文献类型：期刊文献

中文题名：机载AISA Eagle Ⅱ高光谱数据在温带天然林树种分类中的应用

英文题名：Tree Species Classification Using Airborne Hyperspectral Data in A Temperate Natural Forest

作者：李军玲[1] 庞勇[1] 李增元[1] 荚文[1]

第一作者：李军玲

机构：[1]中国林业科学研究院资源信息研究所

年份：2019

卷号：47

期号：5

起止页码：72-76

中文期刊名：东北林业大学学报

外文期刊名：Journal of Northeast Forestry University

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家重点研发计划(2017YFD0600404);中央级公益性科研院所基本科研业务费专项(CAFYBB2016ZD004);国家重点基础研究发展计划(973计划)(2013CB733404)

语种：中文

中文关键词：AISA;Eagle;Ⅱ;机载高光谱影像;大气校正;天然林

外文关键词：AISA Eagle Ⅱ;Airborne hyperspectral images;Atmospheric correction;Natural forest

分类号：S771.8

摘要：以内蒙古根河地区的温带天然林试验区为研究对象,采用支持向量机(SVM)方法,对经过大气校正后的机载AISA Eagle Ⅱ高光谱地表反射率影像分航带进行树种分类,将地面光谱测量与高光谱同平台的高分辨率航空相片结合,进行训练样本的选择,使用地面样地数据对分类结果进行验证。结果表明:利用AISA Eagle Ⅱ高光谱影像对温带天然林区分类的总体精度和kappa系数分别达到了96.71%和0.95;灌木分类精度最高,其制图精度和用户精度分别达到了98.07%和98.31%;落叶松和白桦的用户精度分别为98%和94%。
With the temperate forest area of Genhe in Inner Mongolia as an example, Support Vector Machine(SVM) was used to classify the reflectance images. Tree species training samples were selected based on high resolution aerial photographs. The filed data were used to verify the classification results. The overall accuracy and Kappa coefficient reached 96.71% and 0.95, respectively, and for a single tree species it had good classification result. The shrub had highest classification accuracy, with the mapping accuracy and user accuracy of 98.07% and 98.31%, respectively. The user accuracies of Larix gmelinii and Betula platyphylla were 98% and 94%, respectively.