Vegetation corrected continuum depths model and its application in mineral extraction from hyperspectral image

被引：4
|
作者：
College of Geoexploration Science and Technology, Jilin University, Changchun [1 ]
130026, China
不详 [2 ]
100027, China
不详 [3 ]
215000, China
机构：
[1] College of Geoexploration Science and Technology, Jilin University, Changchun
[2] Oil and Gas Survey Geological Survey, Beijing
[3] GIS Training Centre Chinese Academy of Sciences, Suzhou
来源：
Diqiu Kexue Zhongguo Dizhi Daxue Xuebao | / 8卷 / 1365-1370期
关键词：
Continuum removal; Hydroxyl/carbonate mineral content; Hyperion; Hyperspectral; Remote sensing; Vegetation;
D O I：
10.3799/dqkx.2015.119
中图分类号：
学科分类号：
摘要：
The objective of this study is to enhance the absorption feature of hydroxyl and carbonate minerals, and to improve the precision of the minerals information extraction in the vegetation covered area. The linear mixing spectra of a pixel containing a hydroxyl/carbonate mineral, green and dry vegetation has been simulated. When a fixed wavelength range is considered, continuum removed absorption depths for diagnostic absorption features of three end-members show significantly linear relation. The vegetation corrected continuum depths (VCCD) model was established to detect hydroxyl or carbonate mineral, which was tested with hyperspectral data (Hyperion) collected at Huma in Xiaoxing' anling. Comparing the extracting mineral results and field samples of rock, it is found that the extracting minerals information correspond with that of the polished section of mineral, but the disturbance information is found in the river bed or along the road. ©, 2015, China University of Geosciences. All right reserved.
引用
收藏
页码：1365 / 1370
页数：5
相关论文
共 18 条
  • [1] Chen S.B., Liu Y.L., Yang Q., Et al., Lithological Classification from Hyperspectral Data in Dense Vegetation Cover Area, Journal of Jilin University(Earth Science Edition), 42, 6, pp. 1959-1965, (2013)
  • [2] Clark R.N., King T.V.V., Klejwa M., Et al., High Spectral Resolution Reflectance Spectroscopy of Minerals, Journal of Geophysical Research, 95, B8, (1990)
  • [3] Crowley J.K., Brickey D.W., Rowan L.C., Airborne Imaging Spectrometer Data of the Ruby Mountains, Montana: Mineral Discrimination Using Relative Absorption Band-Depth Images, Remote Sensing of Environment, 29, 2, pp. 121-134, (1989)
  • [4] Galvao L.S., Almeida-Filho R., Vitorello I., Spectral Discrimination of Hydrothermally Altered Materials Using ASTER Short-Wave Infrared Bands: Evaluation in a Tropical Savannah Environment, International Journal of Applied Earth Observation and Geoinformation, 7, 2, pp. 107-114, (2005)
  • [5] Gan F.P., Wang R.S., Ma A.N., Spectral Identification Tree (Sit) for Mineral Extraction Based on Spectral Characteristics of Minerals, Earth Science Frontiers, 10, 2, pp. 445-454, (2003)
  • [6] Gong P., Pu R.L., Hyperspectral Remote Sensing and Its Applications, (2000)
  • [7] Hewson R.D., Cudahy T.J., Drake-Brockman J., Et al., Mapping Geology Associated with Manganese Mineralisation Using Spectral Sensing Techniques at Woodie Woodie, East Pilbara, Exploration Geophysics, 37, 4, (2006)
  • [8] Hunt G.R., Spectroscopic Properties of Rocks and Minerals, Handbook of Physical Properties of Rocks, 1, (1982)
  • [9] Karnieli A., Kaufman Y.J., Remer L., Et al., AFRI-Aerosol Free Vegetation Index, Remote Sensing of Environment, 77, 1, pp. 10-21, (2001)
  • [10] Kokaly R.F., Clark R.N., Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression, Remote Sensing of Environment, 67, 3, pp. 267-287, (1999)
12