Abstract:
Objectives To address the limitations of existing non-navigational triangulated irregular network (TIN)-based digital depth model (DDM) generalization algorithms in maintaining seabed morphology and improving computational efficiency, this paper aims to develop an optimized automatic generalization algorithm by refining the critical rolling ball radius. The goal is to enhance both the accuracy of morphology preservation and the practical applicability of DDM in engineering projects.
Methods The proposed algorithm first investigates the physical significance of the critical rolling ball radius and establishes its relationship with the normal vectors of TIN-DDM sampling points. By precisely calculating the normal vectors of each sampling point, the method derives a novel workflow for determining positive and negative critical rolling ball radii based on spatial analysis of sampling point locations and radius values. This optimized radius is then integrated into existing generalization algorithms to improve their performance.
Results Experimental comparisons demonstrate that the proposed algorithm achieves better preservation of seabed morphology and higher terrain accuracy in TIN-DDM generalization compared to conventional methods. Additionally, the computational efficiency of the algorithm is significantly enhanced, with reduced runtime while maintaining or improving output quality.
Conclusions We successfully validate the effectiveness of the critical rolling ball radius optimization approach in enhancing both the accuracy and efficiency of TIN-DDM generalization. This method provides a robust framework for maintaining complex seabed features during data simplification, offering practical benefits for applications such as marine engineering and hydrographic surveying. The integration of normal vector analysis and adaptive radius computation represents a notable advancement in automated DDM generalization techniques..
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