摘要
在曲线数据压缩的Ramer-Douglas-Peucker(RDP)算法中,只有那些垂距大于限差的点得以保留,而原始曲线上所有其他点则会被删除,这就使得压缩后的数据在保留点和删除点处精度不一致.通过采用总体最小二乘法对原始数据进行分段拟合,提高了压缩数据的精度.实验结果表明,与RDP算法相比,该算法可以更好地逼近原始数据,特别是当给定限差较大时,相对RDP算法的精度改善更为明显.
In the well-known Ramer-Douglas-Peucker （RDP） algorithm for polyline data compression, only the points with distance greater than a given tolerance from polyline （a chain of vertices） to segment （the first and last vertices of the polyline） are retained, while all other points on the original polyline are deleted. This gives rise to inconsistent data compression accuracy among the retained and the deleted points. In this paper, a new algorithm based on total least squares is presented, which takes each subset of a polyline as a processing unit and uses all the points on the original polyline to fit a new line. These fitted lines are intersected to form a final polyline, thus leading to improved compression accuracy. An experiment is included, which shows that compared with the traditional RDP algorithm, the proposed method has smaller approximation errors, especially for larger tolerance.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2008年第5期946-950,共5页
Journal of Xidian University
基金
国家自然科学基金资助(40401052)
关键词
数据压缩
RDP算法
总体最小二乘
data compression
Ramer-Douglas-Peucker algorithm
total least squares
作者简介
杨云（1969-），女，西安测绘研究所高级工程师，信息工程大学博士研究生，E-mail：yytoall@126.com
