基于GraphCut算法的大比例尺地理测图动态分割技术OA

In large-scale geographic mapping,non-natural transition areas present issues such as"jagged mutations"and"spikes",leading to a continuous expansion of contradictions between high frequency(details)and low frequency(overall appearance).When the tones of geographic features overlap,the segmentation results suffer from ambiguous boundary rela-tionships and lower accuracy.To address this,this paper proposed a dynamic segmentation technique based on the GraphCut algorithm.By referencing the Euler-Lagrange equation and calculating updated diffusion coefficients,the boundary relation-ship in large-scale geographic mapping was iteratively updated,alleviating the contradiction between high frequency(details)and low frequency(overall appearance).Additionally,the red-green-blue(RGB)color model data from multi-scale geographic mapping sequence was converted to hue,saturation,and value(HSV),while simultaneously transforming the candidate range for large-scale geographic mapping to reduce the impact of overlapping tones.A self-adaptive scale selec-tion mechanism for boundary relationships was designed,employing a dynamic selection logic and creating a three-dimensional hyperplane segmentation function for the candidate range.By comparing information entropy and empirical val-ues,the dynamic segmentation results for large-scale geographic mapping at various scales were determined.The experimen-tal results show that after applying the proposed method,a significant number of boundary relationship features for the incon-sistent regions are obtained,with an accuracy of 0.989,a recall rate of 0.962,an F-measure value of 0.973,and an inter-section over union(IoU)value of 0.974.The candidate ranges in the salient map are marked with white boxes,accurately covering the core areas of urban and transportation network density in the mapping.The dynamic segmentation shows excel-lent performance and high accuracy,providing reliable technical support for high-precision automated extraction of geo-graphic information.