Hyperspectral characteristics analysis of ground features is the basis for applications of high-resolution imaging technology to ground target identification and ground features classification. Based on morphological multi-scale Top-Hat transformation, a novel spectral absorption enhancing algorithms was put forward, which enhanced spectral absorption features while maintaining shape features of the absorption peak bands. Eleven reflectance spectra of different mineral groups were chosen from the mineral spectral library of the United States Geological Survey (USGS), and we used a K-means clustering analysis on both the absorption-enhanced spectra and the original reflectance spectra. Results showed that, firstly, clustering groups of the absorption-enhanced spectra (AES) had better similarity within the same clustering group, and greater difference between different groups, furthermore, they were more consistent with the geological background of these minerals compared with clustering result of the original spectra (OS). Secondly, while all the original spectra were re-sampled to their ASTER spectra and the AES clustering result was displayed in the form of ASTER spectra of the minerals, we could easily describe both the representative spectral feature of each clustering group, and the typical spectral differences between every two groups. These fully demonstrate that the absorption-enhanced spectra have enhanced absorption features of the mineral spectra, and improved the separability of hyper-spectra. Accordingly, feature analysis based on absorption enhanced spectra can be used as reference for information extracting based on multi-spectral remote sensing image data, and it is a very useful method of hyperspectral analysis.