| 特征变量选择结合SVM的耕地土壤Hg含量高光谱反演 |
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| 引用本文： | 郭云开,张思爱,王建军,章琼,谢晓峰.特征变量选择结合SVM的耕地土壤Hg含量高光谱反演[J].测绘工程,2022,31(1):17-23. |
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| 作者姓名： | 郭云开 张思爱 王建军 章琼 谢晓峰 |
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| 作者单位： | 长沙理工大学 交通运输工程学院,长沙 410076;长沙理工大学 测绘遥感应用技术研究所,长沙 410076,清远市土地整理中心,广东 清远 511518,广州城建职业学院建筑工程学院,广州 510925 |
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| 摘 要： | 为探讨应用高光谱数据反演耕地土壤重金属汞（Hg）含量,对原始光谱进行10 nm重采样和SG平滑处理,用不同光谱变换数据与土壤重金属Hg含量进行相关性分析,采用IRIV、Random Frog和PCC提取光谱特征波段,分别建立SVM与GWO-SVM土壤Hg含量高光谱反演模型,获取Hg含量最优反演路径。研究表明,一阶微分变换光谱后土壤光谱特征更明显;上述特征提取方法在不同程度上减少光谱数据冗余,保留有效变量信息;经灰狼算法优化后支持向量机模型反演精度提高,IRIV结合GWO-SVM预测精度更高,其验证集R2为0.894,RMSE为0.082,MAE为0.016。研究成果可为类似土壤重金属含量的反演提供借鉴。
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| 关 键 词： | 土壤重金属 高光谱遥感 特征波段提取 灰狼算法 支持向量机 |
Feature variable selection combined with SVM for hyperspectral inversion of cultivated soil Hg content |
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| GUO Yunkai,ZHANG Siai,WANG Jianjun,ZHANG Qiong,XIE Xiaofeng.Feature variable selection combined with SVM for hyperspectral inversion of cultivated soil Hg content[J].Engineering of Surveying and Mapping,2022,31(1):17-23. |
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| Authors: | GUO Yunkai ZHANG Siai WANG Jianjun ZHANG Qiong XIE Xiaofeng |
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| Institution: | (School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410076,China;Institute of Surveying, Mapping and Remote Sensing Application Technology, Changsha University of Science and Technology, Changsha 410076,China;Qingyuan City Land Consolidation Center, Qingyuan 511518,China;School of Civil Engineering, Guangzhou Urban Construction Vocational College, Guangzhou 510925,China) |
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| Abstract: | In order to explore the application of hyperspectral data to invert the content of heavy metal mercury(Hg)in cultivated soils,the original spectrum is resampled and SG smoothed at 10nm,and the correlation between different spectral transformation data and soil heavy metal Hg content is analyzed.IRIV,Random Frog and PCC extracts spectral characteristic bands,and establishes SVM and GWO-SVM soil Hg content hyperspectral inversion models,respectively,to obtain the optimal Hg content inversion path.Research shows that the soil spectral characteristics are more obvious after the first-order differential transformation spectrum;the above-mentioned feature extraction methods reduce spectral data redundancy to varying degrees and retain effective variable information;the support vector machine model inversion accuracy is improved after the gray wolf algorithm is optimized.IRIV combined with GWO-SVM has higher prediction accuracy.Its verification set R2 is 0.894,RMSE is 0.082,and MAE is 0.016.The research results can provide reference for the inversion of similar soil heavy metal content. |
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| Keywords: | soil heavy metal hyperspectral remote sensing feature band extraction gray wolf algorithm support vector machine |
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