基于光谱指数的遥感影像岩性分类

于亚凤; 杨金中; 陈圣波; 王楠

doi:10.3799/dqkx.2015.127

Volume 40 Issue 8

Aug. 2015

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Article Navigation > Earth Science > 2015 > 40(8): 1415-1419

Yu Yafeng, Yang Jinzhong, Chen Shengbo, Wang Nan, 2015. Lithologic Classification from Remote Sensing Images Based on Spectral Index. Earth Science, 40(8): 1415-1419. doi: 10.3799/dqkx.2015.127

Citation:

Yu Yafeng, Yang Jinzhong, Chen Shengbo, Wang Nan, 2015. Lithologic Classification from Remote Sensing Images Based on Spectral Index. Earth Science, 40(8): 1415-1419. doi: 10.3799/dqkx.2015.127

Yu Yafeng, Yang Jinzhong, Chen Shengbo, Wang Nan, 2015. Lithologic Classification from Remote Sensing Images Based on Spectral Index. Earth Science, 40(8): 1415-1419. doi: 10.3799/dqkx.2015.127

Citation:

Yu Yafeng, Yang Jinzhong, Chen Shengbo, Wang Nan, 2015. Lithologic Classification from Remote Sensing Images Based on Spectral Index. Earth Science, 40(8): 1415-1419. doi: 10.3799/dqkx.2015.127

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Lithologic Classification from Remote Sensing Images Based on Spectral Index

doi: 10.3799/dqkx.2015.127

Yu Yafeng^{1, 2
,},
Yang Jinzhong²,
Chen Shengbo^{1
,
,},
Wang Nan¹

1.
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
2.
China Aero Geophsical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China

Publish Date: 2015-08-01

Abstract

Abstract

In order to solve the problem of interference factors of the rock radiated in traditional lithologic classification with the phenomenon of the different spectra for the same substance and the same spectrum for different substances, and the low lithologic classification accuracy, A study based on the in-depth analysis of spectrum characteristics of mineral rock, taking monzonitic granite, quartz-syenite and syenite of West Kunlun metallogentic belt as the research object is presented in the paper. We set up two spectral index RI and SI on the basis of measured spectral data of three kinds of lithology and the characteristics of ASTER imaging data. Two spectral index methods are used to classify ASTER remote sensing data, and the results show that the accuracy in monzonitic granite extraction with RI and SI spectral index method is above 70%, about 80% in quartz-syenite extraction. The precision of the two kinds of lithology has been significantly increased compared with the classification results of maximum likelihood method.
- spectral index method,
- ASTER,
- West Kunlun metallogenic belt,
- maximum likelihood method,
- remote sensing

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References(15)

References

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Lithologic Classification from Remote Sensing Images Based on Spectral Index

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