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Research on enhanced extraction method for weak information of remote sensing mineralization alteration zoning: A case study of the Pulang porphyry copper deposit |
| WANG Di1,2, ZHAO Zhifang2, WANG Ruixue1, CHEN Qi2, HE Binxian2, XI Jing2 |
1. Kunming University of Science and Technology, College of Land Resources and Engineering, Kunming 650091, China;
2. Yunnan University, College of Resources and Environment, Kunming 650091, China |
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Abstract To compensate the insufficiency of previous studies shown by the low degree of refined extraction of remote sensing mineralization alteration and the limitation of the supporting role for advancing prospecting and exploration of Southwest Sanjiang deep porphyry copper deposits,the authors took into account the zoning characteristics of the mineralization alteration of the Pulang porphyry copper ore district in the central south segment of the southwest Sanjiang metallogenic belt,i.e., propylitization-sericitization-silicification and potassic alteration from outside to inside of the alteration zone and, on such a basis, analyzed the features of the characteristic minerals,the diagnostic spectra and ASTER remote sensing spectral response of the porphyry copper mineralization and alteration zonation. With the multifractal theory of "generalized self similarity" and "singularity" and the method of energy spectrum area (S-A),the authors probed into the enhancement and extraction methods for the weak information of the characteristic minerals in the porphyry copper alteration mineral zone under the condition of complex geological background,including the propylitization (chlorite and epidote),lithification (quartz and sericite),potassium (potassium feldspar) and silica (quartz). Through the field test of the remote sensing alteration zoning results and mineral identification,the reliability of the methods of enhancement and extraction of the remote sensing abnormal information for the porphyry copper ore alteration zoning was confirmed.
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| Keywords
QuickBird images
principal component transform
shadow detection
morphological filtering
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Issue Date: 02 March 2015
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2015,
Vol. 27
