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The accuracy of Digital Surface Models (DSMs) generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images. It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced, in which multiple DSMs are combined through computational approaches into a single, more accurate, and complete DSM. However, accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs. Therefore, we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points. The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.