| 基于高光谱的黑土区土壤重金属含量估测 |
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| 引用本文： | 林楠,刘翰霖,孟祥发,刘海琪,杨佳佳.基于高光谱的黑土区土壤重金属含量估测[J].农业机械学报,2021,52(3):218-225. |
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| 作者姓名： | 林楠 刘翰霖 孟祥发 刘海琪 杨佳佳 |
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| 作者单位： | 吉林建筑大学;东北大学;中国地质调查局沈阳地质调查中心 |
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| 基金项目： | 国家自然科学基金项目（41702357、41801283）和吉林省教育厅科学技术项目（JJKH20180608KJ） |
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| 摘 要： | 以黑龙江省讷河市采集的80份黑土样品和高光谱实测数据为数据源,对黑土中铜(Cu)、锌(Zn)、锰(Mn)重金属元素的光谱反射率及其特征变化进行研究,分析了光谱反射率、光谱反射率一阶微分变换、光谱反射率连续统去除变换、光谱反射率连续统去除一阶微分变换与元素铜、锌、锰含量的相关性,并利用相关系数法提取敏感波段。利用核主成分分析(Kernel principal component analysis,KPCA)方法对高光谱敏感波段数据进行降维及特征提取,将特征信息作为极限学习机(Extreme learning machine,ELM)模型建模的样本数据,构建KPCA-ELM估测模型,进行黑土重金属含量的定量估算。结果表明:KPCA具有较强的非线性特征提取能力,可以有效地选择最佳变量集合,KPCA-ELM模型预测土壤元素含量效果理想,3种重金属元素含量估测的决定系数均达到0.6以上,其中,锌元素预测精度最高,决定系数和均方根误差分别为0.805和3.275 mg/kg,比特征提取前模型预测精度优化了14.0%和18.5%,说明构建的KPCA-ELM模型是一种快速可行的重金属含量高光谱估测方法。
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| 关 键 词： | 黑土区 重金属 高光谱 含量估测 |
| 收稿时间： | 2020/5/21 0:00:00 |
Hyperspectral Estimation of Heavy Metal Contents in Black Soil Region |
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| LIN Nan,LIU Hanlin,MENG Xiangf,LIU Haiqi,YANG Jiajia.Hyperspectral Estimation of Heavy Metal Contents in Black Soil Region[J].Transactions of the Chinese Society of Agricultural Machinery,2021,52(3):218-225. |
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| Authors: | LIN Nan LIU Hanlin MENG Xiangf LIU Haiqi YANG Jiajia |
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| Institution: | Jilin Jianzhu University;Northeastern University; Shenyang Institute of Geology and Mineral Resources,China Geological Survey |
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| Abstract: | Taking 80 black soil samples collected from Nehe City, Heilongjiang Province, and hyperspectral measured data as data sources, the spectral reflectance and its feature changes of copper (Cu), zinc (Zn), manganese (Mn) in black soil were analyzed, the correlations between four different forms of spectral reflectance, which included original, first-order differential, continuum removal, and first order derivative of continuum removal and soil Cu, Zn, Mn contents were calculated, and the correlation coefficient method was used to extract sensitive bands. Then the kernel principal component analysis (KPCA) was applied for dimension reduction and feature extraction of hyperspectral sensitive band data, and the feature information was input into extreme learning machine (ELM), and the KPCA-ELM estimation model was constructed to quantitatively estimate the heavy metal contents. The results showed that KPCA had a strong ability to extract nonlinear features and effectively selected the optimal variable set. The KPCA-ELM model was feasible in predicting soil element content and the determination coefficients of the three heavy metal elements were all more than 0.6, where the prediction accuracy of Zn was the highest among the three heavy metal elements. And the determination coefficient and root mean square error were 0.805 and 3.275mg/kg respectively, which were improved by 14.0% and 18.5% compared with without feature extraction. Therefore, KPCA-ELM model was a fast and feasible method for hyperspectral estimation of heavy metal content. |
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| Keywords: | black soil region heavy metal hyperspectral content estimation |
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