| 基于无人机多光谱影像特征融合的小麦倒伏监测 |
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| 引用本文： | 魏永康,杨天聪,臧少龙,贺利,段剑钊,谢迎新,王晨阳,冯伟.基于无人机多光谱影像特征融合的小麦倒伏监测[J].中国农业科学,2023,56(9):1670-1685. |
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| 作者姓名： | 魏永康 杨天聪 臧少龙 贺利 段剑钊 谢迎新 王晨阳 冯伟 |
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| 作者单位： | 河南农业大学农学院，郑州 450000 |
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| 基金项目： | 河南省科技研发计划联合基金（优势学科培育类）(222301420104) |
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| 摘 要： | 【目的】小麦倒伏严重影响小麦光合及成熟进程，进而造成小麦减产及品质下降。为快速精确获取倒伏信息，评估无人机遥感监测小麦倒伏的能力，构建小麦倒伏监测模式，为灾情评估、保险理赔及灾后补救提供技术支持。【方法】利用近地无人机获取包含红、绿、蓝、红边和近红外5个多光谱波段图像，经过预处理飞行高度50 m的小麦冠层图像，得到分辨率为1.85(cm/像素)的数字正射影像图（DOM）和数字表面模型（DSM），从中提取光谱特征、高度特征和光谱纹理共3类特征信息；采用支持向量机（SVM）和随机森林（RF）2种分类器对6种不同特征集组合进行倒伏分类比较，使用准确率（Acc）、精确率（Pre）、召回率（Re）和调和平均数（F1）以确定较优的特征组合和分类器；同时使用3种不同的特征集筛选方法（套索算法Lasso、随机森林递归算法RF-RFE和Boruta算法）对优化的特征子集进行综合评价，确立适宜的倒伏分类评价方法。【结果】单一特征的光谱和纹理及其组合对小麦倒伏的分类评价结果较差，“椒盐现象”严重，在此基础上融合DSM信息的分类精度显著提高。采用随机森林分类器对光谱特征、纹理特征和高度特征进行特征集组合，小麦...
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| 关 键 词： | 冬小麦 无人机 多光谱 特征融合 倒伏 |
| 收稿时间： | 2022-08-11 |
Monitoring Wheat Lodging Based on UAV Multi-Spectral Image Feature Fusion |
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| WEI YongKang,YANG TianCong,ZANG ShaoLong,HE Li,DUAN JianZhao,XIE YingXin,WANG ChenYang,FENG Wei.Monitoring Wheat Lodging Based on UAV Multi-Spectral Image Feature Fusion[J].Scientia Agricultura Sinica,2023,56(9):1670-1685. |
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| Authors: | WEI YongKang YANG TianCong ZANG ShaoLong HE Li DUAN JianZhao XIE YingXin WANG ChenYang FENG Wei |
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| Institution: | College of Agronomy, Henan Agricultural University, Zhengzhou 450000 |
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| Abstract: | 【Objective】The wheat lodging seriously affected the process of photosynthesis and maturity, which led to the reduction of yield and quality. In order to obtain lodging information quickly and accurately, UAV ability of remotely monitoring wheat lodging was evaluated, and a wheat lodging monitoring model was built, so as to provide a technical support for disaster assessment, insurance claims and post-disaster remediation.【Method】Five original multispectral band images, including red, green, blue, red-edge and near-infrared, were acquired by near-ground UAV. The wheat canopy image with flying height of 50 m was preprocessed to obtain digital orthophoto map (DOM) and digital surface model (DSM) with a resolution of 1.85 (cm/pixel), three types of feature information were extracted, namely spectral features, height features, and texture features. Support Vector Machine (SVM) and Random Forest (RF) were used to compare the lodging classification of six different feature set combinations, and accuracy (Acc), precision (Pre), recall (Re) and harmonic mean (F1) were used to determine the best feature combination and classifier. At the same time, three different feature set screening methods (Lasso algorithm, random forest recursive algorithm RF-RFE and Boruta algorithm) were used to comprehensively evaluate the optimized feature subset, and to establish an appropriate evaluation method for lodging classification.【Result】The results showed that the single feature spectrum and texture as well as their combinations had poor classification and evaluation results for wheat lodging, and the “salt and pepper phenomenon” was serious. On this basis, the classification accuracy of DSM information fusion was significantly improved. The random forest classifier was used to combine spectral features, texture features and height features, and the classification accuracy of wheat lodging identification reached 91.48%. In order to reduce the number of feature set variables, three feature optimization methods were adopted. Compared with the full feature set, Lasso algorithm and the RF-RFE algorithm, the optimized feature subset based on the Boruta algorithm had higher classification accuracy and better overall stability. From the perspective of the mean value of the three feature combinations containing DSM, the overall classification accuracy and Kappa coefficient were improved by 0.17% and 0.01 (full feature set), 2.45% and 0.05 (Lasso), 2.87% and 0.05 (RF-RFE), respectively. Among them, spectrum-texture-DSM was the best, with the overall classification accuracy of 92.82% and Kappa coefficient of 0.86.【Conclusion】The study showed that the Boruta algorithm could effectively optimize the number of feature subsets of the spectrum-texture-DSM combination, allow fewer feature parameters to participate in the classification, and obtain higher classification accuracy. Meanwhile, a multi-feature combination-Boruta-RFC technology model was established for accurately monitoring wheat lodging, which provided a reference for wheat disaster assessment and the formulation of remediation measures. |
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| Keywords: | winter wheat UAV multispectral feature fusion lodging |
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