基于AdaBoost模型和mRMR算法的小麦白粉病遥感监测

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。

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

Abstract: 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 three 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 (mRMR) 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 three group features obtained by three different feature selection methods mentioned above. Model with mRMR algorithm combining AdaBoost method (mRMR-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 mRMR-AdaBoost model were the highest than those of models with CA algorithm and models with mRMR algorithm which constructed by FLDA and SVM methods. It indicated that mRMR-AdaBoost model had a better performance than the other five models. The validation results showed that, the overall accuracies and the Kappa coefficient of AdaBoost models with CA and mRMR algorithms were 81.4%, 0.685 and 88.4%, 0.807, respectively, and they were higher by 27.9%, 27.9%, 14.0% and 9.3% than those of FLDA and SVM models with corresponding selection algorithms. The overall accuracies of FLDA, SVM and AdaBoost models with mRMR algorithm were higher by 7.0%, 11.7% and 7.0% than those of the corresponding methodological models with CA algorithm. Furthermore, mRMR-AdaBoost model had the lowest omission and commission error in all six models. Additionally, compared with the spatial distribution results of wheat powdery mildew severities which mapped by SVM and AdaBoost models and combined with surface survey results of wheat powdery mildew occurrence severity, the mapping results of mRMR-SVM model and two AdaBoost models were similar and close to ground survey results, and among them, the results of mRMR-AdaBoost model was the closest to ground reality than the others''. These results revealed that for remote sensing monitoring of crop disease, the application of AdaBoost method had a good prospect, and for feature variables selecting of crop disease monitoring model, the minimal redundancy maximal relevance algorithm had more advantages than CA algorithm. The study results can provide a method reference for monitoring of other crop diseases.