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

摘要/Abstract

摘要： 针对如何利用少量有标记样本和大量无标记样本训练出鲁棒性的土壤重金属质量浓度反演模型的问题，以土壤中重金属镉（Cd）为研究对象，选取4个不同地区（衡阳-郴州，原平-保定）的光谱数据分两组进行实验验证。在通过迁移成分分析方法缩小不同区域的光谱分布差异后，提出一种基于半监督回归的高光谱土壤重金属质量浓度反演模型。实验结果显示，与传统的全监督建模方法相比，在第1组衡阳-郴州的实验中，所提的半监督方法能够将可决系数R²提升至0.75，相对分析误差(relative predictive deviation,RPD)提升至2.15；在第2组原平-保定的实验中，R²提升至0.70，RPD提升至1.61。实验表明，在较少标记样本情况下，通过引入大量的未标记样本进行半监督回归分析可有效提升模型反演精度。

关键词: 高光谱遥感, 半监督回归, 迁移成分分析, 土壤重金属质量浓度反演

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

中图分类号:

P751.1

毛耿旋, 涂彦, 崔文博, 陶超. 基于半监督回归的高光谱土壤重金属质量浓度反演[J]. 应用科学学报, 2022, 40(6): 941-952.

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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