云南师范大学学报（自然科学版）2017，Vol.37Issue(3)：73-78,6.DOI:10.7699/j.ynnu.ns-2017-043
随机森林在城市不透水面提取中的应用研究
Application of Random Forest Model in the Urban Impervious Surface Extraction
摘要
Abstract
Impervious surface is an important index of urbanization and environmental quality assessment.This article is based on GF1 data using random forest model,extracted 17 Features,including normalized difference water index(NDWI),normalized difference vegetation index(NDVI),soil adjust vegetation index(SAVI),building index (BAI),brightness index,independent component,texture information and image bands.Extract impervious surface of Shenzhen region in the matlab,and with the maximum likelihood classification method and support vector machine were compared.Results show that random forest method can effectively improve classification accuracy,the overall accuracy increased by 7.681％ than traditional parameter classification method(MLC),the Kappa coefficient in creased 0.119 4.关键词
不透水面/随机森林模型/高分一号Key words
Impervious surface/Random forests model/GF1 data分类
信息技术与安全科学引用本文复制引用
赵艺淞,杨昆,王保云,黎晓路..随机森林在城市不透水面提取中的应用研究[J].云南师范大学学报（自然科学版）,2017,37(3):73-78,6.基金项目
国家自然科学基金资助项目(41461038). （41461038）
