Discrimination of the degree of heavy metal pollution in corn leaves in mining areas based on semi-supervised learning-Reference-Cited by-同舟云学术

Discrimination of the degree of heavy metal pollution in corn leaves in mining areas based on semi-supervised learning

Published:2023-12-25 Issue:1 Volume:15 Page:10-23
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

He Jiale¹^ORCID,Yang Keming¹^ORCID,Yang Fei²,Li Yanru¹^ORCID,Wu Bing¹,Zhang Jianhong¹^ORCID

Affiliation:

1. School of Earth Science and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing, China

2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing, China

Funder

the National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2023.2293473

Reference15 articles.

1. Heavy metals concentration in food crops irrigated with pesticides and their associated human health risks in Paki, Kaduna State, Nigeria

2. Semi-supervised feature learning for disjoint hyperspectral imagery classification

3. Accuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy

4. A method for reconstructing NDVI time-series based on envelope detection and the Savitzky-Golay filter

5. Uptake of heavy metals by crops near a mining field: Pathways from roots and leaves

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning;2024