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

doi:10.1007/s40333-022-0050-8

Journal of Arid Land

2022, Vol. 14

Issue (1): 102-114 DOI: 10.1007/s40333-022-0050-8

Geography, geology and natural resources in Central Asia (Guest Editorial Board Member: Prof. Dr. XIAO Wenjiao)

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

MA Xiumei¹^,²^,³^,⁴, ZHOU Kefa¹^,²^,³^,⁴, WANG Jinlin¹^,²^,³^,⁴^,^*, CUI Shichao¹^,²^,³^,⁴, ZHOU Shuguang¹^,²^,³^,⁴, WANG Shanshan¹^,²^,³^,⁴, ZHANG Guanbin⁵

¹State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
²Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
³Xinjiang Research Centre for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
⁴University of Chinese Academy of Sciences, Beijing 100049, China
⁵Xinjiang Academy of Science and Technology for Development Strategy, Urumqi 830011, China

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Abstract

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 (R²) 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 (R²=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.

Key words： hyperspectral remote sensing Cu element bandwidth Partial Least Squares Regression inversion accuracy Kalatage polymetallic ore concentration area

Received: 30 October 2020 Published: 31 January 2022

Corresponding Authors: * WANG Jinlin (E-mail: wangjinlin@ms.xjb.ac.cn)

About author: First author contact:

The third author and the seventh author contributed equally to this work.

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	MA Xiumei
	ZHOU Kefa
	WANG Jinlin
	CUI Shichao
	ZHOU Shuguang
	WANG Shanshan
	ZHANG Guanbin

Cite this article:

MA Xiumei, ZHOU Kefa, WANG Jinlin, CUI Shichao, ZHOU Shuguang, WANG Shanshan, ZHANG Guanbin. Optimal bandwidth selection for retrieving Cu content in rock based on hyperspectral remote sensing. Journal of Arid Land, 2022, 14(1): 102-114.

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http://jal.xjegi.com/10.1007/s40333-022-0050-8 OR http://jal.xjegi.com/Y2022/V14/I1/102

Fig. 1 Geological map and alteration zone of the Yudai porphyry Cu (Au) deposit (revised according to Mao et al. (2017))

Fig. 2 Distribution of sampling points. YD, Yudai.

Fig. 3 Original spectral curves of some rocks (a) and spectral curves of some rocks after the removal of water vapour absorption bands (b)

Fig. 4 The 5 nm custom spectral response function (a) and spectral curves of the original rock resampled to 5 nm (b)

Fig. 5 Values of the prediction error sum of squares (PRESS) corresponding to the number of different principal components detected at 35 nm bandwidth using the Partial Least Squares Regression (PLSR) method

Table 1 Accuracy of Cu content prediction with different bandwidths based on the partial least squares regression (PLSR) model

Fig. 6 Accuracy curve of the PLSR model with different bandwidths for Cu content prediction

Fig. 7 Cu content at a bandwidth of 35 nm. It should be noted that due to the limitations of the PLSR model, negative values of Cu content will inevitably appear. LOOCV, leave-one-out cross validation.

Fig. 8 Equation and accuracy of Cu content predication with a bandwidth of 35 nm

Fig. 9 Accuracy and fitting results of the LOOCV method on Cu content prediction with a bandwidth of 35 nm

Fig. 10 Trend of Cu content for different samples. The x-axis indicates the sample number.


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