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林业科学 ›› 2011, Vol. 47 ›› Issue (9): 69-74.doi: 10.11707/j.1001-7488.20110912

• 论文 • 上一篇    下一篇

基于邻近相关图像和决策树分类的森林景观变化检测

李世明1, 王志慧1,2, 李增元1, 陈尔学1, 刘清旺1   

  1. 1. 中国林业科学研究院资源信息研究所 北京100091;2. 中国矿业大学 北京100083
  • 收稿日期:2010-11-29 修回日期:2011-04-10 出版日期:2011-09-25 发布日期:2011-09-25
  • 通讯作者: 李增元

A Forest Change Detection Model Based on Neighborhood Correlation Images and Decision Tree Classification

Li Shiming1, Wang Zhihui1,2, Li Zengyuan1, Chen Erxue1, Liu Qingwang1   

  1. 1. Institute of Forest Resources Information Techniques, CAF Beijing 100091;2. China University of Mining & Technology Beijing 100083
  • Received:2010-11-29 Revised:2011-04-10 Online:2011-09-25 Published:2011-09-25

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Landsat5 TM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0.808 5。

关键词: 变化检测, 邻近相关图像, 决策树

Abstract:

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.

Key words: change detection, neighborhood correlation image, decision tree

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