基于纹理-颜色特征与植被指数融合的冬小麦LAI估测

Winter Wheat Leaf Area Index Estimation Based on Texture-color Features and Vegetation Indices

准确、快速、无损估测叶面积指数(LAI)对于冬小麦生产管理具有重要意义。利用无人机搭载Prime ALTUM多光谱相机获取冬小麦拔节期、孕穗期、抽穗期、灌浆期多光谱图像,利用LAI-2200C型植物冠层分析仪获取地面LAI数据。通过Pearson相关性分析筛选出25个植被指数,并提取植被指数影像中8种纹理特征:对比度(CON)、熵(ENT)、方差(VAR)、均值(MEA)、协同性(HOM)、相异性(DIS)、二阶矩(SEM)和相关性(COR),以及3种颜色特征:一阶矩(M)、二阶矩(V)和三阶矩(S),再分别利用多元逐步回归模型(MSR)、支持向量回归模型(SVR)和高斯过程回归模型(GPR)构建冬小麦LAI估测模型。结果表明:相对于考虑单一类型变量,考虑结合纹理特征和颜色特征进行估测时模型精度更高;3类模型中,GPR模型估测冬小麦LAI的精度最高;所有模型中,基于纹理-颜色特征与植被指数融合的GPR模型估测冬小麦LAI精度最高(决定系数R2为0.94,均方根误差(RMSE)为0.17 m^(2)/m^(2),平均绝对误差(MAE)为0.13 m^(2)/m^(2),归一化均方根误差(NRMSE)为4.06%)。纹理特征和颜色特征能有效改善植被指数在高密度冠层下的饱和问题,能够从有限的信息中衍生得到更多信息用于更高精度地估测冬小麦LAI,从而为冬小麦长势监测和生产管理提供理论依据。

Accurate,fast and non-destructive estimation of leaf area index(LAI)is of great significance for the production and management of winter wheat.Multi-spectral images were obtained by using the Prime ALTUM camera at the joining stage,booting stage,heading stage and filling stage of winter wheat,and the LAI was measured by using the LAI-2200C plant canopy analyzer.Totally twenty-five vegetation indices were selected based on the Pearson correlation analysis.And eight texture features were extracted:contrast(CON),entropy(ENT),variance(VAR),mean(MEA),homogeneity(HOM),dissimilarity(DIS),the second moment(SEM)and correlation(COR),and three color features:mean(M),variance(V)and skewness(S)were extracted as well.Then the multiple stepwise regression(MSR),support vector regression(SVR)and Gaussian process regression(GPR)models were used for winter wheat LAI inversion.The results showed that compared with single type variable-based models,models with combined texture and color features produced greater estimation accuracy;among the three types of models,GPR model outperformed the other two models in estimating winter wheat LAI;among all models,the GPR model with texture-color features and vegetation indices obtained the best estimation accuracy,with coefficient of determination(R2)of 0.94,root mean square error(RMSE)of 0.17m^(2)/m^(2),mean absolute error(MAE)of 0.13m^(2)/m^(2),and normal root mean square error(NRMSE)of 4.06%.The extraction of texture and color features can solve the oversaturation issue of vegetation indices under high-density canopy conditions,and more information can be derived for more accurate estimation of winter wheat LAI,which provided theoretical basis for winter wheat growth monitoring,production and management.