【摘要】 利用海南省文昌市清澜港红树林保护区的无人机高光谱影像,采用递归特征消除的随机森林算法(Recursive Feature Elimination-Random Forest,RFE-RF)优选植被光谱特征和纹理特征,通过机器学习中的随机森林(Random Forest,RF)和支持向量机(Support Vector Machine,SVM)算法对研究区内的红树林树种进行精细分类,并对比分析和评价分类模型参数设置对总体精度的影响。结果表明:RF分类方法的总体精度为92.70%、Kappa系数为0.91,与传统的SVM分类方法相比,RF算法均提高了5类树种的生产者精度和使用者精度,能够有效地对红树林树种进行精细分类,可为种植资源规划和生态环境保护等方面提供技术支持。更多还原

【Abstract】 In this paper,we used the UAV hyperspectral images of the mangrove reserve at Qinglan Harbor,Wenchang,Hainan Province,and then preferentially selected vegetation spectral features and texture feature variables using Recursive Feature Elimination-Random Forest(RFE-RF). We further used the Random Forest(RF)and Support Vector Machine(SVM)algorithms to classify the mangrove tree species in the study area,and further the results of the classification model parameters on the overall accuracy were analyzed and evaluated. The results showed that the overall accuracy of RF classification was 92.70% and the Kappa coefficient was 0.91. Compared with the traditional SVM classification method,RF improved the producer accuracy and user accuracy of five types of tree species,which could effectively classify mangrove tree species and provide technical support for germplasm resource planning and ecological environmental protection.更多还原