高光谱成像
环境科学
Mercury(编程语言)
采样(信号处理)
遥感
土工试验
土壤科学
土壤水分
地质学
计算机科学
计算机视觉
滤波器(信号处理)
程序设计语言
作者
Kun Tan,Weibo Ma,Lihan Chen,Huimin Wang,Qian Du,Peijun Du,Bokun Yan,Rongyuan Liu,Haidong Li
标识
DOI:10.1016/j.jhazmat.2020.123288
摘要
The problem of heavy metal pollution of soils in China is severe. The traditional spectral methods for soil heavy metal monitoring and assessment cannot meet the needs for large-scale areas. Therefore, in this study, we used HyMap-C airborne hyperspectral imagery to explore the estimation of soil heavy metal concentration. Ninety five soil samples were collected synchronously with airborne image acquisition in the mining area of Yitong County, China. The pre-processed spectrum of airborne images at the sampling point was then selected by the competitive adaptive reweighted sampling (CARS) method. The selected spectral features and the heavy metal data of soil samples were inverted to establish the inversion model. An ensemble learning method based on a stacking strategy is proposed for the inversion modeling of soil samples and image data. The experimental results show that this CARS-Stacking method can better predict the four heavy metals in the study area than other methods. For arsenic (As), chromium (Cr), lead (Pb), and zinc (Zn), the determination coefficients of the test data set (RP2) are 0.73, 0.63, 0.60, and 0.71, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.
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