基于GF-1 WFV数据的玉米与大豆种植面积提取方法

发布时间：2018-04-02 00:27

  本文选题：遥感　切入点：提取　出处：《农业工程学报》2017年07期

【摘要】：准确掌握农作物的空间种植分布情况,对于国家宏观指导农业生产、制定农业政策有重要意义。针对黑龙江省玉米与大豆生育期接近、光谱特征相似,较难区分的问题,以多时相16 m空间分辨率高分一号(GF-1)卫星宽覆盖(wide field of view,WFV)影像为数据源,选择归一化植被指数(normalized difference vegetation index,NDVI)、增强植被指数(enhanced vegetation index,EVI)、宽动态植被指数(wide dynamic range vegetation index,WDRVI)、归一化水指数(normalized difference water index,NDWI)4个特征,结合实地调查样本点,采用随机森林分类算法,提取黑龙江省黑河市嫩江县玉米与大豆种植面积。研究表明,区分玉米与大豆的最佳时段为9月下旬至10月上旬,即大豆已收获而玉米未收获的时段,在4个待选特征中,NDVI、NDWI与WDRVI指数组合表现最佳;随机森林算法与最大似然算法、支持向量机算法相比,分类精度更高,其总体分类精度为84.82%,Kappa系数为77.42%。玉米制图精度为91.49%,用户精度为93.48%;大豆制图精度为91.14%,用户精度为82.76%。该方法为大区域农作物的分类提供重要参考和借鉴价值。
[Abstract]:It is of great significance for the state to guide agricultural production and formulate agricultural policies to accurately understand the spatial planting and distribution of crops.Aiming at the problem that the growth period of maize and soybean in Heilongjiang Province is close, the spectral characteristics are similar, and it is difficult to distinguish the spectral characteristics, the multitemporal spatial resolution of high resolution GF-1 (GF-1) satellite wide coverage field of view WFV image is taken as the data source.Four characteristics of normalized difference vegetation index NDVI, enhanced vegetation index EVI, wide dynamic range vegetation indexWDRVI, normalized difference water index NDWI) were selected. The random forest classification algorithm was used in combination with field survey sample points.Corn and soybean planting area were extracted from Nenjiang County, Heihe City, Heilongjiang Province.The results showed that the best time to distinguish maize from soybean was from late September to early October, that is, soybean had been harvested but maize had not been harvested, and the combination of NDWI and WDRVI index was the best among the four selected characters.Compared with the maximum likelihood algorithm and the support vector machine algorithm, the stochastic forest algorithm has higher classification accuracy, and its overall classification accuracy is 84.82 and the Kappa coefficient is 77.42.The precision of maize mapping is 91.49, the user accuracy is 93.48, the precision of soybean mapping is 91.14 and the user precision is 82.76.This method provides an important reference and reference value for the classification of crops in large area.
【作者单位】： 中国农业大学信息与电气工程学院;
【基金】：国家自然科学基金资助(41671418,41471342,41371326)
【分类号】：S513;S565.1;S127
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本文编号：1697991