摘要
本文提出了一种利用GlobeLand30数据辅助多源数据融合进行城市不透水面自动提取的方法。首先基于波段映射和小波变换的影像融合方法,融合哨兵二号和高分二号影像,获得同时具有较高空间分辨率和光谱分辨率的融合影像,其具有丰富的光谱特征和空间特征,有利于提升复杂城市区域的不透水面和非不透水面区分能力。然后利用GlobeLand30数据的类别信息自动获取初始分类样本,基于融合影像的丰富光谱信息构建多种植被指数、水体指数和建成区指数,对初始分类样本进行优化。最后利用优化后的训练样本,使用光谱、地物指数等特征训练分类器,实现城市不透水面的自动准确提取。本文以济南市2019年的高分二号和哨兵二号影像为试验数据,在时相、分辨率与影像均不同的GlobeLand30全球地表覆盖数据辅助下获得了总体精度优于92%的不透水面提取结果,验证了本文方法的有效性。
In this paper,an automatic extraction method of urban impervious surface area from multi-source image fusion data is proposed using GlobeLand30 data asauxiliary data.Firstly,an image fusion method based on band mapping and wavelet transform is proposed to fuse Sentinel-2 and GF-2 images to obtain fusion images with high spatial resolution and spectral resolution.The fusion image has rich spectral and spatial characteristics,which is conducive to improving the ability of distinguishing impervious and non-impervious surface in complex urban areas.Then,the category information of GlobeLand30 data is used to automatically to obtain the initial samples,a variety of ground index such as vegetation index,water index,and built-up area index are constructed to refine the initial samples.Finally,the optimized training samples are used to train the classifier with spectral and ground index features to achieve automatic and accurate extraction of urban impervious surface.In this paper,images of GF-2 and Sentinel-2 in Jinan city in 2019 are used as experimental data.With the help of GlobeLand30 global land cover data with different phases,resolutions and images,the overall accuracy of impervious surface extraction is better than 92%,which verifies the effectiveness of the proposed method.
作者
霍嘉婷
赵展
朱秀丽
HUO Jiating;ZHAO Zhan;ZHU Xiuli(School of Surveying and Geo-Informatics,Shandong Jianzhu University,Jinan 250101,China;National Center for Basic Geographic Information,Beijing 100830,China;Key Laboratory of Spatiotemporal Information and Intelligent Service,Ministry of Natural Resources,Beijing 100830,China)
出处
《测绘通报》
CSCD
北大核心
2024年第2期19-25,共7页
Bulletin of Surveying and Mapping
基金
国家科技基础资源调查专项(2019FY202502)
山东建筑大学博士科研基金(X19031Z)。
