摘要
为探究高分六号(GF6)宽幅遥感影像红边波段在春季作物识别中的应用,以河南省杞县为研究区,通过分析2019年3月25日单时相影像及其光谱特征,利用随机森林算法完成4种不同红边波段方案下冬小麦、大蒜和其他作物(油菜、蔬菜等)的分类提取,并基于地面采样数据实现不同方案分类精度评价、样本间可分性测度以及光谱反射率计算分析。结果表明,有红边波段参与下,较无红边波段参与时作物总体分类精度和不同作物可分性测度值均有所提高;单红边波段参与下,红边波段2作物总体分类精度较红边波段1提高了1.98个百分点;引入全部红边波段较无红边参与方案的作物总体分类精度由81.56%提高到86.19%,提高了4.63个百分点,Kappa系数由0.72提高到0.79,冬小麦-大蒜、冬小麦-其他作物、大蒜-其他作物的J-M(Jeffries-Matusita)可分性测度也分别增加了0.0856、0.0761和0.0251。研究表明,红边波段的引入不仅增加了作物间的可分性测度,降低了分类结果中作物误分、漏分情况,也在一定程度上降低了结果中的“椒盐现象”,为国产红边卫星数据在农业上的应用提供参考。
The objective of this study is to explore the application of the red edge bands of GF6 WFV image in the identification of spring crops.Based on analysis of the spectral characteristics of single image,the identification and acreage extraction of major spring crops can be effectively achieved by random forest algorithm,taking Qixian,Henan Province as a study area,and employing basic image with 8 bands,which was collected in March 25th,2019.Combined with the ground samples and sample points data,the overall classification accuracy of four schemes,J-M distance and spectral reflectance among different training samples were calculated and analyzed.The result showed that,compared with the scheme without red-edge,the overall identification accuracy of 3 types of ground objects(winter wheat,garlic and others)with one or more red-edge was enhanced,and the separability was improved by calculating the J-M distance of different features.Compared with the scheme with red-edge band 1,the overall classification accuracy of red-edge band 2 was improved by 1.98 percentage points.The overall identification accuracy of 3 types of ground objects with all red-edge bands was 86.19%,the Kappa coefficient was 0.79,while the overall identification accuracy of 3 types of ground objects without red-edge was 81.56%,and the Kappa coefficient was 0.72.By introducing all red-edge bands,the overall identification accuracy of 3 ground objects was improved by 4.63 percentage points,the separabilities of winter wheat-garlic,winter wheat-other crops and garlic-other crops were increased by 0.0856,0.0761 and 0.0251 based on J-M distance,respectively.Therefore,by introducing red-edge band,the rate of wrong classification and miss classification,and“Pepper salt”effect were reduced.It could improve the overall identification accuracy of crop planting area.The result of this paper will provide a reference for the application of domestically-produced red-edge satellite data in agriculture.
作者
王利军
郭燕
王来刚
贺佳
张红利
刘婷
WANG Lijun;GUO Yan;WANG Laigang;HE Jia;ZHANG Hongli;LIU Ting(Institute of Agricultural Economics and Information,Henan Academy of Agricultural Sciences,Zhengzhou 450002,China)
出处
《河南农业科学》
北大核心
2020年第6期165-173,共9页
Journal of Henan Agricultural Sciences
基金
河南省科技攻关(国际科技合作)项目(182102410024)
河南省重大科技专项(171100110600)
河南省农业科学院科研发展专项(2020CY019)
河南省农业科学院农业经济与信息研究所科研助理人才培养专项(njxxsjj201901)。
