摘要
本研究提出一种基于机器学习算法的区域尺度上改变空间分辨率来提升高光谱树种分类精度的方法,为陆生调查的树种分类研究提供一个新的思路。通过利用无人机获取成都市植物园全域的高光谱影像,采集了园内140种树种共1249个样本。通过构建的32种植被指数及176个原始波段进行变量筛选,运用随机森林和支持向量机两种算法建立分类模型。结合研究区典型树种的林分类型和冠层大小,在9种不同空间分辨率下,分别选取了10、15、20种树种,探索树种分类精度。结果显示,当空间分辨率从0.12 m逐步降低至4 m时,10、15、20种树种的模型分类精度均在3 m分辨率时达到最高,且支持向量机分类结果整体精度较高。表明基于支持向量机算法、开展特征变量提取与选择、确定最佳观测尺度的方法可以较好地捕获不同树种的冠层信息,提升树种分类精度。
This study proposes a method based on machine learning algorithms to improve the accuracy of hyperspectral tree species classification by changing spatial resolution at the regional scale,providing a new approach for tree species classification research in terrestrial surveys.This study used drones to obtain hyperspectral images of the entire Chengdu Botanical Garden,and collected 1249 samples of 140 tree species in the garden.By constructing 32 vegetation indices and 176 original bands for variable screening,a classification model was established using two algorithms:random forest and support vector machine.Based on the forest stand types and canopy sizes of typical tree species in the study area,10,15,and 20 tree species were selected at 9 different spatial resolutions to explore the accuracy of tree species classification.The results showed that when the spatial resolution gradually decreased from 0.12 m to 4 m,the classification accuracy of the models for 10,15,and 20 tree species reached the highest level at a resolution of 3 m,and the overall accuracy of the support vector machine classification results was relatively high.This indicates that methods based on support vector machine algorithm,feature variable extraction and selection,and determining the optimal observation scale can effectively capture canopy information of different tree species and improve tree classification accuracy.
作者
周湘山
杨武年
罗可
朴虹奕
周涛
周杰
唐晓鹿
ZHOU Xiangshan;YANG Wunian;LUO Ke;PIAO Hongyi;ZHOU Tao;ZHOU Jie;TANG Xiaolu(POWERCHINA Chengdu Engineering Corporation Limited,Chengdu 611100,China;College of Geography and Planning,Chengdu University of Technology,Chengdu 610059,China;College of Ecology and Environment,Chengdu University of Technology,Chengdu 610059,China)
出处
《遥感技术与应用》
CSCD
北大核心
2024年第4期880-896,共17页
Remote Sensing Technology and Application
基金
国家自然科学基金项目(41671432)
四川省自然资源厅重点项目(KJ-2020-5)
中国电建集团成都院科技项目(P39818)资助。
关键词
无人机高光谱
机器学习算法
树种分类
空间分辨率
UAV hyperspectral
Machine learning algorithm
Classification of tree species
Spatial resolution
