基于神经网络和奇异值分解方法的尼泊尔坐标变换参数

IF 0.9 Q4 REMOTE SENSING Journal of Geodetic Science Pub Date : 2019-01-01 DOI:10.1515/jogs-2019-0003
K. Ansari, P. Gyawali, P. Pradhan, Kwan-Dong Park
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引用次数: 7

摘要

摘要本文利用尼泊尔地区全球定位系统(GPS)的坐标测量记录,计算了从世界大地测量系统1984 (WGS-84)到珠穆朗玛峰基准面(1830年)和珠穆朗玛峰(1956年)的B-W扩展模型(扩展Bursa-Wolf模型)坐标转换参数。利用人工神经网络(ANN)和奇异值分解(SVD)方法确定合成坐标或建模坐标。我们利用人工神经网络技术研究了9个变换参数,并用SVD方法验证了结果。对人工神经网络和SVD方法进行比较分析,并对观察到的输出进行单向方差分析检验。分析表明,两个基准的零假设都是可以接受的,并且表明所有模型在统计上彼此显着相等。本研究的结果将补充相对更好地理解坐标转换和精确坐标分配技术,同时吸收来自不同资源的数据集。
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Coordinate transformation parameters in Nepal by using neural network and SVD methods
Abstract The present study computes B-W extension model (extended Bursa-Wolf model) coordinate transformation parameters from World Geodetic System 1984 (WGS-84) to the Everest datum namely Everest (1830) and Everest (1956) using records of coordinate measurements from Global Positioning System (GPS) observable across Nepal region. Synthetic or modeled coordinates were determined by using the Artificial Neural Network (ANN) and Singular Value Decomposition (SVD) methods. We studied 9-transformation parameters with the help of the ANN technique and validated the outcomes with the SVD method. The comparative analysis of the ANN, as well as SVD methods, was done with the observed output following one way ANOVA test. The analysis showed that the null hypothesis for both datums were acceptable and suggesting all models statistically significantly equivalent to each other. The outcomes from this study would complement a relatively better understanding of the techniques for coordinate transformation and precise coordinate assignment while assimilating data sets from different resources.
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来源期刊
Journal of Geodetic Science
Journal of Geodetic Science REMOTE SENSING-
CiteScore
1.90
自引率
7.70%
发文量
3
审稿时长
14 weeks
期刊最新文献
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