基于AdaBoost模型和mRMR算法的小麦白粉病遥感监测  

Remote sensing monitoring of wheat powdery mildew based on AdaBoost model combining mRMR algorithm

作　　者：马慧琴[1,2] 黄文江[2] 景元书[1] 董莹莹[2] 张竞成[3] 聂臣巍 唐翠翠 赵晋陵[4] 黄林生[4] 

机构地区：[1]南京信息工程大学应用气象学院气象灾害预报预警与评估协同创新中心,南京210044 [2]中国科学院遥感与数字地球研究所数字地球重点实验室,北京100094 [3]杭州电子科技大学生命信息与仪器工程学院,杭州310018 [4]安徽大学电子信息工程学院,合肥230039

出　　处：《农业工程学报》2017年第5期162-169,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中国科学院国际合作局对外合作重点项目(131211KYSB20150034);国家重点研发计划项目(2016YFD030702);国家自然科学基金国际合作项目(61661136004);国家自然科学基金项目(41271412;41601467);江苏省普通高校自然科学研究资助项目(15KJA170003)

摘　　要：除选择合适的建模方法外,选择合适的特征选择算法来优选建模特征对提高作物病害的遥感监测水平具有重要作用。选取陕西省关中平原西部小麦白粉病为对象,基于Landsat 8遥感影像共提取了18个特征变量,通过相关性分析(correlation analysis,CA)和最小冗余最大相关(minimum redundancy maximum relevance,mRMR)2种特征选择算法筛选出了2组不同的特征变量,分别将其输入Fisher线性判别分析(Fisher linear discriminant analysis,FLDA)、支持向量机(support vector machine,SVM)和AdaBoost 3种方法,构建小麦白粉病发生严重程度监测模型,并对其进行精度验证与对比分析。结果表明,2种AdaBoost模型对小麦白粉病发生严重程度的总体监测精度分别比FLDA模型和SVM模型高出27.9%、27.9%和14.0%、9.3%,mRMR算法筛选特征所建FLDA、SVM及AdaBoost监测模型的总体监测精度分别比CA筛选特征所建模型高出7.0%、11.7%和7.0%,且mRMR算法筛选特征结合AdaBoost方法所建监测模型的精度和Kappa系数分别为88.4%和0.807,为所有模型中最高。说明将AdaBoost方法用于作物病害遥感监测效果较好,在作物病害监测模型的特征变量选择中mRMR算法比常用CA算法更具优势。研究结果可为其他作物病害遥感监测提供方法参考。Wheat powdery mildew has become one of the most serious wheat diseases in China,so it is necessary for using modern remote sensing information technology to improve the monitoring ability of the disease for guiding disease prevention and ensuring Chinese grain production safety.Feature selection was one of the key issues for establishing inversion models,and the use of good feature selection method would make a direct impact on disease classification accuracy.In this study,the Landsat 8 remote sensing image was used to extract total eighteen characteristic variables.Then,we got two groups different features,and Wetness,land surface temperature（LST）and shortwave infrared water stress index（SIWSI）were obtained by correlation analysis（CA）algorithm,and Greenness,Wetness,LST,re-normalized difference vegetation index（RDVI）and simple ratio（SR）were obtained by minimum redundancy maximum relevance（mR MR）algorithm.The basic idea of AdaBoost method was through a certain category by using numbers of weak classification classifiers to get a strong classifier which has great classification ability for improving classification accuracy.It generally was used to solve the binary classification problem,and we reformed it to solve three classification problems through dichotomous dismantling way of one against all.Then,we used it and common classification method Fisher linear discriminant analysis（FLDA）and support vector machine（SVM）to monitor wheat powdery mildew occurrence severity（healthy,slight,severe）in western Guanzhong Plain,Shaanxi province,China through two group features obtained by two different feature selection methods mentioned above.Model with mR MR algorithm combining AdaBoost method（mR MR-AdaBoost model）produced the highest Spearman relevance value（0.868）in six models.Moreover,the values of Somers’D,Goodman-Kruskal Gamma,and Kendal’s Tau-c of mR MR-AdaB oost model were the highest than those of models with CA algorithm and models with mR MR algorithm which constructed

关 键 词：病害 遥感 监测 小麦 mRMR算法 ADABOOST方法 

分 类 号：S4[农业科学—植物保护] TP79[自动化与计算机技术—检测技术与自动化装置]