To explore the automatic classification method of Quaternary lithology in vegetation covered areas is significantly helpful to improve the efficiency of Quaternary lithology mapping. Due to the vegetation cover and human modification effects, the traditional lithology identification methods based on image spectra and textures are often challenging to be effective. This paper uses multi-source remote sensing data such as OLI, TIRS, and ASTER GDEM to extract multiple types of spectral (SPEC), textural (TEX), topographic (TOPO), geothermal (TEM), and vegetation (VEG) based on principal component transform, gray co-occurrence matrix, topographic factor calculation, thermal radiation transport model and vegetation index in the Quaternary distribution area of Viet Chi, Vietnam. Remote sensing features were selected and combined to form 16 kinds of classification datasets. The lithological units was automatically classified using the random forest method, The method’s accuracy was evaluated to study the effectiveness of multi-type remote sensing features on the automatic classification of Quaternary lithology in vegetation cover area. The results show that the geothermal, textural, and topographic features can effectively improve the lithological classification accuracy, and the overall classification accuracy is improved by 0.32%, 0.87%, and 2.25%, respectively, compared with the use of spectral data alone. Among the 16 classification datasets constructed, the dataset combining spectral, textural, topographic, and geothermal features (SPEC+ TEX+ TOPO+ TEM) obtained the highest automatic lithology classification accuracy of 80.99%. This study can provide a technical idea for rapid differentiation of regional Quaternary surface sediment lithologies.
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