摘要
反距离加权插值(inverse distance weighting,IDW)的精度受到空间邻近度、距离衰减系数及最邻近点数等多个参数共同的影响。然而,目前的IDW插值算法大多仅考虑单参数的调优,或对各参数独立调优,难以实现插值模型的整体优化。此外,传统的IDW插值算法没有顾及各向异性对空间邻近度的影响。本文提出一种顾及空间各向异性的多参数协同优化IDW插值算法(PIDW)。首先,引入距离调节参数以及方向参数,将经典各向同性的欧氏空间距离扩展为各向异性的“椭圆”距离;然后,引入粒子群优化算法对最邻近点数、距离衰减系数、距离调节及各向异性方向的多参数进行协同优化,获得插值精度的偏差与方差在全局意义下的满意解。试验采用两个不同尺度的空间数据验证了PIDW算法的插值效果,结果表明本文的插值算法能够显著地提高各向异性环境下IDW插值算法的精度。通过与经典的IDW及其改进算法的IDW、普通克里金及顾及各向异性的普通克里金算法的比较分析,进一步证实了PIDW具有较好的插值效果。
The inverse distance weighting(IDW)is one of widely accepted methods employed for predicting an unknown spatial value using known values observed at a set of sample locations.Many factors including spatial proximity,sample size and distance decay affect the estimation of the method simultaneously.However,most of IDW-based methods do not consider the effect of spatial anisotropy.In addition,most of these methods cannot predict accurate interpolating values because they only involve a sole factor for optimization.To obtain accurate missing values and high-resolution spatial surface model,the paper proposes a novel multiple parameters synchronization optimization IDW algorithm which involves anisotropy.The proposed method simultaneously optimizes the parameters of anisotropy,neighbor size and distance decay to improve the accuracy of IDW interpolation by particle swarm optimization(PSO).Moreover,scaling and direction factor are introduced to capture the varying of distance in different direction,and a new fitness function is schemed via cross validation technique.Two different resolution datum are selected to validate the effectiveness of proposed method,and the experimental results demonstrate that our method significantly outperform the typical IDW method.Comparisons with the recently developed IDW-based method,i.e.CIDW(classical IDW),FIDW(four quadrant IDW),AIDW(adaptive-IDW)and KAIDW(K-nearest neighbor adaptive IDW),OK(ordinary Kriging),as well as AnisOK(anisotropic Ordinary Kriging)are also implemented,and the experiments show that our algorithm can achieve the best interpolation results in terms of reliable and accuracy.
作者
颜金彪
吴波
何清华
YAN Jinbiao;WU Bo;HE Qinghua(School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China;National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, Hengyang 421002, China)
出处
《测绘学报》
EI
CSCD
北大核心
2021年第5期675-684,共10页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(41961055,41771150,41830108)
湖南省教育厅优秀青年/一般项目(19B078,19C0272)
国家重点研发计划(2018YFE0207800)
湖南省社会科学规划项目(17ZDB051)。
关键词
反距离加权插值(IDW)
空间邻近度
各向异性
多参数优化
粒子群算法
inverse distance weighting(IDW)
spatial proximity
anisotropy
multiple parameters optimization
Particle Swarm optimization
