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| 基于影像多种特征的CART决策树分类方法及其应用 | |
| 引用本文： | 陈云,戴锦芳,李俊杰.基于影像多种特征的CART决策树分类方法及其应用[J].地理与地理信息科学,2008,24(2):33-36. |
| 作者姓名： | 陈云 戴锦芳 李俊杰 |
| 作者单位： | 1. 中国科学院南京地理与湖泊研究所,江苏南京,210008;中国科学院研究生院,北京,100039 2. 中国科学院南京地理与湖泊研究所,江苏南京,210008 3. 中国资源卫星应用中心,北京,100073 |
| 基金项目： | 江苏省测绘科研项目(JSCHKY200704) |
| 摘 要： | 以扬州市宝应县为研究区,采用主成分分析法对研究区影像进行数据压缩和单波段数据增强,利用灰度共生矩阵分析第一主成分的纹理信息。运用基于CART算法的决策树分类方法,选用影像的光谱特征值、NDVI值以及纹理统计量值为测试变量,并通过计算确定决策树的节点规则,提取影像中主要地物信息。将分类结果与单纯依靠光谱特征的监督分类法结果相比较,表明基于影像多种特征的CART决策树分类方法分类精度较高,尤其较好地提取了围网养殖区和建设用地。 |
| 关 键 词： | 纹理特征 光谱特征 CART 决策树 |
| 文章编号： | 1672-0504(2008)02-0033-04 |
| 修稿时间： | 2007年10月17 |
CART-Based Decision Tree Classifier Using Multi-feature of Image and Its Application |
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| CHEN Yun,DAI Jin-fang,LI Jun-jie.CART-Based Decision Tree Classifier Using Multi-feature of Image and Its Application[J].Geography and Geo-Information Science,2008,24(2):33-36. | |
| Authors: | CHEN Yun DAI Jin-fang LI Jun-jie |
| Abstract: | In this paper,Baoying county is taken as an example to discuss the method of combing texture of the CBERS-02 CCD image with spectrum to improve the accuracy of extracted information of image using CART-based decision tree classifier.Firstly,principle components are extracted from the image,and textures are analyzed using Gray Level Co-occurrence Matrices and statistic index is calculated.By the rules of CART algorithm classification,selecting spectral characteristics,NDVI and textural characteristics as test variables,the node rules of decision tree are determined.The experiment proves that the CART-based decision tree classifier can get higher accuracy compared with the maximum likelihood-based supervised classification method.Especially,the CART-based decision tree classifier also gets better effect in extracting the lake enclosure cultivated areas and building areas. |
| Keywords: | texture characteristics spectral characteristics Classification and Regression Tree(CART) decision tree |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |