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Journal of Applied Remote Sensing

Mineral recognition mapping using measured spectra based on classification and regression tree
Author(s): Yunjun Zhan; Yubin Su; Jiejun Huang; Fawang Ye; Chuan Zhang
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Paper Abstract

The alteration of surrounding rock is an important prospecting indicator in mineral exploration, but some important minerals are unclassified or misclassified when using hyperspectral remote sensing mineral recognition. A method for mineral recognition mapping was proposed. In this method, a decision tree discrimination rule was established based on the classification and regression tree data-mining algorithm and the absorption characteristics of field-measured spectra. Compared with spectral angle mapping and mixture-tuned matched filtering (MTMF), this method is shown to be efficient for mineral recognition mapping using hyperspectral images; its accuracy is 85.06%, which is greater than that of the MTMF method (83.91%). The advantages of the proposed method comprise the reduction of errors caused by the setting of the artificial threshold for mineral mapping and the lesser degree of difficulty in its training. Furthermore, the hierarchy structure of the decision tree in this method reflects the recognition process clearly, and the rule nodes are closely related to the spectra of the minerals; therefore, the advantage of this method is the interpretability of the results and the process. This method could be used for mineral recognition and classification using hyperspectral images.

Paper Details

Date Published: 14 November 2016
PDF: 18 pages
J. Appl. Rem. Sens. 10(4) 045017 doi: 10.1117/1.JRS.10.045017
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Yunjun Zhan, Wuhan Univ. of Technology (China)
Yubin Su, Wuhan Univ. of Technology (China)
Jiejun Huang, Wuhan Univ. of Technology (China)
Fawang Ye, Beijing Research Institute of Uranium Geology (China)
Chuan Zhang, Beijing Research Institute of Uranium Geology (China)


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