融合GF-6 WFV影像主成分分析特征的县域冬小麦种植面积提取  

Extraction of winter wheat planting area in county regions based on principal component analysis features fused with GF-6 WFV image

作　　者：张萌 徐建鹏[1] 周鹿扬[1] 王杰 王状 岳伟[1] ZHANG Meng;XU Jian-peng;ZHOU Lu-yang;WANG Jie;WANG Zhuang;YUE Wei(Anhui Rural Comprehensive Economic Information Center,Hefei 230031,China;Anhui Institute of Meteorological Sciences,Hefei 230031,China)

机构地区：[1]安徽省农村综合经济信息中心,合肥230031 [2]安徽省气象科学研究所,合肥230031

出　　处：《湖北农业科学》2024年第8期201-208,共8页Hubei Agricultural Sciences

基　　金：安徽省自然科学基金“江淮气象”联合基金项目(2208085UQ04);安徽省科技重大专项(2023n06020017)。

摘　　要：为准确、快速获得县域冬小麦的种植信息,针对多时相方法存在的成本高、效率低、过程复杂等问题,以安徽省固镇县为研究区,提出基于单时相GF-6 WFV影像主成分分析特征与原始光谱波段归一化融合、并使用K-最近邻算法进行土地覆盖物分类的有效面积提取方法。结果表明,所提出方法优于RAW和PDR这2种基准方法,且降维维度参数为3时效果最好,总体精度和Kappa系数分别为89.71%和0.87,实际冬小麦提取面积精度达98.49%,相对误差仅为1.51%。In order to obtain the planting information of winter wheat at county level accurately and quickly,Guzhen County of Anhui Province was selected as the research area,aiming at the problems of high cost,low efficiency and complex process of multi-temporal methods.An effective area extraction method based on single temporal GF-6 WFV image principal component analysis and original spectral band normalization fusion was proposed,and K-nearest neighbor algorithm was used for land cover classification.The results showed that the proposed method was superior to the other two benchmark methods of RAW and PDR,and the best effect was achieved when the dimensionality reduction parameter was 3.The overall accuracy and Kappa coefficient were 89.71%and 0.87,respectively.The actual accuracy of the winter wheat extraction area was 98.49%,with a relative error of only 1.51%.

关 键 词：遥感 冬小麦 种植面积提取 主成分分析特征 GF-6 WFV影像 固镇县 

分 类 号：S127[农业科学—农业基础科学] TP79[自动化与计算机技术—检测技术与自动化装置]