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REMOTE SENSING FOR LAND & RESOURCES    1990, Vol. 2 Issue (1) : 20-27     DOI: 10.6046/gtzyyg.1990.01.04
Applied Research |
THE USE ENHANCED REMOTE SENSING IMAGES IN THE STUDY OF STRUCTURAL FEATURES AND SEARCH FOR IRON DEPOSITS IN AN AREA OF METAMORPHIC ROCK
Liu Yunliang1, Yang Deming1, Wang Xihua2, Ye Xuezhong2, Li Xianjun2
1. Changchun College of Geology;
2. Capital Steel-Iron Exploration Corp
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Abstract  

This paper with the measuring of spectral property of the geological formations as bases combined with geological and geophysical data and choosing different Processing methods for contrasting and analyses, satisfactory results are obtained. Several kinds of remote sensing images such as ratio and density slicing images are considered to be more useful in reflecting the buried structure of metamorphic formations which ferrous or iron ore bearing. Accrding to color anomalies in false color composite image of ratio and in density slicing image the exposed metamorphic formations which contain iron show dark brown and violet strip, whereas the quaternary overburden show irreqular color strips or dots. The tone indicate the buried depth of metamorphic rocks which contain iron. With the enhanced image, we have found some perspective areas.

Keywords Index analysis      Land use      Driving force      Xi’an city     
Issue Date: 02 August 2011
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HE Yan-Jie
Wei-Hai-Yan
Xue-Liang
Li-Bi-Li
LAI Jiang-de
ZHANG Jin-duo
CA Dong-di
YANG Guang-qin
ZHANG Su-hong
Cite this article:   
HE Yan-Jie,Wei-Hai-Yan,Xue-Liang, et al. THE USE ENHANCED REMOTE SENSING IMAGES IN THE STUDY OF STRUCTURAL FEATURES AND SEARCH FOR IRON DEPOSITS IN AN AREA OF METAMORPHIC ROCK[J]. REMOTE SENSING FOR LAND & RESOURCES, 1990, 2(1): 20-27.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1990.01.04     OR     https://www.gtzyyg.com/EN/Y1990/V2/I1/20


[1] 孙大中主编:冀东早前寒武地质, 禾津科学技术以版社, 1984年

[2] 钱祥麟等:冀东前寒武纪铁矿地质, 一河北科学技术出版社, 1985年

[3] 刘允良等:密云水库北部变质岩构造遥感图像特征的研究, 长春地质李院学报, 19多2年第二期

[4] 刘允良等:应用陆地卫星图像的处理资料研究京北地区地质构造特征, 长春地质学院学报, 1985年第二期

[5] 李铁芳等:遥感图象数字处理原理与应用, 云南科技出版社, 1987年

[6] Fditor-in-chief Rober NColwell,1983,Mauualof.RemoteSensingsecondFdition Vol. p1762-1805

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