基于无人机RGB影像的大豆种植区提取方法研究

Study on extraction method of soybean planting areas based on unmanned aerial vehicle RGB image

针对皖北大豆主产区——阜阳市太和县境内的典型破碎农田环境,基于无人机RGB影像与多种机器学习算法构建大豆遥感识别模型,据此实现种植区的精细制图。除了R、G、B波段的相对反射率外,还选取了3个HLS色彩空间分量、9个可见光植被指数、6个纹理特征和1个几何特征共22个候选特征变量扩大RGB影像的信息量。采用与分类器相耦合的特征选择方法筛选出针对4种算法——随机森林(RF)、支持向量机(SVM)、极端梯度提升(XGBoost)和BP神经网络(BPNN)的特征子集,并基于最优特征子集和相应的算法构建监督分类模型进行大豆分布区的提取制图,并对比效果差异。结果表明,在4种算法下,基于优选特征子集构建的监督分类模型的提取效果全部优于基于原始RGB波段的提取效果,其中,RF算法结合优选特征的表现最佳,总体精度为93.96%,Kappa系数达到0.87。总的来看,RF算法结合特征优选方法在无人机大豆遥感识别中具有较大的应用潜力,通过特征筛选可在较高的分类精度与较少的数据量之间取得平衡。

A typical fragmented farmland in Taihe County,Fuyang City,which is located in the main soybean producing area in the northern Anhui Province,China,was taken as the study area in the present study.Soybean planting area extraction models were constructed based on the unmanned aerial vehicle(UAV)RGB images and different machine-learning algorithms to perform fine crop mapping.In addition to the relative reflectances of R,G,B bands,the 3 components of HLS color space,9 visible light vegetation indices,6 texture features and 1 geometrical feature,were selected as the candidate feature variables.Then,a feature selection method coupled with classifier was employed to single out four optimum feature-subsets for the algorithms of random forest(RF),support vector machine(SVM),extreme gradient boosting(XGBoost)and BP neural network(BPNN).Corresponding models were subsequently constructed based on the filtered subsets of features and algorithms for mapping,and their performances were examined.It was shown that the optimum feature-subsets of the four algorithms outperformed the original RGB bands in terms of extraction accuracy,among which RF exhibited the best performance,as its overall accuracy was 93.96%,and the Kappa coefficient reached 0.87.In general,the RF algorithm combined with optimum feature-subset showed great potential in soybean planting area extraction based on UAV platform,and the feature selection method could achieve a balance between higher classification accuracy and less data volume,which had certain practical significance.