To overcome nonconformity between the boundaries of a region using a segmentation algorithm and real object boundary

a segmentation optimization algorithm based on the fusion information of boundaries is presented in this study using the homogeneity characteristic of the objects and the salient boundary information in a high-resolution remote-sensing image. First

the canny edge filter was applied to extract edge information for remote-sensing images

and then the edge discontinuity problem was addressed to obtain the closed boundary. Second

the boundary and the initial segmentation results were fused

and new segmentation results were obtained. Finally

the new segmentation results under the closed boundary constraint were merged to obtain the optimization segmentation results based on the gray similarity criterion. This study presents a segmentation algorithm used to optimize the segmentation results obtained via MeanShift and eCognition. Furthermore

the rightly-segmented ratio (RR) is increased by 4% compared with the initial results

thereby verifying the effectiveness of the optimized algorithm. The optimized algorithm has extensive applicability and can optimize methods based on regions

boundaries

and clustering. This algorithm cannot only maintain the regional integrity of the object in high-resolution remote sensing

but can also keep the details of the object edge

thereby improving segmentation accuracy.