基于无人机多光谱影像特征融合的小麦倒伏监测

doi:10.3864/j.issn.0578-1752.2023.09.005

Abstract

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.

Key words: winter wheat, UAV, multispectral, feature fusion, lodging

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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References 45

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植被指数 Vegetation index	公式 Formula	参考文献 Reference
差值植被指数 DVI Difference vegetation index	Rred.edge-Rred	[21]
归一化植被指数 NDVI Normalized difference vegetation index	(Rnir-Rred)/(Rnir-Rred)	[22]
比值植被指数 RVI Ratio vegetation index	Rnir/Rred	[21]
优化型土壤调节植被指数 OSAVI Optimized soil adjust vegetation index	(1+0.6)(Rnir-Rred)/(Rnir+Rred+0.16)	[23]
绿度叶绿素植被指数GCI Green chlorophyll vegetation index	(Rnir/Rgreen)-1	[24]
绿度宽波段植被指数GWDRVI Green wide band vegetation index	0.9/1.1+(0.1×Rnir- Rgreen)/(0.1×Rnir+Rgreen)	[25]
非线性植被指数NLI Nonlinear vegetation index	(Rnir²- Rred)/(Rnir²+Rred)	[26]
红边归一化植被指数RENDVI Normalized vegetation index with red edge	(Rred.edge-Rred)/(Rred.edge+Rred)	[16,27]

光谱特征 Spectral feature	倒伏小麦Lodging wheat		正常小麦 Normal wheat
光谱特征 Spectral feature	均值 Mean (MN)	标准差 Standard deviation (SD)	均值 Mean (MN)	标准差 Standard deviation (SD)
蓝光 Blue	0.0292	0.0055	0.0173	0.0037
绿光 Green	0.1315	0.0253	0.0733	0.0184
红光 Red	0.2134	0.0366	0.1632	0.0200
红边Red edge	0.2191	0.0344	0.1404	0.0241
近红外 Near infrared	0.4635	0.0620	0.4278	0.0378
归一化植被指数 NDVI	0.8449	0.0780	0.9107	0.0367
比值植被指数 RVI	14.1055	4.2096	25.7437	7.8395
差值植被指数 DVI	0.4256	0.0659	0.4079	0.0402
绿度叶绿素植被指数 CI_green	5.5450	1.2144	9.6541	2.0189
非线性植被指数 NLI	0.0004	0.1576	0.0517	0.1144
绿度宽动态植被指数 GWDRVI	0.3520	0.0770	0.5789	0.1068
土壤调节植被指数 OSAVI	0.7397	0.0761	0.7750	0.0396
红边归一化植被指数 RENDVI	0.7053	0.1049	0.7505	0.0381

特征组合 Feature combination	支持向量机SVM				随机森林 RF
特征组合 Feature combination	准确率 Accuracy (%)	精准率 Precision (%)	召回率 Recall (%)	F1 (%)	准确率 Accuracy (%)	精准率 Precision (%)	召回率 Recall (%)	F1 (%)
光谱特征 Spectral feature	75.17	64.81	97.27	77.79	77.43	67.51	95.95	79.56
纹理特征 Texture feature	75.84	65.29	98.11	78.40	75.97	65.48	97.34	78.29
光谱-纹理 Spectrum-Texture	75.82	65.13	98.33	78.36	75.70	65.10	97.86	79.16
光谱-DSM Spectrum-DSM	84.83	75.69	97.31	85.15	91.48	91.59	89.11	90.33
纹理-DSM Texture-DSM	78.58	68.03	98.33	80.42	91.43	87.74	93.97	89.78
光谱-纹理-DSM Spectrum-Texture-DSM	78.52	67.95	9835	80.37	91.46	87.18	94.84	90.85

特征子集优化方法 Feature subset optimization method	纹理-DSM Texture-DSM		光谱-DSM Spectrum-DSM		光谱-纹理-DSM Spectrum-Texture-DSM		平均值 Mean
特征子集优化方法 Feature subset optimization method	Kappa系数 Kappa	总体精度 OA (%)	Kappa系数 Kappa	总体精度 OA (%)	Kappa系数 Kappa	总体精度 OA (%)	Kappa系数 Kappa	总体精度 OA (%)
全特征Full-feature	0.82	92.21	0.82	91.26	0.82	91.24	0.82	91.57
Lasso algorithm	0.75	87.54	0.77	88.80	0.83	91.54	0.78	89.29
RF-RFE algorithm	0.80	90.26	0.70	84.91	0.83	91.45	0.78	88.87
Boruta algorithm	0.82	91.15	0.82	91.26	0.86	92.82	0.83	91.74
平均值 Mean	0.80	90.29	0.78	89.06	0.84	91.76

Monitoring Wheat Lodging Based on UAV Multi-Spectral Image Feature Fusion

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