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51.
无拓扑矢量数据快速压缩算法的研究与实现 总被引:17,自引:0,他引:17
首先论述传统矢量数据压缩算法:道格拉斯—普克法,分析将其应用于多边形边界数据压缩所造成的图形失真现象,在此基础上提出一种针对无拓扑矢量数据的快速压缩算法,并在MapInfo环境中实现该算法。 相似文献
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ZHENGZhaobao 《地球空间信息科学学报》2002,5(1):1-6
In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures. 相似文献
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The essential of feature matching technology lies in how to measure the similarity of spatial entities.Among all the possible similarity measures,the shape similarity measure is one of the most important measures because it is easy to collect the necessary parameters and it is also well matched with the human intuition.In this paper a new shape similarity measure of linear entities based on the differences of direction change along each line is presented and its effectiveness is illustrated. 相似文献
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The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used. 相似文献
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欧氏聚类算法是多元统计中的一种重要分类方法,可以将其应用于测绘领域中点云数据的分割。本文首先计算点云数据中两点之间的欧氏距离,将距离小于指定阈值作为分为一类的判定准则;然后迭代计算,直至所有的类间距大于指定阈值,完成欧氏聚类分割。具体步骤为:①利用Octree法建立点云数据拓扑组织结构;②对每个点进行k近邻搜索,计算该点与k个邻近点之间的欧氏距离,最小归为一类;③设置一定的阈值,对步骤②迭代计算,直至所有类与类之间的距离大于指定阈值。试验证明,欧氏聚类算法对不同测量技术手段获取的点云数据均具有适用性,可以成功对点云数据进行分割,分割效果良好。 相似文献
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