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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
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.

hyperspectral remote sensingCu elementbandwidthPartial Least Squares Regressioninversion accuracyKalatage polymetallic ore concentration area

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

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State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China

Xinjiang Key Laboratory of Mineral Resources and Digital Geology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China

Xinjiang Research Centre for Mineral Resources,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China

University of Chinese Academy of Sciences,Beijing 100049,China

Xinjiang Academy of Science and Technology for Development Strategy,Urumqi 830011,China

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Science and Technology Major Project of Xinjiang Uygur Autonomous Region,ChinaKey Area Deployment Project of the Chinese Academy of Sciences国家自然科学基金

2021A03001-3ZDRW-ZS-2020-4-30U1803117

2022

干旱区科学
中国科学院新疆生态与地理研究所,科学出版社

干旱区科学

CSTPCDCSCDSCI
影响因子：1.743
ISSN：1674-6767
年,卷(期)：2022.14(1)
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