基于不同遥感数据源的农作物精细化分类研究
Crop Refinement Classification Based on Different Remote Sensing Data Sources
梁明月 1杨倩 1何卫军 1张生1
作者信息
- 1. 广西壮族自治区遥感中心,广西南宁 530023;广西遥感地质工程技术研究中心,广西南宁 530023
- 折叠
摘要
遥感技术已成为农业信息提取的重要手段.为探究不同遥感数据源下的农作物精细化识别与分类,选取广西壮族自治区贺州市八步区东融供港蔬菜产业示范区为研究区,基于Planet、GF6 WFV、Landsat 8 OLI影像数据,利用支持向量机分类算法,对研究区豆杯、学斗、青仔、尖叶菜心、芥蓝等不同农作物进行识别与提取,通过类别可分离性、总体分类精度、Kappa系数、光谱变化、成图效果等几个方面对提取效果进行评价,结果表明GF6 WFV影像是研究区农作物识别与提取的最佳数据源.
Abstract
Remote sensing technology has become an important means of extracting agricultural information.In order to explore the identifica-tion and classification of crops of different remote sensing data sources,we selected Babu District Dongrong vegetable industry demonstration zone supplied to HongKong as the research area.Base on Planet,GF6 WFV,Landsat 8 OLI remote sensing images,we used the support vec-tor machine method to identify and extract different crops of the tip leaves of bean,Xuedou,Qingzai,vegetable heart of pointed leaf,cabbage mustard.We also evaluated the extraction effect through class separability,overall classification,Kappa coefficient,spectral variation and mapping effect.Results showed that GF6 WFV images were the best resource for crop recognition and extraction in the study area.
关键词
Landsat/8/OLI/GF6/WFV/Planet/农作物分类/支持向量机Key words
Landsat 8 OLI/GF6 WFV/Planet/Crop classification/SVM引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据