摘要
掌握森林内树木种类及其分布情况对研究森林生态系统具有重要意义.为推广国产高分数据在森林树种分类方面的应用,同时探究不同时相、分类特征及分类器的组合对树种分类结果的影响,本研究利用3景高分二号影像构建了3种单时相和4种多时相,通过多尺度分割、C5.0特征优选及支持向量机(SVM)和随机森林(RF)两种分类器分别实现了不同时相及特征维度下面向对象的8个树种的分类,最终取得了总体精度在63.5~83.5%、Kappa系数在0.57~0.81的良好结果.结果表明:时相的选择会对分类结果产生较大的影响,其中,基于多时相的结果往往优于单时相,多时相下不同影像组合间以及单时相间亦存在明显的精度差异;特征优选会对分类精度的提升起到积极作用,应予以足够重视;SVM在不同时相及特征维度下的表现均较为稳定,在单时相及分类特征难以直接区分树种的情况下应优先使用SVM,但使用SVM时应注意其易发生过拟合;RF不易发生明显的过拟合,但其对分类特征的质量依赖较大,并倾向于在良好的影像组合下取得较为优异的结果.
It’s important to master tree species composition and distribution in forests for the study of forest ecosystems.To promote the application of domestic Gaofen data in the classification of tree species and to explore the effects of different combining images,classification features and classifier on tree species classification results,three kinds of single temporal data and four kinds of multi-temporal data were constructed.Based on three GF-2 images,according to the multi-scale segmentation,C5.0 feature optimization as well as two classifiers including support vector machine(SVM)and random forest(RF),we finished the object-based classification of eight tree species of different temporal and feature dimensions respectively,and finally achieved good results with overall accuracy between 63.5%and 83.5%and the Kappa coefficient between 0.57 and 0.81.The results showed that the choice of temporal stage would affect the classification results.The results based on multi-temporal were generally better than that on single temporal stage.There were obvious precision differences between different image combinations of multi-temporal as well as different single temporal stage.It is notable that feature optimization played a positive role in the improvement of classification accuracy.SVM was relatively stable across different temporal and feature dimensions,which should be given priority when single temporal and classification features are difficult to distinguish tree species directly,while it should be noted that SVM was easy to overfit.RF was not easy to overfit,but it was more dependent on the quality of classification features and would get good results under favorable image combination.
作者
李哲
张沁雨
邱新彩
彭道黎
LI Zhe;ZHANG Qin-yu;QIU Xin-cai;PENG Dao-li(College of Forestry,Beijing Forestry Universityy Beijing 100083,China)
出处
《应用生态学报》
CAS
CSCD
北大核心
2019年第12期4059-4070,共12页
Chinese Journal of Applied Ecology
基金
国家重点研发计划项目(2016YFD0600205)资助~~
关键词
高分二号
树种分类
时相选择
C5.0决策树
支持向量机
随机森林
GF-2
tree species classification
temporal stage selection
C5.0 decision tree
support vector machines
random forest
