湖北农业科学 ›› 2021, Vol. 60 ›› Issue (13): 60-62.doi: 10.14088/j.cnki.issn0439-8114.2021.13.011

• 资源·环境 • 上一篇    下一篇

偃师市土地利用遥感分类研究

王国重1, 李中原2, 张继宇3, 程焕玲4, 李佳红5   

  1. 1.黄河水文水资源科学研究院,郑州 450004;
    2.河南省水文水资源局,郑州 450003;
    3.河南黄河河务局,郑州 450003;
    4.河南省水土保持监测总站,郑州 450008;
    5.河南省焦作水文水资源勘测局,河南 焦作 454002
  • 收稿日期:2020-08-26 出版日期:2021-07-10 发布日期:2021-07-26
  • 作者简介:王国重(1972-),男,河南南阳人,高级工程师,博士,主要从事水文水资源、水土保持研究,(电话)15890151558(电子信箱) zhonggw@tom.com。
  • 基金资助:
    河南省科技攻关计划项目(GG201719)

Study on remote sensing classification of land utilization in Yanshi city

WANG Guo-zhong1, LI Zhong-yuan2, ZHANG Ji-yu3, CHENG Huan-ling4, LI Jia-hong5   

  1. 1. Yellow River Scientific Research Institute of Hydrology and Water Resources,Zhengzhou 450004,China;
    2. Hydrology and Water Resources Bureau of Henan Province,Zhengzhou 450003,China;
    3. Henan Yellow River Bureau,Zhengzhou 450003,China;
    4. Soil and Water Conservation Supervision Central Station of Henan Province,Zhengzhou 450008,China;
    5. Hydrology and Water Resources Survey Bureau of Jiaozuo,Henan Province,Jiaozuo 454002,Henan,China
  • Received:2020-08-26 Online:2021-07-10 Published:2021-07-26

摘要: 为了掌握河南省偃师市真实、准确的土地利用数据,根据其Landsat8影像,采用支持向量基(SVM)和最大似然法(ML)对其2016年用地类型进行分类。结果表明,2种方法都具有较高的分类精度,但SVM的分类精度略低,这可能与所选参数、核函数及训练样本的影响有关,需做进一步研究。

关键词: 支持向量基(SVM), 最大似然法, 遥感影像, 分类, 偃师市

Abstract: To grasp true and accurate land data in Yanshi city, Henan province, the classing methods were adopted by support vector (SVM) and maximum likelihood (ML) to classify its land use types in 2016 with landsat8 images. The results showed that both methods had high classification accuracy, but the SVM classification accuracy was slightly lower, which could be related to the selected parameters, the influence of kernel function and the training sample.

Key words: support vector machine(SVM), maximum likelihood method, remote sensing image, classification, Yanshi city

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