Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

被引：7

作者：

Qin, Yuehan ^{[1
]}

Zhang, Xinle ^{[2
,3
,4
]}

Zhao, Zhifang ^{[2
,3
,4
]}

Li, Ziyang ^{[2
,3
,4
]}

Yang, Changbi ^{[5
]}

Huang, Qunying ^{[1
]}

机构：

[1] Univ Wisconsin, Dept Geog, Madison, WI 53706 USA

[2] Yunnan Univ, Sch Earth Sci, Kunming 650500, Yunnan, Peoples R China

[3] Engn Res Ctr Domest High Resolut Satellite Remote, Kunming 650500, Yunnan, Peoples R China

[4] MNR Key Lab Sanjiang Metallogeny & Resources Expl, Kunming 650051, Yunnan, Peoples R China

[5] Yunnan Bur Geol & Mineral Explorat & Dev, Geol Brigade 2, Wenshan 663000, Peoples R China

来源：

REMOTE SENSING | 2022年 / 14卷 / 01期

基金：

中国国家自然科学基金;

关键词：

coupling relationship analysis; GF-5 hyperspectral data; geographically weighted regression (GWR); spectrum-area (S-A); large-sample-learning; Au geochemical anomaly; Chahuazhai Carlin-type Gold Deposit; SW China; MACHINE LEARNING ALGORITHMS; OPHIOLITE COMPLEX; DEPOSITS; GUIZHOU; DISTRICT; CLASSIFICATION; GEOCHEMISTRY; KURDISTAN; REGION;

D O I：

10.3390/rs14010109

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky-Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

引用

页数：20