基于特征优选随机森林算法的农耕区土地利用分类

S25; 为了提高农耕区土地利用分类精度,该文采用较高空间分辨率和丰富光谱信息的Sentinel-2数据生成光谱特征、无红边波段的植被指数、红边指数和纹理特征4种基本特征变量,并对以上特征变量优选后进行特征重要性排序,进而构建7种特征组合方案,基于随机森林算法和支持向量机对农耕区土地利用信息进行提取并对比验证分类精度.研究结果表明:通过特征优选的随机森林算法进行土地利用信息提取效果最佳,总体精度达到88.24％,Kappa系数为0.84,精度优于相同特征变量下的支持向量机分类方法.该方法能够有效提高农耕区土地利用分类精度,可为土地资源监测、管理提供技术支持和理论参考....

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Published in农业工程学报 Vol. 36; no. 4; pp. 244 - 250
Main Authors 王李娟, 孔钰如, 杨小冬, 徐艺, 梁亮, 王树果
Format Journal Article
LanguageChinese
Published 江苏师范大学地理测绘与城乡规划学院,徐州,221116%国家农业信息化工程技术研究中心,北京,100097 15.02.2020
Subjects
特征优选
红边指数
农耕区
土地利用分类
随机森林算法
Sentinel-2
Online AccessGet full text
ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2020.04.029

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Abstract S25; 为了提高农耕区土地利用分类精度,该文采用较高空间分辨率和丰富光谱信息的Sentinel-2数据生成光谱特征、无红边波段的植被指数、红边指数和纹理特征4种基本特征变量,并对以上特征变量优选后进行特征重要性排序,进而构建7种特征组合方案,基于随机森林算法和支持向量机对农耕区土地利用信息进行提取并对比验证分类精度.研究结果表明:通过特征优选的随机森林算法进行土地利用信息提取效果最佳,总体精度达到88.24％,Kappa系数为0.84,精度优于相同特征变量下的支持向量机分类方法.该方法能够有效提高农耕区土地利用分类精度,可为土地资源监测、管理提供技术支持和理论参考.
AbstractList S25; 为了提高农耕区土地利用分类精度,该文采用较高空间分辨率和丰富光谱信息的Sentinel-2数据生成光谱特征、无红边波段的植被指数、红边指数和纹理特征4种基本特征变量,并对以上特征变量优选后进行特征重要性排序,进而构建7种特征组合方案,基于随机森林算法和支持向量机对农耕区土地利用信息进行提取并对比验证分类精度.研究结果表明:通过特征优选的随机森林算法进行土地利用信息提取效果最佳,总体精度达到88.24％,Kappa系数为0.84,精度优于相同特征变量下的支持向量机分类方法.该方法能够有效提高农耕区土地利用分类精度,可为土地资源监测、管理提供技术支持和理论参考.
Author 王树果
梁亮
王李娟
杨小冬
徐艺
孔钰如
AuthorAffiliation 江苏师范大学地理测绘与城乡规划学院,徐州,221116%国家农业信息化工程技术研究中心,北京,100097
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Author_FL Yang Xiaodong
Wang Shuguo
Liang Liang
Wang Lijuan
Kong Yuru
Xu Yi
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Keywords 特征优选
红边指数
农耕区
土地利用分类
随机森林算法
Sentinel-2
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Snippet S25; 为了提高农耕区土地利用分类精度,该文采用较高空间分辨率和丰富光谱信息的Sentinel-2数据生成光谱特征、无红边波段的植被指数、红边指数和纹理特征4种基本特征变量,并...
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Title 基于特征优选随机森林算法的农耕区土地利用分类
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