Pollution level mapping of heavy metal in soil for ground-airborne hyperspectral data with support vector machine and deep neural network: A case study of Southwestern Xiong'an, China

Author:

Wang Mingwei,Wang Cheng,Ruan Jinghou,Liu Wei,Huang Zhaoqiang,Chen Maolin,Ni Bin

Publisher

Elsevier BV

Subject

Health, Toxicology and Mutagenesis,Pollution,Toxicology,General Medicine

Reference36 articles.

1. Automated lithological mapping using airborne hyperspectral thermal infrared data: a case study from Anchorage Island, Antarctica;Black;Remote Sens. Environ.,2016

2. A comprehensive survey on support vector machine classification: applications, challenges and trends;Cervantes;Neurocomputing,2020

3. Sources and spatial distribution of heavy metals and arsenic in soils from Xiongan New Area, China;Dong;Hydrogeol. Eng. Geol.,2021

4. A study on support vector machine based linear and non-linear pattern classification;Ghosh,2019

5. Characteristics of soil heavy metal pollution and health risk in Xiong'an New District;Guo;China Environ. Sci.,2021

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