摘要
为探究Sentinel-2遥感影像林分类型分类的优选特征组合,实现对阔叶林、马尾松林、杉木林和竹林的分类及其效果评价,选取福建省长汀县为研究区,利用Sentinel-2影像提取10个原始波段(O),计算9个光谱指数(S)、7个红边光谱指数(R)和8个纹理特征(Te),以及基于数字高程数据计算2个地形特征指数(To),共计36个特征;利用随机森林算法分析不同特征在林分类型分类中的重要性,并利用袋外样本(Out of Band,OOB)数据与平均不纯度减少方法优选特征组合(Optimum Individuality Combination,OIC);对6种不同试验方案(O、O+To、O+To+S、O+To+S+R、O+To+S+R+Te和OIC)进行林分类型分类,并利用混淆矩阵评价分类结果。结果表明,参与林分类型分类的36个特征的重要性为2.11%~5.43%,其中,海拔因子的重要性最高,红边波段、红边光谱指数、纹理特征中均值与相关性也具有较高的重要性;单独使用原始波段对林分类型进行分类,分类精度不高,总体精度为73.26%,Kappa系数为0.64;以原始波段为基础引入其他特征,除原始波段外,其他特征均可以提高分类精度;优选特征组合(OIC)为重要性前27个特征,包含海拔、8个原始波段、7个红边光谱指数和3个纹理特征,分类精度最高,总体精度为83.13%,Kappa系数为0.77,比其余5种试验方案的总体分类精度提高了0.82%~9.87%。以Sentinel-2影像为数据源,随机森林算法优选的特征组合综合多类型特征中对林分类型分类有重要贡献的特征,从而提高了分类精度。研究结果可为GEE平台Sentinel-2影像在森林资源调查中林分类型信息的提取提供参考。
In order to explore the optimal feature combination of Sentinel-2 remote sensing image stand type classification,the classification and effect evaluation of broad-leaved forest,masson pine forest,fir forest and bamboo forest were realized.Selecting Changting County,Fujian Province as the study area,10 original bands(O)were extracted from Sentinel-2 images,and 9 spectral indices(S),7 red-edge spectral indices(R),8 texture features(Te)were calculated,and 2 terrain feature indices(To)based on digital elevation data were calculated,for a total of 36 features.Using random forest algorithm to analyze the importance of different features in stand type classification,using out of band(OOB)data and average impurity reduction method to optimum individuality combination(OIC).6 different experimental protocols(O,O+To,O+To+S,O+To+S+R,O+To+S+R+Te,and OIC)were classified into stand types and the results were evaluated by confusion matrix.The results showed that the importance of the 36 features involved in the classification of stand types was 2.11%-5.43%,the altitude factor was the most important,and the mean and correlation of the red edge band,red edge spectral index,and texture features were also of high importance.Using the original band alone to classify the stand types,the classification accuracy was not high,the overall accuracy was 73.26%,and the Kappa coefficient was 0.64.Based on the original band,other features were introduced.Except the original band,other features can improve the classification accuracy.The optimum individuality combination(OIC)was the top 27 features of importance,including altitude,8 original bands,7 red-edge spectral indices,and 3 texture features,the classification accuracy was the highest,the overall accuracy was 83.13%,and the Kappa coefficient was 0.77,which was 0.82%-9.87%higher than the overall classification accuracy of the other five experimental schemes.Using Sentinel-2 images as the data source,the feature combination optimized by the random forest algorithm integrated the features that had an important contribution to the classification of stand types among the multi-type features,thereby improving the classification accuracy.The research results can provide reference for GEE platform Sentinel-2 image extraction of stand type information in forest resource survey.
作者
闫国东
左雪漫
陈瑾
胡喜生
周成军
巫志龙
YAN Guodong;ZUO Xueman;CHEN Jin;HU Xisheng;ZHOU Chengjun;WU Zhilong(College of Transportation and Civil Engineering,Fujian Agriculture and Forestry University,Fuzhou 350002,China;National Forestry and Grassland Administration Engineering Research Center of Chinese Fir,Fuzhou 350002,China)
出处
《森林工程》
北大核心
2023年第3期12-20,共9页
Forest Engineering
基金
国家自然科学基金项目(31971639)
福建省自然科学基金项目(2019J01406)
福建省工程索道工程技术研究中心开放课题基金项目(ptjh16006)。
