摘要
为研究沙地信息提取的方法,采用基于CART决策树的面向对象方法,提取中卫市沙坡头区的沙地信息。首先对研究区进行多尺度分割和光谱差异分割得到对象层,然后选择合适的提取特征和训练样本点,最后输入选择的提取特征和样本点生成CART规则树,并对地物进行分类,提取出沙地信息。结果表明:采用面向对象的CART决策树方法提取沙地信息具有较高自动化程度和精确度,依此构建的CART决策树总体分类精度可达到77%,是最近邻分类结果的1.12倍,支持向量机分类结果的1.57倍,此外,NDBI(归一化裸露指数)、GSI(粒度指数)和SWIR 2(第七波段)均值可以成功的将沙地、戈壁和裸岩石砾地三个易混地物区分开来,是沙地提取过程中三个重要的特征指数。
This paper used the object-oriented method and CART decision tree method to extract the sand information with high degree of automation and comprehensive extraction features.The main research process is as follows:(1) Select the study area and preprocess the image of the study area.(2) Use multi-scale segmentation and spectral difference segmentation to obtain the object layer.(3) Select rich extraction features and training sample objects.(4) Training features and sample objects to get the CART rule tree.(5) Apply all objects to the rule tree to get the classification result.(6) Compare the Nearest neighbor and Support vector machine classification results.Finally,Compared with the current research on extracting sand information by CART decision tree.The overall classification accuracy reached 77%,which is 1.12 times of the Nearest neighbor classification result,1.57 times of the support vector machine classification result.In addition,normalized diffevence bare index(NDBI), granularity size index(GSI) and the seventh band(SWIR 2) can successfully distinguish three easily mixed objects of sand, Gobi and bare rock,which are three important characteristic indexes in the process of sand extraction.The experiment has proven this method is a feasible sand extraction method for actual desertification monitoring.
作者
张睎伟
王磊
汪西原
ZHANG Xi-wei;WANG Lei;WANG Xi-yuan(School of Physics and Electronic-Electrical Engineering,Ningxia University,Yinchuan 750021,Ningxia,China;Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Yinchuan 750021,Ningxia,China;Ningxia Hui Autonomous Region Desert Information Intellisense Key Laboratory,Ningxia University,Yinchuan 750021,Ningxia,China)
出处
《干旱区地理》
CSCD
北大核心
2019年第5期1133-1140,共8页
Arid Land Geography
基金
国家自然基金(41561087)
关键词
面向对象
多尺度
光谱差异
CART决策树
沙地提取
object-oriented
multi-scale
spectral differences
CART decision tree
sand extraction
