摘要
以西藏自治区林芝县的Landsat-8影像、地形图为信息源,结合样地调查数据及森林资源二类调查数据,研究基于纹理信息的CART决策树的面向对象分类对研究区内的森林地物类别进行提取,分类的总体精度和Kappa系数分别为82.53%和0.768,相较于不利用纹理信息的决策树分类和基于最大似然分类法的研究区地物类别的提取总体精度均高近10%,Kappa系数分别高0.12和0.111。结果表明:基于纹理信息的CART决策树面向对象分类方法对研究区Landsat-8影像进行植被类型提取,分类结果较好,能够满足研究要求。
The research used Landsat-8imagery of LinZhi,Tibet Autonomous Region and the topographic map as data source,took the ground sample data and the data of forest management inventory as ancillary data,the study mainly extracted the vegetation cover types,including coniferous forest,broad-leaved forest,shrub forest,river and residential areas,from the Landsat-8imagery of LinZhi,Tibet Autonomous Region by using CART decision tree with texture information of object-oriented classification method.The overall accuracy and Kappa coefficient of the decision tree with texture information classification result was82.53% and 0.768,respectively,compared with decision tree without texture information and the maximum likelihood classification results the overall accuracy have increased by almost 10%,and Kappa coefficient have increased 0.12 and 0.111,respectively.The study results showed,the classification results were better and can satisfied the accuracy requirements by using CART decision tree with texture information of object-oriented classification method with the Landsat-8imagery.
出处
《遥感技术与应用》
CSCD
北大核心
2017年第2期386-394,共9页
Remote Sensing Technology and Application
基金
国家科技基础性工作专项"中国森林植被调查"(2013FY111600)