基于多特征决策树的建设用地信息提取  

Extraction of information on construction land based on multi-feature decision tree classification

作　　者：饶萍[1,2] 王建力[1] 王勇[1] 

机构地区：[1]西南大学地理科学学院,重庆400715 [2]毕节学院生态工程学院,毕节551700

出　　处：《农业工程学报》2014年第12期233-240,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：中央高校基本科研业务费专项资金项目(XDJK2011C089);国家自然科学基金项目(40971122)

摘　　要：城乡交接带的土地利用/覆盖类型兼具城镇和农村的典型特征。为了解决土地覆盖类型复杂、存在"同谱异物"现象的西部山区环境中建设用地信息难以精确提取的问题,该文提出一种包含多个特征节点的决策树分类法,该方法以Landsat-8影像为主要数据源,以决策树分类法为框架,结合地物光谱特征及空间特征,建立以4种归一化指数(归一化三波段指数normalized difference three bands index,NDTBI;归一化建筑指数normalized difference building index,NDBI;改进的归一化水体指数modified normalized difference water index,MNDWI;归一化植被指数normalized difference vegetation index,NDVI)、支持向量机(support vector machine,SVM)分类结果和河流缓冲区作为特征节点的决策树分类器,对贵州省毕节市城乡交接带建设用地专题信息进行提取。NDTBI是该文新构建的指数,取名为归一化三波段指数,目的是为了弥补归一化建筑指数NDBI的不足;支持向量机分类结果的使用在多指数法的基础上提高了地物的可分离性;以构建河流缓冲区的方式加入的地物空间信息,进一步提高了信息提取的精确性。由于决策树特征节点的构建过程是利用先验知识来优化特征值和提高精度的过程,克服了利用单一指数法、多指数法及单独使用模式识别法中出现的问题,精度评价结果显示总体精度达到了97.52%。为了验证方法的推广性,采用毕节市七星关区中心城区遥感影像数据该方法进行验证,精度评价结果显示总体精度达到98.03%。Spatial distribution status of construction land is closely related to the regional economic and social development. Therefore, timely monitoring and delivery of data on the dynamics of construction land are far-reaching for policy and decision making processes. Classifying land-use/land-cover and analyzing changes are among the most common applications of remote sensing. One of the most basic and difficult classification tasks is to distinguish the construction land from other land surfaces. Landsat imagery is one of the most widely used sources of data in remote sensing of construction land. Several techniques of construction land extraction using Landsat data are described in some literatures, but their applications are constrained by low accuracy in various situations, and usually using the technique of single index or multi-index. The purpose of this study was to devise a method to improve the accuracy of construction land extraction in the presence of various kinds of environmental noise. Thus we introduce a multi-features decision tree （DT） classification model for improving classification accuracy in the areas that including bare land, shadow and some streams, in which the other classification methods often fail to classify correctly. The model integrates four spectral indexes, the pattern recognition technique and spatial algorithms. The four spectral indexes are the normalized difference three bands index （NDTBI）, the normalized difference building index （NDBI）, the modified normalized difference water index （MNDWI） and the normalized difference vegetation index （NDVI） respectively. The pattern recognition technique is referred to support vector machine （SVM）. And the spatial algorithm is to create buffer zone. The test site was deliberately selected so that it consists of complex surface features, such as bare land, hill shade, and some small streams that are liable to be mixed up with construction land on the Landsat imagery. For that reason, Landsat-8 OLI images （path/row 12

关 键 词：遥感 土地利用 决策树 土地覆盖 建设用地 Landsat-8 

分 类 号：S127[农业科学—农业基础科学]