| 基于GF-2遥感影像的塑料大棚提取方法对比 |
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| 引用本文： | 高梦婕,姜群鸥,赵一阳,杨文涛,史明昌.基于GF-2遥感影像的塑料大棚提取方法对比[J].中国农业大学学报,2018,23(8):125-134. |
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| 作者姓名： | 高梦婕 姜群鸥 赵一阳 杨文涛 史明昌 |
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| 作者单位： | 北京林业大学水土保持学院/水土保持国家林业局重点实验室 |
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| 基金项目： | 典型脆弱生态修复与保护研究（2017YFC050550604） |
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| 摘 要： | 为实现GF-2遥感影像在农业领域的有效利用,针对GF-2遥感影像提取塑料大棚,对比分析随机森林、CART决策树及支持向量机3种分类方法的应用,以GF-2遥感影像为数据源,以内蒙古赤峰市喀喇沁旗王爷府镇为研究区,通过潜在分割误差(PSE)、分割强度(NSR)、欧氏距离(ED)3个指标确定最优分割参数组合,利用随机森林(RF)算法筛选出参与分类的最优特征子集,采用随机森林、CART决策树、支持向量机3种分类器进行了塑料大棚提取对比分析。试验结果表明:1)基于PSE、NSR和ED最优分割参数选择方法,应用于面向对象连片塑料大棚特定地物提取的研究分割效果较好;2)通过RF算法分析包含光谱、纹理、形状及相邻关系等多种特征,得出特征个数与分类精度之间呈现先逐渐增大后减小的趋势,该方法在保证分类精度的同时,可有效删除冗余与不相关特征,以提高分类器性能;3)将采用最优特征子集的3种分类器进行对比,随机森林分类效果最好,分类正确率达到89.65%,表明该方法能有效提取GF-2遥感影像连片塑料大棚,为提取设施农业的应用提供参考。
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| 关 键 词： | GF-2遥感影像 分割参数 随机森林 特征选择 塑料大棚提取 |
| 收稿时间： | 2017/10/17 0:00:00 |
Comparison of plastic greenhouse extraction method based on GF-2 remote-sensing imagery |
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| GAO Mengjie,JIANG Qunou,ZHAO Yiyang,YANG Wentao and SHI Mingchang.Comparison of plastic greenhouse extraction method based on GF-2 remote-sensing imagery[J].Journal of China Agricultural University,2018,23(8):125-134. |
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| Authors: | GAO Mengjie JIANG Qunou ZHAO Yiyang YANG Wentao and SHI Mingchang |
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| Institution: | School of Soil and Water Conservation/Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China,School of Soil and Water Conservation/Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China,School of Soil and Water Conservation/Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China,School of Soil and Water Conservation/Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China and School of Soil and Water Conservation/Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China |
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| Abstract: | |
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| Keywords: | GF-2 remote-sensing imagery segmentation parameters random forest feature selection extraction of plastic greenhouses |
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