The image segmentation method using division-combination mitigates the limitations of the traditional pixel-based remote sensing image segmentation algorithm, such as noise interference, low segmentation efficiency, and poor segmentation effect. Thus, this paper proposes a new split-merge-based remote sensing image segmentation method using the super-pixel and dot product representation of graphs. First, the image is divided into super-pixels using the simple linear iterative clustering (SLIC) algorithm. Second, the texture feature of each super-pixel area is measured and distance between any two areas is calculated with respect to spatial proximity. Third, each super-pixel area is mapped as a vertex of the graph. Therefore, the dot product representation of graphs is modified and used to construct a similarity matrix; thereafter, all vertices (i.e., super-pixel areas) are mapped as new vectors clustered by angular-based k-means algorithm to get the final segmentation results. The experimental results show that the proposed method has stable segmentation results, improves the accuracy of the segmentation, and achieves a better visual segmentation effect.