Abstract: Extracting information about bare land is crucial for territorial planning, environmental protection, and sustainable development. However, current information extraction methods for bare land struggle to balance the extraction efficiency and accuracy in large-scale and multitemporal applications. This study constructed normalized difference indices based on the analysis of the hue-saturation-value (HSV) features. By combining texture features and vegetation index, this study proposed a simple, efficient hierarchical fine-scale information extraction method for bare land. This proposed method was applied to the urban area of Qufu City, Shandong Province, China. First, with three GF-1 satellite images as the data source, the red, green, and blue bands from the images were converted to the HSV color space. Based on the differences in H, S, and V components between bare land and other land types, the normalized difference SH and SV indices were constructed for preliminary hierarchical information extraction of bare land. Second, texture features were introduced to low-rise building areas and bare land, where the differences in H, S, and V components are nonsignificant. Different texture features were comparatively analyzed for further information extraction of bare land. Third, the normalized difference vegetation indices were used to achieve the final information extraction of bare land, followed by post-processing of the results. The results of this study demonstrate that the constructed normalized difference indices, combined with homogeneous texture features, showed the optimal extraction performance, with an overall accuracy of above 93% and a Kappa coefficient of above 0.84, outperforming other classification methods. Therefore, the proposed method proves effective in extracting information about bare land, serving as a novel approach for bare land information extraction.