| 基于高分六号WFV数据的冬小麦种植面积提取 |
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| 引用本文： | 刘宝康,王仁军,尤晓妮,于志远,南岭,张起升,黄炳婷.基于高分六号WFV数据的冬小麦种植面积提取[J].测绘与空间地理信息,2021,44(1):1-4. |
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| 作者姓名： | 刘宝康 王仁军 尤晓妮 于志远 南岭 张起升 黄炳婷 |
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| 作者单位： | 天水师范学院 资源与环境工程学院,甘肃 天水741000;天水师范学院 资源与环境工程学院,甘肃 天水741000;天水师范学院 资源与环境工程学院,甘肃 天水741000;天水师范学院 资源与环境工程学院,甘肃 天水741000;天水师范学院 资源与环境工程学院,甘肃 天水741000;天水师范学院 资源与环境工程学院,甘肃 天水741000;天水师范学院 资源与环境工程学院,甘肃 天水741000 |
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| 摘 要： | 冬小麦是我国重要的粮食作物之一,准确获取冬小麦种植面积具有重要的现实意义。为探究高分六号卫星影像进行冬小麦遥感监测的可行性和精确性,本文选取甘肃省崆峒区为研究区,运用红边波段+监督分类中的支持向量机法,提取了2019年崆峒区冬小麦种植面积,并利用混淆矩阵对分类结果进行精度验证。结果表明:提取崆峒区冬小麦种植面积为15045 hm 2,与实际种植面积相比,误差率为1.02%;该模型能有效地提取崆峒区冬小麦,总体精度为98.88%,Kappa系数为0.97;红边波段能有效地提取干扰地物,提取精度比直接使用监督分类高7.88个百分点;GF6影像在提取冬小麦种植面积上具有明显优势。
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| 关 键 词： | GF6 崆峒区 冬小麦 面积 提取 |
Extraction of Winter Wheat Area Based on GF6-WFV Remote Sensing Image |
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| LIU Baokang,WANG Renjun,YOU Xiaoni,YU Zhiyuan,NAN Ling,ZHANG Qisheng,HUANG Bingting.Extraction of Winter Wheat Area Based on GF6-WFV Remote Sensing Image[J].Geomatics & Spatial Information Technology,2021,44(1):1-4. |
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| Authors: | LIU Baokang WANG Renjun YOU Xiaoni YU Zhiyuan NAN Ling ZHANG Qisheng HUANG Bingting |
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| Institution: | (College of Resources and Environmental Engineering,Tianshui Normal University,Tianshui 741000,China) |
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| Abstract: | Winter wheat is one of the vital food crops in China.It′s of great practical significance to acquire the planting area of winter wheat accurately.The mission of this study was to explore the feasibility and accuracy of remote sensing monitoring of winter wheat by satellite image of GF6-WFV and taking Kongtong District of Gansu Province as study area,using Red Band and supervised model to extract the winter wheat planting area of Kongtong District in 2019.And the accuracy of classification results was verified by the confusion matrix.The results showed that Red Band and Supervised model,extracting the area of Kongtong District was 15045 hm 2,overall accuracy was 98.88%,Kappa coefficient was 0.97,and error rate compared to the actual area is 1.02%,is valid to extract winter wheat acreage of Kongtong District.The red edge band can effectively extract disturbed objects,and the extraction accuracy is 7.88 percentage points higher than that of direct supervised classification.This study will provide GF6-WFV images which have obvious advantages in extracting winter wheat planting area. |
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| Keywords: | GF6 Kongtong District winter wheat area extract |
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