Random Forests-Based Hybrid Feature Selection Algorithm for Soil Potassium Content Inversion Using Hyperspectral Technology

doi:10.3964/j.issn.1000-0593(2018)12-3883-07

	Random Forests-Based Hybrid Feature Selection Algorithm for Soil Potassium Content Inversion Using Hyperspectral Technology
	Wang Xuan-hui 1,2; Zheng Xi-lai 1; Han Zhong-zhi 2; Wang Xuan-li 3; Wang Juan 4
刊名	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2018-12
卷号	38 期号:12 页码:3883-3889
关键词	Soil available potassium content Hyperspectral Characteristic wavelength selection Hybrid feature selection Random forests
ISSN号	1000-0593
DOI	10.3964/j.issn.1000-0593(2018)12-3883-07
英文摘要	In order to solve the problem of lower prediction performance caused by the difficulty in retrieving the key features from hyperspectral data of soil available potassium, this paper proposes a novel hybrid feature selection algorithm based on Random Forests. Firstly, wrapper-based feature selection methods were applied to rapidly remove the redundancies and preserve the related features. Secondly, an Improved-RF feature selection algorithm was applied to further accurately select the wavelength variables from the pre-selected feature sets. In this step, characteristic wavelength with strong robustness and discriminative could be selected through improving the dipartite degree between the key and redundant features and using an iterative feature selection method. Therefore, the problem of low prediction performance in the soil available potassium inversion model could be better solved by using our hybrid feature selection algorithm. In order to verify the validity of our algorithm, 124 representative soil samples collected from the Dagu River Basin were chosen. Using our algorithm, the optimal feature subset which contained 13 sensitive bands have been selected and used to build soil available potassium content inversion model. This work compared the model performance of full bands, current feature selection algorithms and our algorithm. The comparison results indicated that our algorithm not only selects minimum numbers of wavelength features and reduces the dimension of full bands, but also achieves better prediction performance with lower RMSEP (9. 661 5), higher R (0. 936 9) and RPD (2. 14). As an effective method of soil available potassium inversion model, the algorithm proposed in this paper can provide theoretical basis for the design of real-time soil nutrient sensors.
WOS关键词	INFRARED SPECTROSCOPY
WOS研究方向	Spectroscopy
语种	中文
出版者	OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
WOS记录号	WOS:000454185500038
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/25019]
专题	自然资源部第一海洋研究所
通讯作者	Zheng Xi-lai
作者单位	1.Ocean Univ China, Key Lab Marine Environm Sci & Ecol, Minist Educ, Coll Environm Sci & Engn, Qingdao 266100, Peoples R China 2.Qingdao Agr Univ, Sci & Informat Coll, Qingdao 266109, Peoples R China 3.Shanxi Inst Technol, Informat Engn & Automat Dept, Yangquan 045000, Peoples R China 4.State Ocean Adm, Environm Monitoring Ctr North China Sea, Qingdao 266033, Peoples R China
推荐引用方式 GB/T 7714	Wang Xuan-hui,Zheng Xi-lai,Han Zhong-zhi,et al. Random Forests-Based Hybrid Feature Selection Algorithm for Soil Potassium Content Inversion Using Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2018,38(12):3883-3889.
APA	Wang Xuan-hui,Zheng Xi-lai,Han Zhong-zhi,Wang Xuan-li,&Wang Juan.(2018).Random Forests-Based Hybrid Feature Selection Algorithm for Soil Potassium Content Inversion Using Hyperspectral Technology.SPECTROSCOPY AND SPECTRAL ANALYSIS,38(12),3883-3889.
MLA	Wang Xuan-hui,et al."Random Forests-Based Hybrid Feature Selection Algorithm for Soil Potassium Content Inversion Using Hyperspectral Technology".SPECTROSCOPY AND SPECTRAL ANALYSIS 38.12(2018):3883-3889.