| 矿区土壤重金属Pb、Cd污染状况高光谱分类建模 |
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| 引用本文： | 钱佳,郭云开,章琼,蒋明.矿区土壤重金属Pb、Cd污染状况高光谱分类建模[J].测绘通报,2019,0(9):82-84,89. |
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| 作者姓名： | 钱佳 郭云开 章琼 蒋明 |
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| 作者单位： | 长沙理工大学交通运输工程学院,湖南 长沙410014;长沙理工大学测绘遥感应用技术研究所,湖南 长沙410076;长沙理工大学交通运输工程学院,湖南 长沙410014;长沙理工大学测绘遥感应用技术研究所,湖南 长沙410076;长沙理工大学交通运输工程学院,湖南 长沙410014;长沙理工大学测绘遥感应用技术研究所,湖南 长沙410076;长沙理工大学交通运输工程学院,湖南 长沙410014;长沙理工大学测绘遥感应用技术研究所,湖南 长沙410076 |
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| 基金项目： | 国家自然科学基金（41671498；41471421） |
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| 摘 要： | 针对矿区土壤重金属含量高度变异性及样本不均衡导致重金属污染状况分类误差较大的问题，本文在光谱预处理及光谱变换基础上，采用主成分分析（PCA）对光谱进行降维处理，并通过SMOTE算法生成虚拟样本均衡各污染等级样本，最后应用随机森林（RF）对Cd、Pb进行回归与分类。研究结果表明：定量反演重金属Pb、Cd含量精度很低；在定性分析试验中对降维前光谱样本应用SMOTE算法，土壤重金属Pb、Cd污染等级分类精度较原始样本分类精度均有较大提升，且少数类别误判率也降低明显。其研究为大面积监测矿区土壤重金属污染状况提供了一种有效、精确的方法。
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| 关 键 词： | SMOTE算法 高光谱 土壤重金属 随机森林 分类 |
| 收稿时间： | 2019-02-26 |
Pollution classification of heavy metals Pb and Cd in mining area based on hyperspectral |
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| QIAN Jia,GUO Yunkai,ZHANG Qiong,JIANG Ming.Pollution classification of heavy metals Pb and Cd in mining area based on hyperspectral[J].Bulletin of Surveying and Mapping,2019,0(9):82-84,89. |
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| Authors: | QIAN Jia GUO Yunkai ZHANG Qiong JIANG Ming |
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| Institution: | 1. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410014, China;2. Institute of Surveying and Mapping Remote Sensing Application Technology, Changsha University of Science & Technology, Changsha 410076, China |
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| Abstract: | In view of the high variability of soil heavy metal content and the imbalanced samples lead to the high classification error of heavy metal pollution in mining area.Based on the spectral preprocessing and spectral transformation, this paper uses principal component analysis (PCA) for spectral dimension, and applies SMOTE algorithm to generate virtual samples balance each pollution grade sample, and heavy metal Cd and Pb are regressed and classified by random forest(RF). The results show that the quantitative inversion precision of heavy metals Pb and Cd is bad. In the qualitative analysis experiment, SMOTE algorithm was applied to spectral samples before dimension reduction. The classification accuracy of Pb and Cd pollution levels in soil was greatly improved compared with that of the original samples, and the misjudgment rate of a few categories was also significantly decreased. The study provides an effective and accurate method for monitoring soil heavy metal pollution in a large area. |
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| Keywords: | SMOTE algorithm hyper-spectral soil heavy metal random forest classification |
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