摘要
随着遥感数据获取能力的不断增强,自动化程度已经成为大尺度遥感土地覆被分类面临的关键问题。然而,现有训练样本的人工选取方法成为制约土地覆被分类自动化的瓶颈。本文以河南、贵州两省为研究区,提出一种基于多源数据的土地覆被样本自动提取方法,以构建适用于大尺度的土地覆被自动分类。首先,以2010年1∶10万土地利用数据CHINALC和30 m分辨率全球土地覆被数据Globle Land30为样本数据源;然后,利用空间一致性分析及异质性分析确定样本初选区域;最后,通过样本提纯去除无效样本。结果表明:(1)应用多源数据的土地覆被样本自动提取方法获得的分类产品总体分类精度高于人工样本提取方法制作的全球土地覆被产品MCD12Q1。(2)与单源样本自动提取方法相比,应用多源数据的土地覆被样本自动提取方法,可获得更好的分类稳定性。综上,多源数据的土地覆被样本自动提取方法可在保证精度的同时,提升土地覆被分类的自动化程度。
The capability of remotely sensed data acquisition is constantly improved. Thus, enhancing the automation degree for land cover classification at a large scale by remote sensing has become a key problem. However, present manual methods of selecting samples are be- coming the bottleneck of automatic land cover classification. Many global and national land cover datasets based on remote sensing have been produced in the past two decades for different international or national initiatives. However, the rich knowledge implied in these products has not been fully exploited. The overall objective of this study is to set up an automatic land cover classification approach at a large scale by remote sensing through an automatic method of collecting land cover samples based on multisource datasets. The practical goals are to improve automation degree of land cover classification and enhance the accuracy of !and cover classification. Henan and Guizhou provinces were selected as the study areas based on their types of land covers. First, the national land use database of China at a scale of 1 ： 100000 （CHINALC） and global land cover data （GlobleLand30） at a resolution of 30 m were selected as the data sources for the sample collection. Second, the initial sample areas were collected based on the spatial consistency analysis and heterogeneity analysis. Third, invalid samples were removed from the initial samples through the technology of sample purification. Finally, the Jeffries-Matusita distance was used to measure the classification feature separability of the samples between the different land cover types to prove the feasibility of the proposed method. The accuracy of the land cover product by the proposed method of sample collection was as- sessed and compared with the globe land cover product MCD12Q1. Experimental results show that the following： （a） The overall accuracy of the classification product through the proposed automatic method of sample collection based on multisource datasets was higher t
作者
黄亚博
廖顺宝
HUANG Yabo LIAO Shunbao(College of Environment and Planning, Henan University, Kaifeng 475004, China Institute of Disaster Prevention, Beijing 101601, China College of Computer and Information Engineering, Henan University, Kaifeng 475004, China Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)
出处
《遥感学报》
EI
CSCD
北大核心
2017年第5期757-766,共10页
NATIONAL REMOTE SENSING BULLETIN
基金
国家重点研发计划项目(编号:2017YFD0300400)
河南省高等学校重点科研项目(编号:16A520081)
中国科学院战略性先导科技专项(编号:XDA05050000)~~
