Non-navigational TIN-DDM Automatic Generalization Algorithm for Optimizing Critical Rolling Ball Radius

To effectively improve the computational performance of the current non-navigational TIN-DDM automatic generalization algorithm in seabed topographic forms maintenance, and enhance the computational efficiency in engineering applications, this paper proposes a non-navigational TIN-DDM automatic generalization algorithm for critical rolling ball radius optimization. This algorithm deeply analyze the physical meaning of the critical rolling ball radius, clarify the correlation between the critical rolling ball and the TIN-DDM sampling point normal vector, based on the accurate calculation of the normal vector of each sampling point, the numerical analysis of the spatial position of the sampling point and the critical rolling ball radius, a calculation process for the positive and negative critical rolling ball radius is constructed, and a more accurate critical rolling ball radius value is obtained by directly applying this value to existing generalization algorithms. Experiments show that compared with the contrast algorithm, the comprehensive results of the algorithm in this paper have been improved to a certain extent in both the topographic forms maintenance and terrain accuracy of TIN-DDM, and the algorithm running rate has also been relatively improved.