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Remote sensing mineralization alteration information extraction based on PCA， Multilevel Segment Method， and SVM

  • role： First author第一作者
  • Affiliation：

    School of Earth Science and Resources. Chang'an University, Xi'an 710054, China

    School of Management. Xi'an University of Finance and Economics, Xi'an 710100, China

  • Email：304714562@qq.com
  • Introduction：唐淑兰，1979年生，女，博士研究生，研究方向为遥感影像模式识别、遥感地质应用。E-mail:304714562@qq.com
TANG Shulan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Science and Resources. Chang'an University, Xi'an 710054, China

  • Email：zyjncao@chd.edu.cn
  • Introduction：曹建农，1963年生，男，教授，研究方向为遥感影像模式识别。E-mail：zyjncao@chd.edu.cn
CAO Jiannong1*,  
  • Affiliation：

    Xi'an Center of Geological Survey, CGS, Xi'an 710054, China

WANG Kai3

Resümee

In order to accurately locate the deposit, the ASTER data of Weiya area in eastern Tianshan Mountain of Xinjiang is selected to study the extraction method of mineralization alteration information. To improve the accuracy of ASTER data mineralization alteration information extraction method, a method based on Principal Component Analysis (PCA), multilevel segment method, and Support Vector Machine (SVM) is proposed in this study. First, the special band of alteration information is selected after analyzing the ASTER data, and the principal component image is acquired by PCA. Then, the mean image is obtained after the principal component image is segmented. Subsequently, the training samples are trained by SVM after the training samples are extracted. Moreover, the optimal model is constructed using the optimal kernel parameters and flabby variable obtained by repeated testing. Finally, the optimal model is used to accomplish the extraction of alteration information from ASTER data. The abnormal ferric contamination is extracted using 1, 2, 3, and 4 bands, the alteration anomalies with AL-OH groups are extracted from 1, 4, 6, and 7 bands, and the alteration anomalies with OH, CO32- groups are extracted by 1, 2, 8, and 9 bands. SMO is adopted to improve operation efficiency. Thus, the speed is increased by 12%. A comparison with band ratio method, PCA method, spectral angle mapper and SVM method is conducted. The degree of the abnormal ferric contamination, the alteration anomalies with AL-OH groups, and the alteration anomalies with OH and CO32- groups are 87.98%, 90.01%, and 88.93%, respectively. The corresponding Kappa coefficients are 0.8011, 0.8134, and 0.8023. The extraction results of anomaly information are consistent with metallogenic belt, the known mineralization points, and the mineralization characteristics of different geological conditions.

Schlüsselwort

remote sensing;ASTER;mineralization alteration information extraction;multilevel segment method;Principal Component Analysis (PCA);Support Vector Machine(SVM);Sequential Minimum Optimization(SMO)

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