基于半监督回归的高光谱土壤重金属质量浓度反演

doi:10.3969/j.issn.0255-8297.2022.06.005

Abstract

Abstract: Aiming at training a robust inversion model of soil heavy metal mass concentration with a small number of labeled samples and a large number of unlabeled samples, we took cadmium (Cd) in soil as research object, and experimentally verified the model by using two groups of the spectral data of four different regions (Hengyang-Chenzhou, Yuanping-Baoding). A hyperspectral retrieval model of soil heavy metal mass concentration based on semi-supervised regression was proposed after reducing the spectral distribution differences of different regions by means of transfer component analysis. Experimental results show that compared with traditional fully supervised modeling method, in the group of Hengyang-Chenzhou, the semi-supervised method proposed in this paper can improve the determination coefficient R² to 0.75 and relative percent difference (RPD) to 2.15; In the group of Yuanping-Baoding, R² increases to 0.70, and RPD increases to 1.61. The experiments show that the model inversion accuracy can be effectively improved by introducing a large number of unlabeled samples to semi-supervised regression analysis in the situations of few labeled samples.

Key words: hyperspectral remote sensing, semi-supervised regression, transfer component analysis, inversion of soil heavy metal mass concentration

CLC Number:

P751.1

MAO Gengxuan, TU Yan, CUI Wenbo, TAO Chao. Hyperspectral Inversion of Soil Heavy Metal Mass Concentration Based on Semi-supervised Regression[J]. Journal of Applied Sciences, 2022, 40(6): 941-952.

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Hyperspectral Inversion of Soil Heavy Metal Mass Concentration Based on Semi-supervised Regression

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