江西科学2024，Vol.42Issue(2)：289-296,8.DOI:10.13990/j.issn1001-3679.2024.02.012
融合多源数据的城市功能区识别与分析
Identification and Analysis of Urban Functional Areas by Fusion of Multi-source Data
摘要
Abstract
The rapid development of urbanization has changed the spatial structure of the city,and the rational division of urban functional areas is conducive to the monitoring of urbanization and ur-ban planning and management.Remote sensing images can reflect the physical characteristics of ground objects,but cannot obtain their socioeconomic characteristics.This study uses Gaofen-2 re-mote sensing image data,POI data,nighttime light data and building outline data to integrate the feature information of multi-source data and implement the division of urban functional areas based on the Scikit-Learn machine learning method.Firstly,the traffic analysis area was constructed with the road network as the basic research unit,and the research area was divided into 827 plots,and then combined with kernel density analysis,frequency density method and regional analysis,extrac-ted and the feature information of multi-source data is integrated to identify urban functional areas based on three classification models.The research results show that the best recognition results are achieved by comprehensively utilizing the BOVW model constructed from spectrum and texture,so-cioeconomic characteristics constructed from POI and night light data,and landscape characteristics of building outline data,combined with the random forest model method,its accuracy is as high as 76.65%.The feasibility and effectiveness of this method are verified.关键词
高分二号遥感影像/POI/随机森林/城市功能区Key words
Gaofen-2 remote sensing image/POI/random forest/urban functional area分类
天文与地球科学引用本文复制引用
齐广玉,程玮瑜,程朋根..融合多源数据的城市功能区识别与分析[J].江西科学,2024,42(2):289-296,8.基金项目
国家自然科学基金项目(41861052) （41861052）
江西省自然科学基金面上项目(20202BABL202045). （20202BABL202045）
