<br />
Hyperspectral Mineral Mapping and Its Application

doi:10.6046/gtzyyg.2010.01.01

Download: PDF(2801 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Funded by Ministry of Land and Resources of China, China Geological Survey and National High Technology Research and

Development Program of China (National 863 Program), the Hyperspectral Research Group of China Aero Geophysical Survey and Remote

Sensing Center for Land and Resources (AGRS) has made remarkable scientific achievements in hyperspectral mineral identification

and mineral mapping since the implementation of the ‘Ninth Five-Year Plan’. These achievements include: (1) the analysis of

features of rock and mineral spectra and influencing factors, (2) mineral identification, abundance estimation and component

derivation, (3) multispectral and hyperspectral data processing and mineral mapping in mid-thermal infrared spectral region, (4)

geological application model, working methods and technical processes, and technology systematic architecture of hyperspectral

mineral mapping, and (5) applications of mineral mapping in such fields as regional mineral survey, geological exploration,

metallogenic and prospecting modeling, plant geochemical detection, mining environmental monitoring, and lunar and planetary

exploration. These achievements are reviewed and summarized in this paper, and the development tendency and research orientations

in hyperspectral mineral mapping are also discussed.

Keywords Hyperspectral remote sensing Soil content Water depth Pollution type Pollution degree Regression analysis Image analysis

Issue Date: 22 March 2010

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

WANG Run-Sheng, GAN Fu-Ping, YAN Bai-Kun, YANG Su-Ming, WANG Qing-Hua.
Hyperspectral Mineral Mapping and Its Application[J]. REMOTE SENSING FOR LAND & RESOURCES,2010, 22(1): 1-13.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.01.01 OR https://www.gtzyyg.com/EN/Y2010/V22/I1/1

[1]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[2]	GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[3]	JIANG Yanan, ZHANG Xin, ZHANG Chunlei, ZHONG Chengcheng, ZHAO Junfang. Classification of remote sensing images based on multi-scale feature fusion using local binary patterns[J]. Remote Sensing for Natural Resources, 2021, 33(3): 36-44.
[4]	ZANG Chuankai, SHEN Fang, YANG Zhengdong. Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 45-53.
[5]	HU Xinyu, XU Zhanghua, CHEN Wenhui, CHEN Qiuxia, WANG Lin, LIU Hui, LIU Zhicai. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image[J]. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
[6]	WANG Ruijun, ZHANG Chunlei, SUN Yongbin, WANG Shen, DONG Shuangfa, WANG Yongjun, YAN Bokun. Application of hyperspectral spectroscopy to constructing polymetallic prospecting model in Hongshan, Gansu Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 222-231.
[7]	Yiqiang SHI, Qiuqin DENG, Jun WU, Jian WANG. Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City[J]. Remote Sensing for Land & Resources, 2020, 32(1): 106-114.
[8]	Jun CHEN, Runping SHEN, Bolun LI, Chaopu TI, Xiaoyuan YAN, Minyue ZHOU, Shaowu WANG. The development of plastic greenhouse index based on Logistic regression analysis[J]. Remote Sensing for Land & Resources, 2019, 31(3): 43-50.
[9]	Tao DANG, Qi SONG, Yong LIU, Anjian XU, Bo XU, Honggang ZHANG. Extraction of buildings in remote sensing imagery based on multi-level segmentation and classification hierarchical model and feature space optimization[J]. Remote Sensing for Land & Resources, 2019, 31(3): 111-122.
[10]	Xiaojing FAN, Yongfu ZHANG, Zhenzhen CHENG. Research on energy consumption of Xinjiang based on DMSP/OLS night light data from 1992 to 2013[J]. Remote Sensing for Land & Resources, 2019, 31(1): 212-219.
[11]	Donghui ZHANG, Yingjun ZHAO, Kai QIN. Design and construction of the typical ground target spectral information system[J]. Remote Sensing for Land & Resources, 2018, 30(4): 206-211.
[12]	Jing CUI, Xinfeng DONG, Rui DING, Shimin ZHANG, Conghe WANG, Hengxin LU, Yanyun SUN. Stratigraphic division of loess along loess profile based on hyperspectral remote sensing[J]. Remote Sensing for Land & Resources, 2018, 30(2): 202-207.
[13]	Jia LIU, Xin XIN, Bin LIU, Kaichang DI, Zongyu YUE, Cheng’an WANG. Urban expansion mapping and driving factor analysis of Ordos City during the period of 2000—2013 based on DMSP/OLS nighttime light data[J]. Remote Sensing for Land & Resources, 2018, 30(1): 166-172.
[14]	SUN Na, GAO Zhiqiang, WANG Xiaojing, LUO Zhidong. High-precise extraction for water on the Loess Plateau region from high resolution satellite image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 173-178.
[15]	REN Guangli, YANG Min, LI Jianqiang, GAO Ting, LIANG Nan, YI Huan, YANG Junlu. Application of hyperspectral alteration information to gold prospecting: A case study of Fangshankou area,Beishan[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 182-190.