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REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (2) : 112-119     DOI: 10.6046/gtzyyg.2016.02.18
Technology Application |
Application of high resolution remote sensing data to ore-prospecting prediction in East Kunlun metallogenic belt
ZHANG Wei1,3, JIN Moushun2,3, ZHANG Shaopeng2, CHEN Ling1, ZHONG Chang1, DONG Lina1
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Institute of Remote Sensing Applications, Aerial Remote Sensing Bureau of China Coal, Xi'an 710054, China;
3. The BeiDou Aerospace Science and Technology Group, Beijing 100070, China
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Through geological interpretation and alteration anomaly information extraction with remote sensing, rapid pre-selection of large ore concentration areas and delineation of potential mineralization areas can be carried out. In this paper, with the Hongshui River region of the East Kunlun metallogenic belt as a case study, by using WorldView-2 remote sensing images as major data source and adopting methods of image rectification, false color composition, image enhancement, data fusion, alteration anomaly information extraction, field validation, chemical analysis and geological integrated analysis, the authors predicted the potential regions of mineral resources. The results show that the remote sensing technology using high resolution satellite images can be used as an effective method for detection of potential mineral resources enrichment region, which can meet the requirements of mineral resources exploration and assessment in the arid and semi-arid regions in West China and provide the basic data and reference for the deployment of mineral resources exploration.

Keywords multispectral remote sensing      vegetation cover      classification feature      classification methods     
:  TP79  
Issue Date: 14 April 2016
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JIANG Weiwei
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YAN Li,JIANG Weiwei. Application of high resolution remote sensing data to ore-prospecting prediction in East Kunlun metallogenic belt[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 112-119.
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