Abstract: Owing to the growing demands for resources and less exploitable outcrop and shallow ore mines，it is urgent to look for new iron deposits around and below current iron deposits.As traditional prospecting methods are limited in doing this，remotely sensed mineralized alteration information is of great directive significance to geological prospecting work.But extracting alteration information in high vegetation covered areas is one of the difficulties in remotely sensed information extraction.The Fenghuangshan iron deposit is located in Lanling County of Shandong province with dense vegetation coverage,so,it is selected as the study area in this paper.Based on ASTER data,firstly，the ASTER data is geometrically corrected and atmospherically corrected,the water and other interference informations are removed;then,the optimal band combination of mineral alteration information extraction is selected，with references to the spectra of typical altered minerals in the USGS standard spectral database,the principal component analysis (PCA) method is applied to extract the ferric contamination and hydroxyl alteration information，and they are enhanced and classified by thresholding;finally，the anomaly mineralization regions are delineated with references to both known deposits and related alterations，and 8 ferric contamination anomalies and 6 hydroxyl alteration anomalies are selected to validate.Due to high coverage of vegetation and soil and less exposed bedrock，and the agreement between remote sensing anomaly areas and mineralized alteration zones，Quaternary covered areas are investigated especially.The results show that，the extracted ferric contamination and hydroxyl alteration information are verified and the verification accuracy reaches 87.5% and 83.3% respectively，it proves that the ASTER data is of capacity to depict minerals' spectral characteristics in the short wave infrared ranges，which effectively facilitate geological prospecting in dense vegetation covered regions.

Key words: Geological prospecting, ASTER data, Remote sensing, Iron mineralized alteration information, Principal component analysis