基于特征优选的GF-6WFV影像主要粮食作物提取  

Major food crops extraction from GF-6 WFV multispectral imagery based on feature optimization

作　　者：许康[1] 黄冰鑫 王鹏飞 XU Kang;HUANG Bing-xin;WANG Peng-fei(Jiangsu Province Surveying&Mapping Engineering Institute,Nanjing 210013,China;School of Earth Science and Engineering,Hohai University,Nanjing 211100,China)

机构地区：[1]江苏省测绘工程院,南京210013 [2]河海大学地球科学与工程学院,南京211100

出　　处：《湖北农业科学》2024年第2期59-66,共8页Hubei Agricultural Sciences

基　　金：自然资源部国土卫星遥感应用重点实验室经费资助项目(KLSMNR-K202209);江苏省农业科技自主创新资金项目[CX(22)2001]。

摘　　要：针对高分六号(GF-6)宽幅多光谱影像具有红边波段的特点,构建一种基于特征优选的GF-6 WFV影像主要粮食作物提取方法。首先从预处理后的GF-6影像中提取光谱特征、植被指数、水体指数和红边指数特征,然后利用递归特征消除算法进行特征优选来构建最优特征集,最后基于最优特征集和机器学习算法对影像进行分类从而提取主要粮食作物。以江苏省南通市如东县为研究区,采用6种方案进行粮食作物提取试验,并探讨不同特征、不同分类模型对小麦、水稻和玉米3种粮食作物提取精度的影响,结果表明,利用GF-6 WFV影像可以准确提取主要粮食作物,尤其在红边波段和红边指数上主要粮食作物与其他地物间具有较高的可分性;利用最优特征集和XGBoost算法对影像进行分类的精度最高,在小麦和水稻、玉米提取试验中比未采用红边特征时的分类精度分别提高了3.08、5.58个百分点。In view of the characteristics of multiple red edge bands of GF-6 wide field view(WFV)multispectral imagery,a method for extracting major food crops from GF-6 WFV image based on feature optimization was proposed.Firstly,characteristic variables,including spectral feature,vegetation index,water index and red edge index,were extracted from preprocessed GF-6 WFV image.Then,the optimal feature set was generated by using a recursive feature elimination algorithm with permutation importance.Finally,machine learning methods and the optimal feature combination were utilized to extract major food crops.Taking Rudong County,Jiangsu Province as the study area,six experiments were used to extract grain crops,and the effects of different characteristics and different classification models on the extraction accuracy of wheat,rice and corn were discussed.The results indicated that the GF-6 WFV image was suitable for extracting major food crops,and the two red-edge bands and red edge indexes of GF-6 WFV data played an important role in distinguishing three main food crops and other objects.Among the six experiments,the overall accuracy of the classification result based on the optimal feature combination and XGBoost algorithm was the highest,improving 3.08 and 5.58 percentage point respectively compared with the classification result without using red edge bands and indexes.

关 键 词：高分六号 粮食作物 红边波段 特征选择 XGBoost 

分 类 号：S127[农业科学—农业基础科学] S511S512.1