Extraction of Alteration Anomaly Information by Feature-Based Principal Component Analysis from ASTER Data

In order to extract the alteration anomaly information with multi-spectral remote sensing data, this paper improves the traditional principal component analysis is improved and the mineral alteration information of Xingcheng region of Liaoning using the method of the feature oriented principal component analysis is extracted, successfully for the alteration anomaly of the limonite (Fe³⁺), chlorite (Mg-OH group minerals) and kaolinite (Al-OH group minerals) based on the analysis of the characteristic curve of canopy spectral in VNIR-SWIR (visible and near IR-short wave-length IR) band of ASTER (advanced spaceborne thermal emission and reflection radiometer) data about alteration mineral.Field practice and geological data of research area indicate that the method above can effectively extract the alteration information and identify the mainly mineral of research area, which can provide a certain basis for the metallogenic prediction.