimental results show that the average mismatch rate is 4.54%. Compared with the traditional SGM algorithm and some improved algorithms, the proposed algorithm can obtain higher matching accuracy in the weak texture and discontinuous disparity areas of the image.