灾害学2017，Vol.32Issue(1)：36-42,7.DOI:10.3969/j.issn.1000-811X.2017.01.008
基于变化检测－CART决策树模式自动识别沙漠化信息
Automatic Recognition of Desertification Information Based on the Pattern of Change Detection-CART Decision Tree
摘要
Abstract
The desertification of remote sensing monitoring has some problems,such as visual interpretation limitation,constraint of data source and low utilization rate of remote sensing information.Based on this,taking Minqin basin as the test area,firstly,this paper detects the change pixel of two Landsat images in 1994 and 2014, by employing the methods of image difference,maximum value synthesis and two-dimensional maximum between-class variance.Secondly,the decision tree is constructed by the classification and regression tree (CART)algo-rithm then automatically extracts the sandy land information of 2014.Finally,spatial overlay analyze the results of change detection with sandy land information,and realize the pattern of automatic recognition on desertification in-formation.The research shows that the accuracy of change detection-CART decision tree pattern is 89.43% to 93%,and the confidence interval is between 85.90%and 98%at 95%confidence level,clearly that the reliabili-ty of its accuracy is relatively high.This pattern not only can make full use of the abundant remote sensing informa-tion but also can exclude the interference of redundant information.Obviously,the change detection-CART decision tree pattern is one of the effective methods to identify the desertification information,and it will have important ap-plication value to the desertification control project.关键词
沙漠化/分类与回归树(CART)/决策树/变化检测/自动识别Key words
desertification/classification and regression tree/regression tree/change detection/automatic recognition分类
资源环境引用本文复制引用
黄晓君,颉耀文,卫娇娇,付苗,吕利利,张玲玲..基于变化检测－CART决策树模式自动识别沙漠化信息[J].灾害学,2017,32(1):36-42,7.基金项目
国家自然科学基金项目（41471163）；兰州大学中央高校基本科研业务费专项资金 （）
