基于GA-SVM的GNSS-IR土壤湿度反演方法

doi:10.13700/j.bh.1001-5965.2018.0417

北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (3): 486-492.doi: 10.13700/j.bh.1001-5965.2018.0417

基于GA-SVM的GNSS-IR土壤湿度反演方法

孙波¹, 梁勇¹, 汉牟田², 杨磊¹, 荆丽丽¹, 俞永庆³

1. 山东农业大学信息科学与工程学院, 泰安 271019;
2. 北京航空航天大学电子信息工程学院, 北京 100083;
3. 中国石油化工股份有限公司胜利油田分公司海洋采油厂, 东营 257237

收稿日期:2018-07-11 出版日期:2019-03-20 发布日期:2019-04-04
通讯作者: 梁勇,E-mail:yongl@sdau.edu.cn E-mail:yongl@sdau.edu.cn
作者简介:孙波,男,博士研究生。主要研究方向:卫星导航及其应用;梁勇,男,博士,教授,博士生导师。主要研究方向:数字农业。
基金资助:
国家重点研发计划（2016YFC0803104）；北航北斗技术成果转化及产业化资金资助项目（BARI1709）；山东农业大学一流学科资金（XXXY201703）；山东农业大学重点培育学科国家自然科学基金申报项目资助计划；金华市科技特派员项目（20180109151645582）

GNSS-IR soil moisture inversion method based on GA-SVM

SUN Bo¹, LIANG Yong¹, HAN Mutian², YANG Lei¹, JING Lili¹, YU Yongqing³

1. College of Information Science and Engineering, Shandong Agricultural University, Taian 271019, China;
2. School of Electronic and Information Engineering, Beihang University, Beijing 100083;
3. Haiyang Oil Production Plant, Shengli Oil Field of China Petroleum and Chemical Corporation, Dongying 257237, China

Received:2018-07-11 Online:2019-03-20 Published:2019-04-04

摘要/Abstract

摘要： 针对提高大范围土壤湿度测量精度的问题，研究了土壤湿度的全球卫星导航系统干涉测量法（GNSS-IR），提出了一种基于支持向量机（SVM）的土壤湿度反演模型，利用遗传算法（GA）的自动寻优功能寻找SVM的最佳参数。结果表明，GA-SVM模型在测试集上得到的土壤湿度反演值与实测值的平均绝对百分比误差（MAPE）仅为0.69%，最大相对误差（MRE）为1.22%，线性回归方程决定系数达到了0.956 9。进一步与统计回归、粒子群优化的SVM模型（PSO-SVM）及反向传播（BP）神经网络方法进行对比，结果说明：在样本数目有限的情况下，GA-SVM方法更适用于土壤湿度的GNSS-IR技术反演，且反演精度较高，泛化性能良好。

关键词: 土壤湿度, 全球卫星导航系统(GNSS), 干涉测量法(IR), 支持向量机(SVM), 遗传算法(GA)

Abstract: In order to improve the precision of soil moisture measurement in a wide range，in this paper, the global navigation satellite system interferometry and reflectometry (GNSS-IR) for soil moisture was studied and a soil moisture inversion model based on support vector machine (SVM) was proposed. In this model, the automatic optimizing function of genetic algorithm (GA) was applied to optimize the parameters of SVM. The results show that the mean absolute percentage error (MAPE), the maximum relative error (MRE) and the coefficient of determination for equation of linear regression are 0.69%, 1.22% and 0.9569 respectively between the soil moisture inverted by the proposed GA-SVM model and the ground measured values. In addition, the performance of GA-SVM model was also compared with the statistical regression, particle swarm optimization SVM model (PSO-SVM) and back propagation (BP) neural network. The comparison results show that the GA-SVM method is more suitable for the GNSS-IR soil moisture inversion than other machine learning algorithms in small training set scenario, and it has higher inversion precision and better generalization performance.

Key words: soil moisture, global navigation satellite system (GNSS), interferometry and reflectometry (IR), support vector machine (SVM), genetic algorithm (GA)

中图分类号:

P237
TB553

孙波, 梁勇, 汉牟田, 杨磊, 荆丽丽, 俞永庆. 基于GA-SVM的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2019, 45(3): 486-492.

SUN Bo, LIANG Yong, HAN Mutian, YANG Lei, JING Lili, YU Yongqing. GNSS-IR soil moisture inversion method based on GA-SVM[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2019, 45(3): 486-492.

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基于GA-SVM的GNSS-IR土壤湿度反演方法

GNSS-IR soil moisture inversion method based on GA-SVM

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