基于WorldView-3影像特征空间优化的随机森林算法在裸花紫珠信息提取中的研究

Random forest classification of Callicarpa nudiflora from WorldView-3 imagery based on optimized feature space

以海南省白沙县细水乡为研究区,采用在特征选择和分类提取等方面都具有明显优势的随机森林算法,对研究区内的裸花紫珠种植信息进行提取。首先基于World View-3数据生成4类不同特征变量,包括光谱特征、主成分特征、植被指数和纹理特征;其次通过随机森林分类算法对研究区裸花紫珠空间分布进行遥感提取研究;最后基于特征重要性对随机森林分类算法的特征空间进行优化,以得到最佳的随机森林分类结果,并与未优化特征空间的随机森林算法的分类结果进行比较。结果表明:①利用World View-3影像提取的裸花紫珠总体精度为89. 97%,Kappa系数为0. 84,表明随机森林算法在海南裸花紫珠识别中具有较高的分类精度和较好的适用性;②利用降维的分类特征提取裸花紫珠的总体精度为90. 4,Kappa系数为0. 85,表明随机森林算法可以有效地进行特征选择,在特征变量数据挖掘的同时,仍能保证裸花紫珠信息提取的精度,提高运行效率。该研究为栽培类药用植物资源的信息提取在特征选择和方法选择方面提供了一种新思路、方法和技术手段。

Taking the Xiushui township of Baisha county in Hainan province as the research area,the random forest algorithm with obvious advantages in feature selection and classification extraction was used to extract the information of the Callicarpa nudiflora planting in the study area.Firstly,four kinds of different characteristic variables were generated based on World View-3 data,including spectral features,principal component features,vegetation index and texture features.Secondly,the spatial distribution of the C.nudiflora in the study area was extracted by remote sensing by random forest classification algorithm.Finally,the feature space of the random forest classification algorithm was optimized based on the feature importance to obtain the best random forest classification results,and this result is compared with the classification result of the random forest algorithm of the unoptimized feature space.The results showed that:①The overall accuracy of the C.nudiflora extracted by World View-3 image was 89.97%,and the Kappa coefficient was 0.84,which indicates that the random forest algorithm had higher classification accuracy and better applicability in Hainan C.nudiflora recognition.②The overall accuracy of extracting C.nudiflora with the dimension reduction feature was 90.4,and the Kappa coefficient was 0.85,which indicates that the random forest algorithm can effectively select features.At the same time as the feature variable data mining,the precision of the information extraction of the C.nudiflora was still guaranteed,and the operation efficiency was improved.This study provides a new idea,method and technical means for information extraction of cultivated medicinal plant resources in terms of feature selection and method selection.