现代电子技术2024，Vol.47Issue(6)：137-141,5.DOI:10.16652/j.issn.1004-373x.2024.06.022
典型遥感影像分类方法适用性分析
Scalability analysis of typical remote sensing image classification method
摘要
Abstract
The classification is an indispensable step to extract information from remote sensing images.Selecting appropriate classifier is very important to improve the classification accuracy.How to choose suitable classification algorithm for specific research is an urgent problem to be solved.An area in the central of Beijing is selected as the research area,the GF-1 data and Landsat 8 data are applied,and the most commonly used and relatively high classification accuracy supervised classification methods such as minimum distance method,maximum likelihood method,and support vector machine method are applied,resperctively.The research is divided into five categories:forest land,grassland,water body,bare soil and buildings.The comparative analysis for the classification results in terms of spatial distribution,area,and accuracy are conducted.The results show that the selection of the classification algorithm mainly depends on the characteristics of the ground features in the study area,among which the minimum distance classification has a higher accuracy when applied to the area with large vegetation cover,the maximum likelihood classification is suitable for the area with many buildings,and the support vector machine has high universality to all kinds of objects.关键词
遥感影像/分类技术/最小距离分类/最大似然分类/支持向量机/GF-1/Landsat 8Key words
remote sensing images/classification techniques/minimum distance classification/maximum likelihood classification/support vector machine/GF-1/Landsat 8分类
信息技术与安全科学引用本文复制引用
武英洁,冯勇,徐晓琳,刘思宇,朱辉..典型遥感影像分类方法适用性分析[J].现代电子技术,2024,47(6):137-141,5.基金项目
山东省气象局科研项目(2021SDQXZ06 （）
2022SDQN04) （）
