Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing

Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands, high resolution, and abundant information. Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element conten...

Full description

Saved in:
Bibliographic Details
Published in:Journal of arid land 2022, Vol.14 (1), p.102-114
Main Authors: Ma, Xiumei, Zhou, Kefa, Wang, Jinlin, Cui, Shichao, Zhou, Shuguang, Wang, Shanshan, Zhang, Guanbin
Format: Article
Language:English
Subjects:
Accuracy
Bandwidths
Beneficiation
Copper
Earth and Environmental Science
Geochemistry
Geography
geology and natural resources in Central Asia (Guest Editorial Board Member: Prof. Dr. XIAO Wenjiao)
Inversion
Least squares method
Metals
Physical Geography
Plant Ecology
Regression models
Remote sensing
Rock
Rocks
Sediment samples
Spectra
Sustainable Development
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands, high resolution, and abundant information. Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks, the influence of bandwidth on the inversion accuracy are ignored. In this study, we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City, Xinjiang Uygur Autonomous Region, China and measured the ground spectra of these samples. The original spectra were resampled with different bandwidths. A Partial Least Squares Regression (PLSR) model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored. According to the results, the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm, with the model determination coefficient ( R 2 ) of 0.5907. The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5–80 nm, but the accuracy decreases significantly at 85 nm bandwidth ( R 2 =0.5473), and the accuracy gradually decreased at bandwidths beyond 85 nm. Hence, bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model. This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.
ISSN:1674-6767
2194-7783
DOI:10.1007/s40333-022-0050-8