Hyper-Spectral Remote Sensing Apply on Alteration Mineral Mapping

Article Preview

Abstract:

With the rapid development of modern science and technology, remote sensing geological survey theory based on what is built on the interaction mechanism the physics of electromagnetic radiation and geological body. It is through the multi-wave spectrum (light), more than reality, multi-imaging, multi-polarization, multi-level enhancement processing technical means to collect and analyze remote sensing data in order to get more spectral, space geological information than alteration mapping. Remote sensing geological survey does not require direct contact with the target, but use of visible light, infrared, microwave detection instrument, through photography or scanning mode, the induction of electromagnetic radiation energy, transmission and processing, thereby identifying the surface target from a long-range, high-altitude and even outer space platforms.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

729-733

Citation:

Online since:

February 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Alexander F.H. Goetz. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment, 2009, 113(1): s5-s16.

DOI: 10.1016/j.rse.2007.12.014

Google Scholar

[2] Bocai Gao, Marcos J. Montes, Curtiss O. Davis, Alexander F.H. Goetz. Atmospheric correction algorithms for hyper-spectral remote sensing data of land and ocean. Remote Sensing of Environment, 2009, 113 (1): s17-s24.

DOI: 10.1016/j.rse.2007.12.015

Google Scholar

[3] Agterberg F P , Bonham-Carter G F, Cheng Q M, et al. 1993. Weights of Evidence Modeling and Weighted Logistic Regression for Mineral Potential Mapping[M]. In: Davis. J. C. and Herzfeld. U.C. (eds. ). Computers in Geology, 25 Years of Progress . London: Oxford University Press.

DOI: 10.1093/oso/9780195085938.003.0007

Google Scholar

[4] Freek D. van der Meer, Harald M.A. van der Werff, Frank J.A. Multi- and hyper-spectral geologic remote sensing. International Journal of Applied Earth Observation and Geo-information, 2012, (14): 112-128.

DOI: 10.1016/j.jag.2011.08.002

Google Scholar

[5] Rajesh H M. 2008. Mapping Proterozoic unconformity-related uranium deposits in the Rockhole area, Northern Territory, Australia using landsat ETM+[J]. Ore Geology Reviews, (33): 382–396.

DOI: 10.1016/j.oregeorev.2007.02.003

Google Scholar

[6] Qihao Weng. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends Original Research Article. Remote Sensing of Environment, 2012, 112(11): 34-49.

DOI: 10.1016/j.rse.2011.02.030

Google Scholar

[7] FabioRoli, Fumera G. 2001. Support Veetor Maehines for Remote Sensing Image Classifieation [J]. Proeeedings of SPIE, 4170: 160-66.

Google Scholar

[8] Alexander F.H. Goetz. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment, 2009, 113Supplement (1): S5-S16.

DOI: 10.1016/j.rse.2007.12.014

Google Scholar

[9] Elisabeth A. Addink, Frieke M.B. Van Coillie, Steven M. De Jong. Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis. International Journal of Applied Earth Observation and Geoinformation, 2012(15): 1-6.

DOI: 10.1016/j.jag.2011.12.001

Google Scholar

[10] Peter D. Hunter, Andrew N. Tyler, Mátyás Présing, Attila W. Kovács, Tom Preston. Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution. Remote Sensing of Environment, 2008, 112(4) : 1527-1544.

DOI: 10.1016/j.rse.2007.08.003

Google Scholar

[11] Collin G. Homer, Cameron L. Aldridge, Debra K. Meyer, Spencer J. Schell. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring. International Journal of Applied Earth Observation and Geoinformation, 2012, 14(1): 233-244.

DOI: 10.1016/j.jag.2011.09.012

Google Scholar

[12] Antonio Plaza, Jon Atli Benediktsson, Joseph W. Boardman, Jason Brazile, Lorenzo Bruzzone, Gustavo Camps-Valls, Jocelyn Chanussot, Mathieu Fauvel, Paolo Gamba, Anthony Gualtieri, Mattia Marconcini, James C. Tilton, Giovanna Trianni. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 2009, 113Supplement (1): S110-S122.

DOI: 10.1016/j.rse.2007.07.028

Google Scholar

[13] Alan R. Wallace. 2004. Steve Ludington, Mark J, Mihalasky, et al. Assessment of Metallic Mineral Resources in the Humboldt River Basin[R]. Northern Nevada: USGS Bulletin.

Google Scholar

[14] Coolbaugh.M. F and Bedell R A. 2006. Simplification of Weights of Evidence Using a Density Function and Fuzzy Distributions: Using Geothermal Systems in Nevada as an Example[C]. Geological Association of Canada Special Paper GIS applications in the Earth Sciences.

Google Scholar