期刊论文

Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique

中文

农业工程学报

1002-6819

2016

32

13

95-102

10.11975/j.issn.1002-6819.2016.13.014

41401232:国家自然科学基金 41271534:国家自然科学基金 CCNU15A05006:中央高校基本科研业务费专项 CCNU15ZD001:中央高校基本科研业务费专项

本校为第一机构

土壤高光谱数据量大、波段维数高,存在光谱信息无效、冗余和重叠现象,导致基于全波段构建的土壤有机质含量反演模型不稳定、精度难以提升。因此,探寻筛选关键波长变量的方法,通过滤除干扰、冗余、共线信息,提高模型预测性能,是目前土壤高光谱研究的热点之一。该文对江汉平原公安县的土壤样本进行室内理化分析、光谱测量与处理等工作获取了实证数据,采用无信息变量消除法（uninformative variables elimination,UVE）剔除无效变量,利用竞争性自适应重加权算法（competitive adaptive reweighted sampling,CARS）滤除冗余变量,运用连续投影算法（successive projections algorithm,SPA）消除共线变...

During the past decades, soil hyperspectral reflectance had been showed to be a rapid, convenient, low-cost and alternative method for estimating soil key properties. However, the hyperspectral dataset may have thousands of variables because modern spectroscopy instruments usually had a high resolution. Moreover, the full-spectrum includes many wavelengths which could contribute the collinearity, redundancy and noise to models. Thus, the key variable selection is an important step in soil hyperspectral modeling analysis. The main objectives of ...