Remote sensing lithologic classification, especially the automatic identification of mafic- ultramafic rocks and granite, is of great significance for mineral exploration. However, existing methods for lithologic classification that integrate visible light near- infrared (VNIR), short wave infrared (SWIR), and thermal infrared (TIR) images are relatively lacking. In this study, we utilized data from the advanced spaceborne emission and reflection radiometer (ASTER) to conduct lithologic mapping in the Huitongshan area of Beishan, Gansu Province, and Huangshan in the East Tianshan region of Xinjiang Province. By analyzing the reflection spectrum characteristics of rocks in the VNIR- SWIR region and the radiation characteristics in the TIR region, we established the mafic- ultramafic rock index (MI), quartz- bearing rock index (QI), and the comprehensive ratio. These indices, along with the comprehensive ratio results, were used to build a three- dimensional feature space model. The specific lithology was classified and extracted according to the clustering characteristics of the rocks in the feature space. The model was applied to the lithologic classification of Huitongshan in Beishan, Gansu Province, and Huangshan in the east Tianshan region of Xinjiang. Field observations confirmed the high accuracy of the classification results. Our findings demonstrate that the proposed lithologic index and three- dimensional feature space model, using VNIR- SWIR and TIR data, could successfully extract target lithology with high precision and applicability, which has a good application prospect in western China.