High resolution remote sensing images contain rich details and spectral information. Consequently, they have important applications in land use, building detection, land cover classification, and other ground detection scenarios. This study proposed a superpixel segmentation algorithm that combined grafting attention and detail perception to address the issues of incorrect segmentation of texture regions and loss of small targets. First, an edge-guided spatial detail module was constructed to weaken the differences in merging different levels and compensate for the loss of spatial detail information during the sampling process. Second, a grafting attention mechanism was designed to enhance local region features and consequently improve the ability to extract edges of small targets. Finally, the concept of texture aware loss was proposed for enhancing the expression of texture regions through adaptive adjustments to the texture weights of feature maps. Compared to existing mainstream superpixel segmentation algorithms, the experimental results using the proposed algorithm on remote sensing image datasets yield segmentation error and boundary recall performance indicators of 0.15% and 0.87%, respectively. This indicates an improvement in the segmentation performance of the model for texture and small target areas.