文献类型：期刊文献

中文题名：基于邻近相关图像和决策树分类的森林景观变化检测

英文题名：A Forest Change Detection Model Based on Neighborhood Correlation Images and Decision Tree Classification

作者：李世明[1] 王志慧[1,2] 李增元[1] 陈尔学[1] 刘清旺[1]

第一作者：李世明

机构：[1]中国林业科学研究院资源信息研究所;[2]中国矿业大学

年份：2011

卷号：47

期号：9

起止页码：69-74

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：中央级公益性科研院所基本科研业务费专项课题(IFRIT200805);林业公益性行业科研专项(200804001)

语种：中文

中文关键词：变化检测;邻近相关图像;决策树

外文关键词：change detection; neighborhood correlation image; decision tree

分类号：S757

摘要：提出一种基于邻近相关图像和决策树分类的景观变化检测方法,并将其应用于地震干扰引起的森林景观变化检测。以5·12汶川地震中遭受严重破坏的龙溪-虹口国家级自然保护区作为研究区,利用地震前后的Landsat5TM影像创建不同邻近窗口大小的邻近相关图像,结合决策树技术生成变化检测分类图。结果表明:使用邻近相关图像的变化检测精度有所提高,其中以5×5窗口创建的邻近相关图像变化检测效果最佳,总体分类精度和Kappa系数分别达到82.33%和0.8085。
A change detection model based on neighborhood correlation images（NCIs）and decision tree classification using remote sensing data was proposed,and then applied to detect forest landscape change information induced by forest disturbance.Longxi-Hongkou nature reserve which was seriously damaged in 5.12 Wenchuan Earthquake was selected as study area to verify the model,and various neighborhood configuration of correlation images were explored using bi-temporal Landsat5 TM images.Change detection maps were generated by using a machine learning decision tree（C5.0）.The results shows that the accuracy of change detection results using NCIs is higher than that of result without NCI.Result with 5×5 window size is of highest accuracy among the different NCIs,and general accuracy and Kappa coefficient is 82.33% and 0.808 5 respectively.